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The human immune system functions to provide continuous body-wide surveillance to detect and eliminate foreign agents such as bacteria and viruses as well as the body's own cells that undergo malignant transformation . To counteract this surveillance , tumor cells evolve mechanisms to evade elimination by the immune system; this tumor immunoescape leads to continuous tumor expansion , albeit potentially with a different composition of the tumor cell population ( “immunoediting” ) . Tumor immunoescape and immunoediting are products of an evolutionary process and are hence driven by mutation and selection . Higher mutation rates allow cells to more rapidly acquire new phenotypes that help evade the immune system , but also harbor the risk of an inability to maintain essential genome structure and functions , thereby leading to an error catastrophe . In this paper , we designed a novel mathematical framework , based upon the quasispecies model , to study the effects of tumor immunoediting and the evolution of ( epi ) genetic instability on the abundance of tumor and immune system cells . We found that there exists an optimum number of tumor variants and an optimum magnitude of mutation rates that maximize tumor progression despite an active immune response . Our findings provide insights into the dynamics of tumorigenesis during immune system attacks and help guide the choice of treatment strategies that best inhibit diverse tumor cell populations .
In 1909 , Paul Ehrlich was the first to propose the idea that the immune system scans for and eliminates nascent transformed cells in the human body [1] . This hypothesis received much interest from both immunologists and cancer researchers and led to experiments with tumors transplanted into mice; these studies suggested the existence of tumor-associated antigens and formed the basis of the idea of immune surveillance [2] . Since these landmark studies in the 1950s , the model of cancer immune surveillance has gained widespread acceptance , and the central role of immune effector cells , such as B , T , and natural killer ( NK ) cells , have been elucidated [3] , [4] , [5] , [6] , [7] . NK cells and CD8 T cells were found to recognize and kill tumor cells through the interaction of specific cell surface receptors with tumor cell ligands [3] , [8] , [9] , [10] , [11] , [12] . Similarly , CD4 and CD8 T cells recognize MHC class II and class I molecules on tumor cells , respectively , and B cells produce antibodies against tumor antigens [3] , [6] , [13] . When the immune system fails to eliminate all tumor cells , then the malignant cell population continues to grow – a phenomenon termed “tumor immunoescape” . The interaction with the immune system , however , may significantly decimate the abundance of tumor cells and select for those phenotypes with relative resistance against immune system attacks . The “cancer immunoediting” hypothesis then predicts that , while one outcome is complete eradication of a developing tumor , another is the generation of a sculpted tumor cell population that either displays reduced immunogenicity [4] or an increased ability to inhibit anti-tumor immune responses [6] , [14] , [15] , [16] . The latter capacity may be imparted via diverse mechanisms [17] , [18]: ( i ) tumor cells can lose their MHC class I molecules , enabling them to evade CTL attacks [19]; ( ii ) while the immunodominant epitope becomes the main target of immune responses , cells with other phenotypes may continue proliferating in the “shadow” of the dominant clone [20]; ( iii ) furthermore , tumor cell secretion of immunosuppressive cytokines such as TGF- and IL-10 can reduce the efficiency of the immune response [21] , and ( iv ) a modification of death signaling may prevent cells from undergoing apoptosis [22] . Tumor immunoescape is driven by the generation of tumor cell variants [17] , [23] . Frequent genetic and epigenetic alterations enable tumor cells to lose MHC class I molecules , produce immunosuppressive cytokines , and generate other phenotypes that are selected to escape immunosurveillance . Although cells with normal rates of accumulating such alterations may also manage to evade the immune response , this process is accelerated by the evolution of genomic instabilities [24] , [25] . Genomic instabilities are common in most cancer types [26] , and two main categories have been identified: in the majority of tumors , chromosomal instability ( CIN ) leads to an increased rate of losing or gaining whole chromosomes or parts of chromosomes during cell division [24]; in a smaller fraction of cancers , a mismatch-repair deficiency leads to microsatellite instability ( MIN ) at the nucleotide level [27] . Similar to genomic instabilities , epigenetic instabilities were also recently found to contribute to tumorigenesis by modulating the production of oncogenic proteins [28] . An increased chance of accumulating ( epi ) genetic alterations during cell divisions enhances the rate of generating tumor cell variants that may evade the immune response; however , high rates of alterations may in turn lead to an error catastrophe in that a functioning genome cannot be sustained when error-prone replication produces excess damage [29] . The concept of an error catastrophe was first introduced to describe the behavior of RNA viruses [30] , and numerous observations about the extinction of such viruses due to excess error have been reported [31] , [32] , [33] . These findings imply that a mutator phenotype does not serve as an unequivocal benefit for tumor cells , but also harbors a risk of extinction if the extent of variability in the population crosses a threshold . A delicate balance between the cost of a potential error catastrophe and the benefits of outracing the immune response enables tumor cells to survive and expand despite immune system attacks . Several mathematical models have been designed to provide insights into the dynamics of tumorigenesis under immunosurveillance or an error catastrophe of tumor cells [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] . Most studies of the effects of immunosurveillance on tumor evolution considered a homogeneous population of tumor cells and concentrated on phenomena such as tumor dormancy and immunoescape . Studies of the tumor error catastrophe , in contrast , investigated quasispecies models in simplified situations without an immune response . The dynamics of tumor immunoescape and error thresholds , however , result from an interaction between both components; such studies are still lacking from the literature . In this paper , we investigate an integrated model of both concepts during tumor progression - the effects of tumor immunosurveillance and the consequences of a mutator phenotype of tumor cells . We introduce specific immune responses to a formulation of the quasispecies model and study the balance between evasion of immunosurveillance and prevention of an error catastrophe . This study reveals the effects of various tumor antigens on specific immune responses from the viewpoint of evolutionary dynamics , and provides new perspectives on optimum treatment strategies of tumors subjected to immunosurveillance and -editing .
With these considerations , we define the basic mathematical model including tumor variants and their specific immune responses by ( 1 ) Baseline values of model parameters and their respective ranges used for simulations are presented in Table 1 . A subset of these parameter values were estimated in [39] , [45] , [46] . Since the original tumor cell clone has been suggested to proliferate faster than variant tumor cells [37] , [47] , the division rate of variant tumor cells is where ; we assume a default value of but also perform sensitivity analyses ( see a later section ) . We found that , although some of these values are rough estimates and might deviate when measured by other groups or in other systems , our main results are qualitatively preserved within broad ranges around our baseline values . Let us now discuss the possible outcomes of interactions between the immune system and the tumor cell population: there may be tumor dormancy , partial immunoescape , complete immunoescape , and the event of an error catastrophe . In the dormancy state , immunosurveillance serves to effectively suppress the tumor cell population . In the partial immunoescape state , some tumor variants ( but not all ) achieve immunoescape while in the complete immunoescape state , the immune response is completely unsuccessful . Finally , in the error catastrophe state , the original tumor clone , which has the highest division rate , goes extinct due to the accumulation of excess alterations . We now outline how the original tumor clone , the tumor cell variants , and the specific immune system cells behave during the accumulation of alterations and the evolution of higher mutation rates . The four qualitative outcomes of the interaction between tumor cells and the immune system – dormancy , partial and complete immunoescape as well as error catastrophe – are most significantly influenced by two systems parameters: the mutation rate generating tumor variants ( ) and the maximum number of tumor variant types that can emerge ( ) . We therefore investigated the dynamics of tumor evolution in dependence of these parameters , and identified three analytical thresholds ( , , and ) separating the potential outcomes ( Figure 2 ) . The formulas and detailed mathematical analyses of these thresholds are provided in the Methods section . As long as the number of tumor variants is less than the first threshold , , immune responses suppress all tumor variants ( tumor dormancy ) . When the number of variants exceeds this threshold , however , then some tumor cells escape from the specific immune response ( partial immunoescape ) . Once the number of variants passes the second threshold , , all tumor cells escape from immune responses ( complete immunoescape ) . This finding implies that tumor cells can evade immune surveillance by accumulating a sufficiently large extent of intratumor heterogeneity . However , if the number of variants exceeds the third threshold , , then an error catastrophe of tumor cells occurs , in which the original tumor clone can no longer maintain an expanding population and the original tumor cells therefore go extinct . We also found that , as the mutation increases , the threshold increases while and decrease . In all scenarios , however , tumor eradication is unlikely – although the tumor cell burden may shrink by a large amount – when the growth rate of the original tumor clone is negligibly small as compared to their death rate by apoptosis and/or interactions with the immune system . We have thus established that although high rates of accumulating alterations allow tumor cells to reach a state of complete immunoescape , those cells with an excessively high mutation rate suffer an error catastrophe as the number of tumor variant types increases . These systems dynamics suggest that there is an optimum amount of instability that optimizes tumor evolution ( i . e . maximizes the number of tumor cells ) while maintaining a functioning genome . Let us now investigate the system dynamics for varying mutation rates and identify those regimes in which the total tumor cell number is maximized . Every time a new tumor variant arises , the dynamics of tumor evolution rapidly converges to its steady state; we therefore analyze the dynamics in steady state . The total number of tumor cells depends on the number of variants as well as the mutation rate , and an optimum combination of these parameter values exists that maximizes the total tumor cell number . In Figure 3 , we demonstrate how the total number of tumor cells is affected by the number of tumor variant types for three different cases in which the mutation rate is , , and , respectively . Detailed mathematical analyses of those equilibria are provided in the Methods section . In situations in which all tumor cells are effectively suppressed by the immune response ( tumor dormancy ) , the total number of tumor cells increases with the number of variant types . In situations in which some tumor cell types manage to escape from immune surveillance , the total number of tumor cells increases as both the number of variant types and the mutation rate increase ( Figure 3 ) . However , in situations in which all tumor cell types completely escape from their specific immune responses , there exist two thresholds regarding the total number of tumor cells: and ( see Figure 2 ) . In this scenario , the total number of tumor cells increases until the number of variant types and the mutation rate , respectively , exceed the values of and ; once crossing these thresholds , the tumor cell number decreases as and further increase . Therefore , tumor cells with an excessively high mutation rate cannot continue to become more abundant as the number of variant types increases ( Figure 3 ) , but there is an optimum , non-trivial parameter regime that maximizes the number of tumor cells . Our results demonstrate that there are two strategies to maximize the rate of tumor evolution so that the total tumor cell mass is maximally large: one is to maintain a low mutation rate , since then the tumor cell population can increase the number of variant types along the threshold ( see Figure 2 ) ; another is to keep the number of variant types relatively small , since then the tumor cell population can increase the mutation rate along the threshold ( see Figure 2 ) . When both the mutation rate and the number of variant types are large , then the tumor cell population cannot maintain its maximum number without decreasing one of the two parameters . Let us now investigate how the division rate of variant tumor cells affects the evolution of tumor cells during their interaction with immune system cells . Recall that in the basic model , the division rate is , and that the threshold for an error catastrophe to occur ( ) is independent of the division rate . To investigate the dependence of the system behavior on this division rate , we chose four different and performed a sensitivity analysis for the thresholds and . Figure 4 displays how the division rate of variant tumor cells influences the outcome of tumor immunoescape in the plane of mutation rates ( ) and the number of tumor variants ( ) . The four panels of the figure represent cases with different values of . The higher the fitness of variant tumor cells becomes , the more easily they escape from immunosurveillance . However , the qualitative profiles of the system dynamics are preserved; that is , tumor cells with high mutation rates tend to reach a complete immunoescape while tumor cells with low mutation rates effectively produce a diverse population and thus increase in number . Let us next consider additional effects arising during tumor progression such as competition among tumor cells of different variant types , the presence of an innate immune response such as NK cells , which non-specifically target all tumor variants , and differential growth rates among tumor cell variants . In order to investigate the conditions for outcomes such as tumor immunoescape and error catastrophe in these more complex scenarios , we established an extended model , given by ( 2 ) where . The parameter represents the division rate of tumor variant . We now assume that each tumor cell competes with all other tumor cells so that the death term becomes . Furthermore , the variable describes innate immune responses , for instance by NK cells which attack tumor cells without antigen specificity . The parameters , , , and represent , respectively , the maximum proliferation rate of NK cells , the maximum elimination rate of tumor cells by NK cells , the decay rate of NK cells , and a constant source of NK cells . The dynamics of tumor progression considering these situations are shown in Figure 5 . We investigated how inter-variant tumor cell competition ( Figure 5A ) , incorporation of an innate immune response ( Figure 5B ) , growth rates which differ between individual tumor variants ( Figure 5C ) , and all three effects simultaneously modulate the thresholds between outcomes as well as the optimum parameter regimes for maximizing tumor cell numbers . Competition among tumor cells of the same variant type renders it difficult for the tumor cell population to completely escape from immune surveillance and to increase the total cell number beyond a relatively small value , irrespective of the mutation rate ( Figure 5A ) . However , when only an innate immune response is present without inter-variant competition , then there is a larger parameter regime in which complete immunoescape is possible . Furthermore , the total number of tumor cells is larger in this situation as compared to the above case ( Figure 5B ) . Similar to this scenario , the presence of different growth rates for individual variant clones allows for the existence of a large number of tumor cells as well as a large regime in which complete immune escape can be achieved ( Figure 5C ) . Finally , when all three aspects are combined in the mathematical model , then the region of complete immune escape becomes very small; this effect is mainly driven by the incorporation of interal competition . The total number of tumor cells also remains below a rather small threshold for this case ( Figure 5D ) . Finally , let us discuss the effects of different treatment modalities on the rates of cancer progression and the chance of immunoescape . Since the behavior of tumor cells and thus patient outcomes are to a considerable extent driven by the interactions between tumor and immune system cells , we considered both traditional chemotherapy and treatment options that stimulate the immune system to launch or sustain an attack against the tumor cell population . In general , immune therapies have not been proven to be very effective against many tumor types; one of the few exceptions is represented by adoptive cell therapy , which is used in the treatment of metastatic melanoma and causes regressions in about 50 of patients [48] . Recently , however , synergistic effects of immunotherapy in combination with chemotherapy have been reported in both human and animal trials [49] , [50] , [51] , and several mechanisms were identified that may explain these synergistic effects [52] . To study the effects of chemotherapy , immune therapy , and combination therapy on the dynamics of tumor evolution , we introduced a series of different treatment types into the mathematical framework and identified optimal treatment strategies for diverse tumor cell populations ( Figure 6 ) . These different treatment modalities were tested in situations in which tumor cells had previously achieved complete immunoescape and consisted of a large number of tumor cells . The number of tumor variants and the mutation rate were considered to be and at the time of treatment initiation . Chemotherapy then reduces the number of tumor variants and kills tumor cells proportional to the tumor cell number present . We also considered the case in which chemotherapy reduces the growth rate of tumor cells . Then the model after treatment initiation is given by ( 3 ) Here chemotherapy reduces the number of tumor variants to and either kills the tumor cells at rate or reduces the growth rates to and . Immunotherapy increases the number of specific immune cells ( for ) during treatment . We then utilized this system to investigate optimum treatment strategies . First , let us consider the effects of chemotherapeutic agents which reduce the number of tumor cells by inducing cell deaths at a rate proportional to the cell number present within the tumor . Administration of such treatments decreases the total cell number , but may not be capable of leading to complete eradication of all tumor cells ( Figure 6A ) unless its effects are sufficiently ( and maybe unrealistically ) strong ( Figure 6B ) . Second , consider chemotherapeutic drugs which reduce the number of tumor variant types as well as the growth rates of tumor cells . Again , administration of such treatments decreases the total cell number but is incapable of achieving complete eradication of tumor cells ( Figure 6C ) . Third , consider the administration of immunotherapy which increases the population of tumor-specific immune system cells . Such therapy alone is not able to decrease the abundance of tumor cells by a large extent ( Figure 6D ) . However , when combining chemotherapy and immunotherapy , an effective decrease of the tumor cell population can be achieved , which may ultimately lead to tumor eradication and a cure ( Figure 6E ) . Notably , in situations in which the mutation rate is small , the administration of combination therapy is more successful in eradicating all tumor cells as compared to situations in which the mutation rate is high ( Figure 6F ) . In conclusion , our mathematical model predicts successful outcomes of combination therapy when ( i ) chemotherapy is administered which induces tumor cell death at a significantly large rate , or ( ii ) combination therapy is administered which reduces the number of tumor variants , induces tumor cell death , and replenishes immune cell populations . When the mutation rate of tumor cells is small , combination therapy is more effective than when variations arise at a large rate . An explanation of these findings can be found in Figure 2 – activation of the immune response alone does not change the state of the tumor cell population once it has reached complete immunoescape; in that case , the number of tumor cells does not decrease ( Figure 6D ) . A reduction of the number of tumor variant types and tumor cells by administering chemotherapy alone allows for partial immunoescape or dormancy states , but there is an insufficient abundance of immune system cells to effectively control the tumor cell population ( Figure 6A ) . However , combination therapy which enables immune cells to be activated in the states of partial immunoescape or dormancy is capable of eradicating the tumor ( Figure 6E ) . Thus , our mathematical framework is capable of identifying those treatment modalities that have the potential to lead to a cure of the tumor .
In this paper , we have investigated the dynamics of tumor progression under immune system surveillance while considering the effects of increasing rates at which ( epi ) genetic alterations are generated . We defined specific situations that can arise due to the interactions of immune system cells and tumor cells . When the tumor cell population is able to persist under immunosurveillance without leading to tumor growth , then a state of tumor dormancy ensues . Should the immune system not be capable of efficiently suppressing the tumor cell population , then partial or complete immunoescape is possible , depending on whether some or all tumor clones evade immune system inhibition . Finally , an error catastrophe occurs when the tumor cells evolve mutation rates that are incompatible with the maintenance of a functioning genome due to excess error . The dynamics of the system and likelihood of these different states depend on the rate at which variability emerges in the population ( denoted by the mutation rate per cell division ) as well as the number of distinct tumor clones ( given by ) that are distinguished by their capabilities of generating a specific immune response ( see Figure 2 ) . If both quantities are excessively large , then an error catastrophe occurs and the original tumor cell population goes extinct . In intermediate regimes , states of dormancy and partial or complete immunoescape are possible . We also investigated the extent to which the total number of tumor cells depends on these parameters and identified regimes in which the maximum number of tumor cells is attained . Moreover , we relaxed the model assumptions to consider more complex scenarios such as growth competition among tumor variants , innate immune responses that non-specifically recognize and kill tumor cells , and different growth rates of tumor variants . These studies revealed that the patterns of states do not vary significantly as the assumptions of competition , growth , and innate immune responses are altered; however , internal competition among tumor variants renders it difficult for tumor cells to achieve complete immune escape . Finally , we investigated the effects of different treatment modalities on the rates of tumor progression and found that administration of both chemotherapy and immunotherapy leads to optimum response rates , thereby confirming recent experimental findings [49] , [50] , [51] . These investigations have direct implications for the clinical management of cancers since they incorporate both mutator phenotype and the interactions between tumor cells and the immune system . A consideration of these factors is essential for an understanding of the dynamics of tumor cell populations evolving during immune system attacks . Our results thus suggest that combination therapy incorporating both chemotherapeutic and immunostimulatory agents would lead to best results in the clinic . Our mathematical framework represents only one possibility of modeling the system of tumor and immune system cells . This modeling choice was made for reasons of mathematical simplicity as well as availability of parameter estimates; however , several model extensions are conceivable . For instance , the spatial components of the system could be incorporated such that the spatio-temporal aspects can be considered . Also , we have neglected stochasticity in our formulation of the mathematical model since both population sizes and mutation rates are large , and therefore deterministic dynamics dominate . However , for more detailed investigations – such as a determination of the probability that a certain phenotype arises – the stochasticity of the system cannot be neglected . Such studies will be the topic of future contributions . Furthermore , interactions with the microenvironment such as with stromal cells and other factors could be considered . The incorporation of these extensions are complicated by the fact that few quantitative estimates are available . The determination of system parameters necessary for including such factors into a mathematical framework is an important goal of the field .
Let us first consider the basic model , equation ( 1 ) , in detail . The basic model can be considered qualitatively as a 4-dimensional ODE system ( although equation ( 1 ) is a -dimensional ODE system ) when analyzing the equilibria , since all parameters in the equations describing and are the same , and therefore and have the same properties at the equilibria . Hence we can consider that with respect to the equilibria , where is a model parameter . We investigated the existence conditions of the equilibria of model ( 1 ) . The model has seven possible equilibria:and is the positive root of the following equation: While the equilibria and always exist , only exists if . Furthermore , exists if , and exists if , and because . The existence condition of is . In addition , we calculate the existence conditions of as follows . Note that and are equivalent to and , respectively . If , then we have the following relations:because , always has real roots and ( ) . Here we assume that : otherwise never exists and becomes stable . Therefore , is equivalent to . Note that the roots of are and , which implies that . Furthermore , when , is equivalent to . Since in the context of ( which is a suitable assumption ) , we can roughly estimate that if . Consequently , if , , and , then exists . Consider the situation in which exists , all tumor cells are suppressed by their specific immune responses , and the number of tumor variants is ( i . e . , ) . Let us investigate the transversal eigenvalue in the direction at . Here we defineSince , the transversal eigenvalue is evaluated as follows:This result implies that , if , then disappears from and approaches near . Hence only the original tumor cells are suppressed by their specific immune responses while tumor variants escape from immune surveillance once the number of tumor variant exceeds . Next , we investigate the transversal eigenvalue in the direction at . Here we define to satisfyThe transversal eigenvalue is evaluated as follows:On the other hand , we have the following relations: This result implies that is equivalent to near ; that is , if , then disappears from and converges to near ( is stable ) . Thus , the immune response against the original tumor clone also becomes inactivated if the number of tumor variants exceeds . Note that we consider a restricted region of and , where . Consequently , all tumor cells escape from their immune responses once the number of tumor variants exceeds . Furthermore , the original tumor cell clone is no longer sustainable ( i . e . , an error catastrophe occurs ) as soon as the number of the variants exceeds . Let us now calculate the total number of tumor cells at equilibrium . Note that the dynamics of the basic model , equation ( 1 ) , might not converge to an equilibrium , but may oscillate if . When the number of tumor variants is , then the total number of tumor cells at is . Since we evaluate and , the total number of tumor cells increases as the number of variants grows . When the number of variants is , then the total number of tumor cells at is . Again , as we evaluate and , the total number increases as the number of variants and the mutation rate increase . When the number of variants is , then the total number of tumor cells at is . Here we find critical thresholds and such as and . Therefore , the total number of tumor cells increases as long as the number of variants and the mutation rate are and , respectively . However , once the number of variants and the mutation rate exceed and , respectively , the total number of tumor cells decreases . Eventually , when the number of variants is , the total number of tumor cells at is . | Immunologic surveillance is a function of the immune system which serves to constantly monitor the body for microorganisms , foreign tissue , and cancer cells . To evade this surveillance and subsequent elimination , cancer cells evolve strategies to prevent being recognized and killed by immune system cells; one mechanism is to increase the rate at which genetic and/or epigenetic variability is generated . The benefits of an increased variability of cancer cells to counteract immune surveillance , however , stands in contrast to the costs associated with such heightened mutation rates: the risk of an inability to maintain essential genome structure and functions . To study such situations arising in tumorigenesis , we designed a novel mathematical framework of tumor immunosurveillance and the evolution of mutation rates . We then utilized this framework to study how increased mutation rates and immunologic surveillance affect the abundance of tumor and immune system cells . We found that there exists an optimum number of tumor variants and an optimum magnitude of mutation rates that maximize tumor progression despite the presence of actively proliferating and functioning immune system cells . Our study contributes to an understanding of cancer development during immune system attacks and also suggests treatment strategies for heterogeneous tumor cell populations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"oncology",
"medicine",
"mathematics",
"applied",
"mathematics"
] | 2012 | A Race between Tumor Immunoescape and Genome Maintenance Selects for Optimum Levels of (epi)genetic Instability |
Changes in gene regulatory circuits often give rise to phenotypic differences among closely related organisms . In bacteria , these changes can result from alterations in the ancestral genome and/or be brought about by genes acquired by horizontal transfer . Here , we identify an allele of the ancestral transcription factor PmrA that requires the horizontally acquired pmrD gene product to promote gene expression . We determined that a single amino acid difference between the PmrA proteins from the human adapted Salmonella enterica serovar Paratyphi B and the broad host range S . enterica serovar Typhimurium rendered transcription of PmrA-activated genes dependent on the PmrD protein in the former but not the latter serovar . Bacteria harboring the serovar Typhimurium allele exhibited polymyxin B resistance under PmrA- or under PmrA- and PmrD-inducing conditions . By contrast , isogenic strains with the serovar Paratyphi B allele displayed PmrA-regulated polymyxin B resistance only when experiencing activating conditions for both PmrA and PmrD . We establish that the two PmrA orthologs display quantitative differences in several biochemical properties . Strains harboring the serovar Paratyphi B allele showed enhanced biofilm formation , a property that might promote serovar Paratyphi B's chronic infection of the gallbladder . Our findings illustrate how subtle differences in ancestral genes can impact the ability of horizontally acquired genes to confer new properties .
The phenotypic properties that distinguish closely related bacterial species are often ascribed to differences in gene content [1] , [2] . These differences typically result from the acquisition of genetic material by horizontal gene transfer , a process that can readily transform a bacterial species [2] , [3] . For instance , acquisition of the cholera toxin phage by Vibrio cholerae [4] or of the pathogenicity island LEE – for locus of enterocyte effacement – by enteropathogenic Escherichia coli ( EPEC ) [5] conferred virulence properties upon these bacteria . Indeed , these properties can be reconstructed in laboratory strains of E . coli by experimental introduction of the relevant DNA [6] , [7] . Likewise , the recovery of the same antibiotic resistance genes in unrelated bacterial species [8] indicates that horizontally acquired genes are capable of conferring new properties to organisms with significantly different genomes . However , this situation might be different if a horizontally acquired gene product targets ancestral proteins because allelic differences among ancestral orthologs might impact the ability of a horizontally acquired gene to function . Here , we address this issue by examining the molecular basis for the distinct abilities of Salmonella serovars to display resistance to the antibiotic polymyxin B under different environmental conditions . Inducible resistance to polymxyin B in S . enterica serovar Typhimurium is controlled by the ancestral PmrA/PmrB two-component system , the major regulator of lipopolysaccharide ( LPS ) modification genes [9] . This system is directly activated by extracytoplasmic Fe3+ or Al3+ [10] or by low pH [11] that is detected by the sensor PmrB , which then promotes the phosphorylated state of the DNA binding protein PmrA ( PmrA-P ) [10] , [12] , resulting in expression of PmrA-activated genes ( Figure 1 ) [13] . Low Mg2+ indirectly activates the PmrA/PmrB system in a process that requires the horizontally acquired pmrD gene [14] , [15] ( Figure 1 ) . This is because low Mg2+ is an inducing signal for the PhoP/PhoQ two-component system [16] , which governs pmrD transcription [14] . The PmrD protein protects PmrA-P from dephosphorylation by PmrB , thereby enhancing PmrA-P levels and promoting PmrA-dependent gene transcription [17] . Thus , S . typhimurium displays polymxyin B resistance when experiencing low Mg2+ and/or the presence of Fe3+ . We previously reported that natural isolates of S . enterica vary in the degree to which the horizontally acquired pmrD gene activates the PmrA/PmrB system [18] . This raised the possibility of genetic changes in the genome sequences common to the various S . enterica serovars accounting for the observed phenotypic diversity in polymyxin B resistance [19] . We now report that the human-adapted S . enterica serovar Paratyphi B does not activate the PmrA/PmrB system in response to low Mg2+ and that activation of PmrA/PmrB in response to Fe3+ requires the horizontally acquired pmrD gene product . We establish that this disparity from S . typhimurium is due to a single amino acid difference between the PmrA proteins , which dramatically alters PmrA's affinity for its target promoters and the levels of PmrA-P in vivo . The Paratyphi B PmrA allele confers enhanced biofilm formation , which may aid survival of this human-adapted serovar in its particular habitat . Our work provides a singular example whereby quantitative differences in the biochemical properties of an ancestral transcription factor dictate the ability of a horizontally acquired gene product to confer new traits .
SARA46 is an S . enterica isolate belonging to the Paratyphi B serovar and is classified as a member of the systemic pathovar ( SPV ) that causes paratyphoid fever in humans [20] , [21] . This isolate could not grow on N-minimal media agarose plates containing polymyxin B and low Mg2+ ( Figure 2A ) but grew when Fe3+ was present ( Figure 2A ) . This is in contrast to S . typhimurium , which grew on both media ( Figure 2A ) . We determined that this behavior reflects expression of the PmrA-activated pbgP operon , which is required for polymyxin B resistance [22]–[24] ( note that pbgP is often referred to as pmrHFIJKLM [23] or arn [25] ) . SARA46 failed to transcribe pbgP when grown in low Mg2+ but could do so in the presence of Fe3+ whereas S . typhimurium expressed pbgP under both conditions ( Figure 2B ) . The behavior of SARA46 is exhibited by other S . paratyphi B ( SPV ) isolates ( Figure 2B ) . This behavior cannot be ascribed to these isolates being human-adapted or part of the serovar Paratyphi B because the human-adapted serovar Typhi as well as S . paratyphi B strains belonging to the enteric pathovar ( EPV ) , which cause local enteric infections [20] , transcribed pbgP in low Mg2+ regardless of the presence/absence of Fe3+ ( Figure 2B ) , like S . typhimurium . None of the investigated strains transcribed pbgP during growth in high Mg2+ , which is a non-inducing condition for the PmrA/PmrB system ( Figure 2B ) . The inability of S . paratyphi B isolates to transcribe pbgP in low Mg2+ resembles the behavior of an S . typhimurium pmrD null mutant [14] . This raised the possibility of S . paratyphi B ( SPV ) isolates harboring mutations in pmrD , like other natural Salmonella isolates [18] . However , DNA sequence analysis revealed that S . paratyphi B and S . typhimurium specify identical PmrD proteins . Moreover , pmrD transcription in S . paratyphi B was stimulated in low Mg2+ ( Figure 2C ) as in S . typhimurium [14] . Then , why do serovars Paratyphi B and Typhimurium differ in the expression of PmrA-dependent genes when experiencing low Mg2+ even though they specify identical PmrD proteins that are expressed under like conditions ? The results described above indicate that the inability of S . paratyphi B to transcribe the pbgP gene in low Mg2+ is due to a difference from S . typhimurium in a gene ( s ) other than pmrD . Because PmrA-P constitutes the only known target of the PmrD protein [17] , we explored whether the S . paratyphi B PmrA protein differs from the S . typhimurium homolog . Thus , we sequenced the pmrA gene from 32 natural isolates originating from the Salmonella reference collections A [21] , B [26] and C [27] . An alignment of their deduced amino acid sequences demonstrated that the PmrA protein from S . paratyphi B ( SPV ) strains has a glutamate residue at position 211 ( PmrA E211 ) whereas most other analyzed S . enterica isolates , including S . typhimurium and S . paratyphi B ( EPV ) strains , bear a glycine residue at that position ( PmrA G211 ) ( Figure S1A ) . S . typhi s3333 , with an arginine residue at position 211 ( Figure S1A ) , constitutes a third allele of PmrA identified in Salmonella . These data suggested that the presence of a glutamate at position 211 of PmrA prevents expression of pbgP in low Mg2+ whereas isolates with glycine or arginine at that position are competent for pbgP transcription under these conditions ( Figure 2B ) . If a difference in PmrA is solely responsible for S . paratyphi B's inability to transcribe pbgP in low Mg2+ , then replacing its pmrA ( E211 ) by the pmrA ( G211 ) allele should restore expression . To test this notion , we engineered isogenic S . paratyphi B SARA46 strains bearing a pbgP-lac transcriptional fusion and either the pmrA ( G211 ) or pmrA ( E211 ) alleles under the control of the S . paratyphi B pmrCAB promoter at its normal chromosomal location . When grown in low Mg2+ , the S . paratyphi B strain ( pmrA G211 ) produced 10 times more β-galactosidase activity than the isogenic pmrA ( E211 ) strain ( Figure 3A ) . As expected , deletion of pmrD eliminated pbgP expression in both S . paratyphi B strains when grown in media containing low Mg2+ ( Figure 3A ) , as described in S . typhimurium [14]; and no β-galactosidase activity was detected in a pmrA mutant under any growth conditions ( Figure 3A ) . Deleting the pmrD gene prevented S . paratyphi B ( pmrA E211 ) from expressing pbgP during growth in low Mg2+ + high Fe3+ ( Figure 3A ) . This was surprising because Fe3+ is detected directly by the PmrB sensor [10] , which activates the PmrA protein in a process that does not require PmrD in S . typhimurium [14] . By contrast , S . paratyphi B ( pmrA G211 ) supported pbgP transcription in a pmrD mutant incubated in low Mg2+ + high Fe3+ ( Figure 3A ) . That a single amino acid difference between the PmrA orthologs can have such dramatic effects was reinforced by the phenotypes displayed by S . typhimurium strains with either one of the two pmrA alleles ( Figure 3B ) , as they recapitulated the behavior of the S . paratyphi B strains ( Figure 3A ) . We then analyzed the ability of isogenic S . paratyphi B and S . typhimurium strains harboring the pmrA ( G211 ) or pmrA ( E211 ) alleles to survive killing by polymyxin B when grown on N-minimal media agarose plates containing low Mg2+ + high Fe3+ . All four strains survived killing by polymyxin B ( Figure 3C and 3D ) , which was expected given that they all transcribe pbgP under this condition ( Figure 3A and 3B ) . Resistance to polymyxin B requires pmrD if the strains harbor the pmrA ( E211 ) allele but not if they carry the pmrA ( G211 ) allele ( Figure 3C and 3D ) ; this is consistent with our finding that the former strains do not transcribe the PmrA-activated genes responsible for this resistance , unlike the latter bacteria ( Figure 3A and 3B ) . Similar results were obtained when the minimal inhibitory concentrations of polymyxin B were determined for S . typhimurium strains grown in low Mg2+ + high Fe3+ ( Table S1 ) . Collectively , our data indicate that the pmrA allele present in S . paratyphi B requires PmrD to promote transcription of genes mediating polymyxin B resistance in response to low Mg2+ + high Fe3+ . Why does the single amino acid difference between the PmrA proteins from S . paratyphi B and S . typhimurium abolish low Mg2+-dependent expression of PmrA-activated genes in the former but not the latter serovar ? To address this question , we first carried out homology modeling of the PmrA DNA-binding domain in complex with DNA using the structure of the DNA-binding domain of the Escherichia coli PhoB response regulator in complex with its DNA target [28] . This analysis revealed that the amino acid residue at position 211 is located in a flexible loop likely to contact DNA ( Figure S1B ) . Because DNA is negatively charged , we anticipated that a PmrA protein with glutamate at position 211 would bind its DNA target less efficiently than a PmrA with glycine at this position . To test this notion , we examined the ability of the two purified PmrA proteins ( C-terminally tagged with His6 ) to bind a DNA fragment carrying the pbgP promoter , which is fully conserved in S . paratyphi B and S . typhimurium . Using an electrophoretic mobility shift assay , the purified phosphorylated PmrA ( G211 ) protein bound more effectively to the pbgP promoter fragment than the purified phosphorylated PmrA ( E211 ) protein ( Figure 4A ) . The shifting was specific because it could be competed out by the same unlabelled fragment ( Figure 4A ) but not by an unrelated one ( Figure 4A ) . Next , we examined whether the PmrA orthologs differed in their abilities to dimerize since dimerization is known to promote response regulator binding to DNA [29] , [30] . Analytical gel filtration with the phosphorylated forms of the PmrA ( G211 ) and PmrA ( E211 ) proteins revealed that PmrA ( E211 ) -P has a lower propensity for dimerization compared to PmrA ( G211 ) -P ( Table 1 and Figure S2 ) , which might contribute to the reduced binding of PmrA ( E211 ) -P to target promoters ( Figure 4A ) . Because PmrA-P autogenously controls its own expression and that of its cognate sensor PmrB from a PmrA-activated promoter located upstream the pmrCAB operon [31]–[33] , we wondered whether the much lower affinity of PmrA ( E211 ) than PmrA ( G211 ) for DNA impacted the former's ability to positively autoregulate itself . We examined the amount of total PmrA protein in isogenic S . typhimurium strains expressing HA-tagged versions of PmrA ( G211 ) or PmrA ( E211 ) from the normal chromosomal location grown under low Mg2+ conditions . Western blotting with anti-HA antibodies demonstrated that the S . typhimurium ( pmrA E211 ) strain produced much less PmrA protein than the isogenic pmrA ( G211 ) strain ( Figure 4B ) ; by contrast , both strains displayed similar amounts of RpoB ( Figure 4B ) , which is produced independently of the PmrA/PmrB system . Taken together , these results indicate that the reduced affinity of PmrA ( E211 ) for target promoters impedes positive autoregulation of the PmrA/PmrB system and production of PmrA ( E211 ) . Consequently , bacteria harboring the pmrA ( E211 ) allele do not accumulate high enough levels of PmrA ( E211 ) protein and are unable to transcribe PmrA-dependent genes in low Mg2+ , unlike those expressing the pmrA ( G211 ) gene . Why does the single amino acid difference between the PmrA proteins from S . paratyphi B and S . typhimurium render transcription of PmrA-activated genes dependent on PmrD in the former but not in the latter serovar when Fe3+ is present ? And how does S . paratyphi B overcome the lower affinity of its PmrA protein for target promoters in order to stimulate PmrA-dependent expression under such conditions ? When bacteria experience inducing conditions for the PmrA/PmrB system , the sensor PmrB phosphorylates the DNA binding protein PmrA , increasing PmrA's affinity for target promoters and resulting in transcription of PmrA-activated genes [13] , [34] . The PmrD protein , which is produced in low Mg2+ , promotes the phosphorylated state of PmrA by protecting it from dephosphorylation by PmrB , an activity primarily present under PmrA non-inducing conditions [17] . Therefore , we hypothesized that the PmrA ( G211 ) and PmrA ( E211 ) proteins might differ in one or more of these biochemical properties , which , in turn , might impact the levels of phosphorylated PmrA in vivo . First , we analyzed phosphotransfer from PmrB to each of the two purified PmrA proteins and determined that the identity of the amino acid residue at position 211 does not impact PmrA's ability to accept a phosphoryl group from PmrB ( Figure S3A and S3B ) . ( These experiments were performed with the purified cytoplasmic domain of the PmrB protein ( PmrBc ) because it retains all the known enzymatic activities of the full-length PmrB protein [17] . ) The phosphorylated PmrA ( G211 ) and PmrA ( E211 ) proteins also displayed comparable rates of PmrBc-mediated dephosphorylation in the absence of PmrD ( Figure 5A and 5B , Figure S3C and S3D ) . However , we determined that PmrA ( E211 ) -P is better protected by PmrD from PmrB's phosphatase activity than PmrA ( G211 ) -P ( Figure 5A and 5B , Figure S3C and S3D ) . Next , we examined whether the heightened protection of PmrA ( E211 ) -P by PmrD led to higher levels of phosphorylated PmrA protein in vivo when bacteria were incubated with low Mg2+ and high Fe3+ . Cell lysates from S . typhimurium strains expressing HA-tagged versions of PmrA ( G211 ) or PmrA ( E211 ) from the normal chromosomal location were separated on a Phos-tag gel , which retards phosphorylated proteins more than their unmodified forms and has been used to examine phosphorylated response regulators in vivo [35] , 36 . Western blotting with anti-HA antibodies revealed that the S . typhimurium ( pmrA E211 ) strain had a higher proportion of PmrA-P compared to the isogenic pmrA ( G211 ) strain ( Figure 5C and 5D ) , despite both strains having similar levels of total PmrA protein ( Figure 5C ) . Cumulatively , these findings indicate that PmrD is more efficient in protecting PmrA ( E211 ) -P than PmrA ( G211 ) -P from PmrBc-mediated dephosphorylation , leading to increased amounts of phosphorylated PmrA ( E211 ) in bacteria experiencing PmrD- and PmrA-inducing conditions . Because PmrA-P constitutes the active form of PmrA that binds target promoters in vivo [34] , such an increase appears sufficient to compensate for the PmrA ( E211 ) protein's lower affinity for DNA , resulting in PmrA-dependent gene expression . An S . typhimurium strain harboring the pmrA505 allele can transcribe PmrA-activated genes in a pmrD mutant and under non-inducing conditions for the PhoP/PhoQ system [14] ( Figure 5E ) . This is because the PmrA505-P protein , which harbors a histidine residue instead of arginine at position 81 , is resistant to dephosphorylation by PmrB in vitro [17] , presumably resulting in increased levels of PmrA-P in vivo . We hypothesized that this increase might be sufficient to overcome the DNA-binding defect of the PmrA ( E211 ) protein , enabling it to activate expression of PmrA-dependent genes in response to the low Mg2+ signal . As predicted , the R81H substitution rescued the ability of PmrA E211 to promote pbgP transcription . When bacteria experienced low Mg2+ , the S . typhimurium strain with the pmrA ( R81H E211 ) gene transcribed pbgP to levels similar to those produced in response to Fe3+ ( Figure 5E ) . The pmrA ( R81H E211 ) strain expressed pbgP when grown in high Mg2+ , though not to the levels displayed by the isogenic strain harboring the pmrA ( R81H G211 ) allele ( Figure 5E ) . Furthermore , pbgP transcription was ∼5-fold higher in a pmrA ( R81H E211 ) derivative deleted in pmrD than in the isogenic pmrA ( E211 ) ΔpmrD strain when encountering low Mg2+ + high Fe3+ ( Figure 5E ) . Yet , the levels were several fold lower than those produced by the pmrA ( R81H G211 ) ΔpmrD strain ( Figure 5E ) . These results indicate that the R81H substitution in PmrA can partially overcome the defect of the E211 allele . We previously reported that when an S . typhimurium ( pmrA G211 ) strain experiences Fe3+ , there is a surge in the mRNA levels of PmrA-activated genes , which increase , peak and then decrease to reach new steady-state levels in a manner reflecting the amount of PmrA-P protein [12] . Because the PmrA ( E211 ) protein has a lower affinity for the pbgP promoter in vitro than the PmrA ( G211 ) protein ( Figure 4A ) , we reasoned that a strain with the pmrA ( E211 ) allele might differ in the kinetics with which PmrA-dependent transcripts are produced in vivo . To test this idea , bacteria were grown under non-inducing conditions for the PmrA/PmrB system , shifted to media containing low Mg2+ + high Fe3+ and incubated for different extents of time . In the pmrA ( G211 ) strain , the pbgP and pmrC mRNAs peaked at 5 min and 10 min , respectively , before decreasing to steady-state levels ( Figure 6A and 6B ) . By contrast , in the pmrA ( E211 ) strain , the transcripts increased steadily over 60–90 min ( Figure 6A and 6B ) . Deletion of the pmrD gene abolished pbgP and pmrC expression in the pmrA ( E211 ) strain ( Figure 6C and 6D ) , but it decreased expression of these mRNAs only modestly in the pmrA ( G211 ) strain ( Figure 6C and 6D ) . Thus , the pmrA allele affects both the conditions in which PmrA-dependent genes are expressed and the kinetics with which genes are transcribed when bacteria experience inducing conditions . S . paratyphi B can cause chronic infections by persisting in the gallbladder for many years [37] , [38] . The ability of the related S . typhi to form biofilms on cholesterol-coated gallstones is believed to facilitate colonization of the gallbladder [39]–[41] . Thus , we investigated whether the pmrA allele altered S . enterica's ability to form biofilms on cholesterol-coated surfaces using an assay developed by the Gunn laboratory [42] . Biofilm formation was higher in S . paratyphi B ( pmrA E211 ) compared to the isogenic pmrA ( G211 ) strain ( Figure 7A ) . Deletion of the pmrD gene further increased biofilm formation in the S . paratyphi B ( pmrA E211 ) strain , which reached levels similar to those displayed by a pmrA null mutant ( Figure 7A ) . This was expected because the ability of S . paratyphi B ( pmrA E211 ) to express PmrA-dependent genes requires the PmrD protein ( Figure 3A , Figure 6C and 6D ) . By contrast , the S . paratyphi B ( pmrA G211 ) strain deleted for pmrD displayed low levels of attachment to cholesterol-coated surfaces , like the isogenic pmrD+ strain ( Figure 7A ) . The growth rates of these strains are similar ( Figure S4A ) and therefore , are not responsible for the detected differences in biofilm formation . These phenotypes are mediated by the pmrA gene and do not appear to involve genes that are specific to S . paratyphi B because they can be recapitulated in an S . typhimurium strain background ( Figure 7B and Figure S4B ) .
Why is the glutamate residue at position 211 of the PmrA protein evolutionarily conserved among S . paratyphi B ( SPV ) isolates that cause paratyphoid fever , which is in contrast to S . typhimurium and other natural isolates of S . enterica that contain a glycine residue at that position ( Figure S1A ) ? It has been proposed that variation in polymyxin B resistance among enteric bacteria reflects an organism's lifestyle [44] . Therefore , the degree of antibiotic resistance conferred by the pmrA ( E211 ) allele might be sufficient for S . paratyphi B to proliferate in its particular ecological niche . Furthermore , bacteria harboring the pmrA ( E211 ) allele exhibited enhanced biofilm formation on cholesterol-coated surfaces ( Figure 7 ) , which constitutes an in vitro model that mimics bacterial attachment to the surfaces of human gallstones [45] . This property may promote S . paratyphi B's survival in the gallbladder lumen , where it establishes chronic infection [37] , [38] , [40] , [46] . Such fitness benefits might contribute to the maintenance of the pmrA ( E211 ) allele in S . paratyphi B natural populations ( Figure S1A ) . Yet , S . typhi and S . paratyphi A also persist within the gallbladder [37] , [47] in spite of the fact that they encode PmrA proteins identical to that of S . typhimurium ( Figure S1A ) . This suggests that serovars Typhi and Paratyphi A utilize different regulatory strategies for colonization of and/or survival within the gallbladder than serovar Paratyphi B ( SPV ) . Bacterial biofilms are major contributors to persistent infections [48] . We determined that the heightened activity of the PmrA/PmrB system inhibits biofilm development ( Figure 7 ) , even though PmrA does not appear to affect the expression of genes encoding major components of S . enterica biofilms [49]–[51] ( Figure S4C ) . This adds to the diversity of regulatory mechanisms that control S . enterica biofilm formation on cholesterol-coated surfaces [40] , [42] , [52] . Our results , together with the finding that PmrA-dependent genes are downregulated in S . typhimurium biofilms compared to planktonic cells [53] , suggest that PmrA-regulated gene products interfere with S . enterica biofilms . Yet , others have detected expression of PmrA-dependent transcripts encoding LPS modification enzymes in biofilms formed by Pseudomonas aeruginosa [54] and commensal Escherichia coli [55] . Thus , it would appear that complex and distinct gene expression programs underlie biofilm formation in various bacterial species , which differ depending on the environmental signal ( s ) and the type of surface to which bacteria attach [51] . Why does the S . paratyphi B PmrA require the PmrD protein to promote expression of PmrA-activated genes even when bacteria encounter the Fe3+ signal that is directly detected by the PmrB sensor [10] ? The ability of bacteria to express PmrA-dependent genes requires the accumulation of sufficiently high levels of PmrA-P , the active form of the protein that promotes gene transcription [34] . We determined that the PmrA ( E211 ) protein binds with lower affinity in vitro ( Figure 4A ) . Yet , the levels of PmrA ( E211 ) -P are enhanced by PmrD to a larger extent that those of PmrA ( G211 ) -P ( Figure 5A and 5B ) . This builds up PmrA ( E211 ) -P to high enough levels in vivo ( Figure 5C and 5D ) thereby advancing its binding to target promoters and gene expression during growth in low Mg2+ and high Fe3+ ( Figure 3A ) . By contrast , the amount of PmrA ( E211 ) protein , and thus active PmrA ( E211 ) -P , is insufficient to promote transcription when bacteria are incubated in low Mg2+ alone ( Figure 3A and Figure 4B ) . Consistent with this notion , an amino acid substitution in the PmrA ( E211 ) protein that was previously shown to render PmrA ( G211 ) -P resistant to PmrB-promoted dephosphorylation [17] restored pbgP transcription in the presence of low Mg2+ or under repressing conditions for the PmrA/PmrB system ( Figure 5E ) . We suggest that the dissimilar affinities displayed by the PmrA orthologs for target promoters distinguish the ability of bacteria to survive in their particular niches upon experiencing the presence of Fe3+ . The decreased affinity of PmrA ( E211 ) for target DNA ( Figure 4A ) might result in it promoting transcription of genes with high affinity binding sites but not those with low affinity sites . Hence , an organism harboring the PmrA ( E211 ) protein will not necessarily promote expression of all the genes activated by an organism harboring the PmrA ( G211 ) protein . The lower affinity of the PmrA ( E211 ) protein for target promoters also impedes positive autoregulation of the pmrCAB operon ( Figure 4B ) , a property that governs the transient increase in PmrA activity when bacteria initially experience Fe3+ [12] . Consequently , the PmrA ( E211 ) protein confers slower PmrA-dependent gene expression kinetics than the PmrA ( G211 ) protein when bacteria first encounter Fe3+ , even though the levels of PmrA-activated mRNAs eventually reach similar steady state levels ( Figure 6A and 6B ) . Our findings raise the possibility that such disparate expression dynamics in bacteria harboring the pmrA ( G211 ) versus the pmrA ( E211 ) allele lead to distinct cellular behaviors , as previously demonstrated in other signal transduction systems [12] , [56]–[58] . Our work provides a singular example of how different alleles of a conserved transcription factor can display disparate signal prerequisites for activating gene expression . Importantly , these differences are independent of both the signal-sensing domain of the upstream sensor protein that controls the activity of the transcription factor [59] , [60] and of the network architecture of these signaling systems [61] , [62] . Similarly , amino acid substitutions in the transcription factor CEPBP from placental mammals reorganized the location of key phosphorylation sites , changing the way the protein responds to signaling pathways compared to the non-mammalian ortholog [63] . Allelic variation in transcription factors can also affect the ability of orthologous regulatory pathways to control gene expression in response to signal availability . For instance , a single amino acid difference between the E . coli strain B and the E . coli K12 arginine repressor results in transcription of arginine biosynthesis genes in E . coli strain B even when arginine is present , whereas repression of these very genes is selected for in E . coli K12 [64] . Therefore , in addition to modifying protein-protein interactions and altering the recognition of particular DNA-binding motifs [65]–[68] , allelic variation among transcription factors results in different interpretations of signals , leading to phenotypic diversity . We established that a single amino acid difference in the response regulator PmrA impacts several of its biochemical properties . First , substitution of the neutral glycine residue by the negatively charged glutamate at position 211 of the C-terminal DNA-binding domain decreased PmrA's association with its target promoters ( Figure 4A ) . This could be ascribed to electrostatic repulsion with DNA . Second , we determined that an amino acid substitution in the DNA-binding domain of PmrA allosterically affects biochemical activities ascribed to the N-terminal receiver domain in other response regulators [69] . Specifically , the substitution at position 211 reduced the PmrA ( E211 ) protein's propensity to dimerize ( Table 1 ) , likely contributing to its decreased DNA binding affinity since response regulator dimerization promotes binding to target DNA [29] , [30] . Third , PmrA ( E211 ) -P was more resistant to PmrBc-mediated dephosphorylation than PmrA ( G211 ) -P when PmrD was present ( Figure 5A and 5B ) . Therefore , an amino acid substitution in the C-terminal domain of PmrA renders this protein dependent on PmrD , which was previously shown to interact with the N-terminal domain of PmrA-P [17] . Finally , we demonstrated that the PmrA ( E211 ) -P protein exhibits lower affinity for a target promoter than PmrA ( G211 ) -P ( Figure 4A ) even though the levels of PmrA ( E211 ) -P are higher than those of PmrA ( G211 ) -P protein in vivo ( Figure 5C and 5D ) . These results argue against the proposal that DNA binding stimulates response regulator phosphorylation [70] . The continuous increase in genomic information has resulted in organismal behavior being deduced from the presence/absence of genes whose biochemical activity was experimentally determined in orthologs , usually in a model organism . However , our work illustrates the potential danger in adopting this approach , even for closely related organisms belonging to the same species . We established that a single amino acid difference in a natural allele of a 222 amino acid long transcription factor affected its dependence on a horizontally acquired gene product . Consequently , this changes the environments in which the regulon controlled by the transcription factor is expressed , giving rise to phenotypic differences between closely related bacteria . Our findings , and those of others [65] , [71] , underscore that subtle amino acid differences among orthologous proteins , which cannot be readily predicted from sequence conservation and computational comparisons of related genomes , contribute to the existing phenotypic diversity within and across species .
Bacterial strains and plasmids used in this study are listed in Table S2 . S . enterica serovar Typhimurium strains were derived from the wild-type strain 14028s . S . enterica serovar Paratyphi B strains were derived from SARA46 [21] , unless otherwise indicated . Bacteria were grown at 37°C with aeration in Luria-Bertani ( LB ) broth or in N-minimal media ( pH 7 . 7 ) and supplemented with 0 . 1% casamino acids , 38 mM glycerol , 10 µM or 10 mM MgCl2 and 100 µM FeSO4 [72] . When necessary , antibiotics were added at the following final concentrations: ampicillin , 50 µg/ml; chloramphenicol , 20 µg/ml; kanamycin , 50 µg/ml; and tetracycline , 10 µg/ml . Phage P22-mediated transduction of S . enterica strains was performed as described [73] . E . coli DH5α was used as a host for the preparation of plasmid DNA . Strain EG14331 , which has a MudJ transposon insertion in pbgP and expresses the pmrA ( E211 ) gene from the normal chromosomal location , was constructed by combination of the one-step inactivation method [74] and Lac+ selection . A PCR fragment encompassing the coding region of the pmrA ( E211 ) gene was amplified using primers 2426 and 2428 ( Table S3 ) and S . paratyphi B genomic DNA as template and recombined into the pmrA region in the EG14326 chromosome . Lac+ colonies were selected on N-minimal media plates ( pH 5 . 8 ) with 0 . 1% casamino acids , 10 µM MgCl2 , and 100 µM FeSO4 and supplemented with 1 . 3% lactose as the sole carbon source . Strain EG18502 , which harbors a C-terminal HA-tagged version of the S . typhimurium pmrA gene , was constructed using a modification of the one-step inactivation protocol [74] . A CmR cassette was amplified from plasmid pKD3 using primers 7994 and 7995 ( Table S3 ) . The PCR product was gel purified and electroporated into S . typhimurium containing plasmid pKD46 [74] selecting for chloramphenicol-resistant transformants at 37°C . The resultant strain ( EG18501 ) harbored an HA sequence immediately upstream of the stop codon of the pmrA coding region followed by a CmR cassette . The CmR cassette was removed using plasmid pCP20 as described [74] . Strain DC53 , which harbors a C-terminal HA-tagged version of the S . paratyphi B pmrA gene , was constructed using a modification of the one-step inactivation protocol [74] . DNA fragments that encompassed the S . paratyphi B pmrA ORF and a CmR cassette downstream of the pmrA gene were generated by performing two sequential PCR reactions . The S . paratyphi B pmrA ORF was amplified with primers 2426 and 11363 using 14028s genomic DNA as a template; the CmR cassette was amplified from plasmid pKD3 using primers 7995 and 11269 ( Table S3 ) . A second PCR was performed using the first two amplicons as templates and primers 2426 and 11269 ( Table S3 ) . The PCR product was gel purified and electroporated into S . typhimurium containing plasmid pKD46 [74] selecting for chloramphenicol-resistant transformants at 37°C . The resultant strain ( DC51 ) harbored a HA sequence immediately upstream of the stop codon of the pmrA coding region followed by a CmR cassette . The CmR cassette was removed using plasmid pCP20 as described [74] . Strain DC274 , which has a CmR cassette immediately downstream of the pmrA ORF , was constructed by using a modification of the one-step inactivation protocol [74] . A PCR product encompassing the CmR cassette was generated using primers 12235 and 12437 ( Table S3 ) and pKD3 as template . The PCR product was gel purified and electroporated into S . typhimurium containing plasmid pKD46 [74] selecting for chloramphenicol-resistant transformants at 37°C . Strain EG16275 , which has a MudJ transposon insertion in pbgP and expresses the pmrA ( G211 ) gene from the normal chromosomal location , was constructed by combination of the one-step inactivation method [74] and Lac+ selection . A PCR fragment encompassing the coding region of the pmrA ( G211 ) gene was amplified using primers 2426 and 2428 ( Table S3 ) and S . typhimurium 14028s genomic DNA as template and recombined into the pmrA region in the DC306 chromosome . Lac+ colonies were selected on N-minimal media plates ( pH 5 . 8 ) with 0 . 1% casamino acids , 10 µM MgCl2 , and 100 µM FeSO4 and supplemented with 1 . 3% lactose as the sole carbon source . To construct an S . paratyphi B derivative of strain SARA46 harboring the pmrA ( G211 ) ORF from the normal chromosomal location ( DC280 ) , we used a modification of the one-step inactivation protocol [74] . A TetR cassette was amplified using primers 11408 and 11409 ( Table S3 ) and genomic DNA from strain MS7953s , which harbors a Tn10 transposon in the phoP gene [75] . The PCR product was gel purified and used to electroporate S . paratyphi B SARA46 containing plasmid pKD46 [74] selecting for tetracycline-resistant transformants at 30°C . The resultant ΔpmrA::tetR strain ( DC167 ) containing plasmid pKD46 was kept at 30°C . A DNA fragment that encompassed the S . typhimurium pmrA ORF and a CmR cassette downstream of the pmrA gene was amplified with primers 12533 and 12534 ( Table S3 ) using DC274 genomic DNA as a template . The PCR product was gel purified , electroporated into strain DC167 containing plasmid pKD46 to obtain chloramphenicol-resistant recombinants , which were then screened for tetracycline sensitivity . The CmR cassette was removed using plasmid pCP20 as described [74] . To construct an S . paratyphi B derivative of strain SARA46 harboring the pmrA ( G211 ) allele at the normal chromosomal location , we used a modification of the one-step inactivation protocol [74] . A CmR cassette was amplified from plasmid pKD3 using primers 12235 and 12437 ( Table S3 ) . The PCR product was gel purified and electroporated into S . paratyphi B SARA46 containing plasmid pKD46 [74] selecting for chloramphenicol-resistant transformants at 37°C . The CmR cassette was removed using plasmid pCP20 as described [74] . All gene replacements described above were confirmed by sequence analysis . Plasmid pT7-7-PmrA ( E211 ) -His6 encoding the pmrA ( E211 ) with a His6 tag at the C-terminus was constructed by cloning a PCR fragment generated with primers 2453 and 2454 ( Table S3 ) and S . paratyphi B SARA46 DNA as a template between the NdeI and HindIII sites of plasmid pT7-7 . C-terminally His-tagged PmrA proteins from S . paratyphi B and from S . typhimurium , and the N-terminally His-tagged cytoplasmic domains of the PmrB protein ( PmrBc ) or PmrBc T156R mutant from S . typhimurium were overproduced in E . coli strain EG13796 harboring plasmids pT7-7-PmrA ( E211 ) -His6 , pT7-7-PmrA ( G211 ) -His6 , pT7-7-His6-PmrBc and pT7-7-His6-PmrBc T156R as described [15] . β-galactosidase assays were carried out in triplicate , and the activity was determined as described [76] . Bacteria from overnight cultures grown in N-minimal medium at pH 7 . 7 with 10 mM MgCl2 were washed two times with N-minimal medium containing no Mg2+ , and added into the appropriate fresh media with 1∶50 dilution . The bacterial cultures were grown in a shaking water bath for 4 h at 37°C before the assay . Data correspond to the mean values of three independent experiments performed in duplicate . Biochemical assays were carried out as in [15] , [17] . Data correspond to the mean values of three independent experiments . The pbgP , mgtA and ompX DNA fragments for gel mobility shift assays were generated by using PCR primer pairs 767 and 955 , 7192 and 7195 , and 9198 and 9202 , respectively ( Table S3 ) and genomic DNA of S . typhimurium 14028s as template . The DNA fragments were gel-purified with QIAquick columns ( Qiagen ) , and 150 ng of DNA was labeled using T4 polynucleotide kinase ( NEB ) and ( γ-32P ) ATP at 37°C . Unincorporated ( γ-32P ) ATP was removed using G-50 microcolumns ( Amersham ) . 10 µM His-tagged PmrA-H6 was incubated for 60 min at room temperature with 5 µM H6-PmrBc T156R ( a PmrB mutant that was shown to possess autokinase and phosphotransferase activity but lacks phosphatase activity [17] ) in the presence or absence of 1 mM ATP to generate phosphorylated or unphosphorylated PmrA , respectively . 104 cpm of labeled probe , 200 ng poly ( dI-dC ) ( Amersham ) and 0 , 100 , 200 or 300 pmol of phosphorylated or unphosphorylated His-tagged PmrA ( E211 ) or PmrA ( G211 ) proteins were mixed with binding buffer ( 20 mM Hepes ( pH 8 . 0 ) , 10 mM KCl , 2 mM MgCl2 , 0 . 1 mM EDTA , 0 . 1 mM DTT , 50 µg/ml BSA , and 10% glycerol ) in a final volume of 30 µl and incubated at room temperature for 20 min . Samples were run on 4–20% TBE gels ( Invitrogen ) , dried and then autoradiographed using a BAS-5000 imaging system and phosphor imaging plate ( Fuji Film ) . 180 µl PmrA ( 50 µM ) was phosphorylated using pGEX-PmrBc T156R beads as described [17] . Fast performance liquid chromatography ( FPLC ) experiments were conducted with an AKTA FPLC system ( GE Healthcare ) at 4°C . 100 µl of phosphorylated PmrA ( G211 ) or PmrA ( E211 ) was individually applied to a Superdex 200 10/300 GL column ( GE Healthcare ) that had been pre-equilibrated with 1× TBS/1 mM MgCl2/1 mM DTT/10% glycerol . Proteins were then eluted in the same buffer at a flow rate of 0 . 5 ml min−1 . Absorbance was monitored at 280 nm and fractions were analyzed by SDS-PAGE . The column was calibrated with a mixture of protein molecular mass standards ( GE Healthcare ) , containing aprotinin ( 6 . 5 kDa ) , ribonuclease A ( 13 . 7 kDa ) , carbonic anhydrase ( 29 kDa ) , ovalbumin ( 44 kDa ) and conalbumin ( 75 kDa ) , which was applied to the column under similar conditions . Bacteria from overnight cultures grown in N-minimal medium at pH 7 . 7 with 10 mM MgCl2 were washed twice with N-minimal medium containing no Mg2+ and added into fresh N-minimal medium at pH 7 . 7 with 10 µM MgCl2 and 100 µM FeSO4 with 1∶50 dilution . The bacterial cultures were grown to OD600 0 . 4 in a shaking water bath at 37°C before harvesting the cells at 4°C and resuspending the cell pellet with 1 ml ice-cold 20 mM Tris , pH 7 . 0 ( Ambion ) . The samples were then added to a 2 ml Lysing Matrix Tube ( MP Biomedicals ) and lysed three times for 40 s at 6 m/s using the FastPrep-24 instrument ( MP Biomedicals ) . The tubes were spun down to remove the Lysing Matrix beads and 100 µl cell lysate was added to 100 µl 2× Laemeli buffer ( Biorad ) and boiled for 3 min . Equivalent amounts of each sample ( normalized to OD600 ) were run on a 4–12% Bis-Tris gel ( Invitrogen ) in 1× MES buffer ( Invitrogen ) , transferred to a PVDF membrane , and analyzed by Western blotting with an anti-HA monoclonal antibody ( Sigma ) or an anti-RpoB antibody ( Neoclone ) . Western blots were developed using anti-mouse IgG horseradish peroxidase-linked antibodies ( GE Healthcare ) and Supersignal west femto ( Pierce ) . To determine the levels of PmrA-P in vivo , samples were prepared as described in the previous section . 100 µl of cell lysate was added to 100 µl chilled 2× Laemeli buffer ( Biorad ) ( to detect phosphorylated PmrA ) or to 100 µl 2× Laemeli buffer ( Biorad ) and boiled ( to detect total PmrA ) . Samples were analyzed on a Phos-Tag gel as described [36] . Briefly , Phos-tag acrylamide gels containing 10% ( w/v ) 19∶1 acrylamide/Bis solution , 350 mM Tris-HCl , pH 8 . 8 , 75 µM Phos-tag and 150 µM MnCl2 were prepared . Stacking gels contained 4% ( w/v ) 19∶1 acrylamide/Bis solution and 130 mM Tris , pH 6 . 8 . Equivalent amounts of each boiled or unboiled sample ( normalized to OD600 ) were loaded onto the gel and run for 2 h at 4°C under constant voltage ( 150 V ) using chilled running buffer containing 1% ( w/v ) SDS , 25 mM Trizma base and 192 mM glycine . Gels were then equilibrated for 10 min with chilled transfer buffer containing 20% ( v/v ) methanol , 192 mM glycine and 25 mM Trizma base , with 1 mM EDTA to remove Mn2+ from the gel . Gels were incubated for an additional 10 min in transfer buffer without EDTA . Transfer to nitrocellulose membranes was performed using the Bio-Rad wet transfer apparatus under constant voltage ( 100 V ) for 60 min . Western blotting was carried out as described in the previous section . Bacteria from overnight cultures grown in N-minimal medium at pH 7 . 7 with 10 mM MgCl2 were washed twice with N-minimal medium containing no Mg2+ and added into fresh N-minimal medium at pH 7 . 7 with 10 mM MgCl2 with 1∶50 dilution . The bacterial cultures were grown to OD600 0 . 4 in a shaking water bath at 37°C , spun down and resuspended in 100 µl N-minimal medium at pH 7 . 7 with 1 mM MgCl2 , and then added into N-minimal medium at pH 7 . 7 containing no Mg2+ and 100 µM FeSO4 . 0 . 5 ml aliquots of cells were removed at the indicated time points , mixed with RNAprotect Bacteria Reagent ( Qiagen ) for stabilization of RNA , and total RNA was isolated using RNeasy Kit ( Qiagen ) with on-column DNase treatment . cDNA was synthesized using TaqMan ( Applied Biosystems ) and random hexamers . Quantification of transcripts was carried out by real-time PCR using SYBR Green PCR Master Mix ( Applied Biosystems ) in an ABI 7500 Sequence Detection System ( Applied Biosystems ) . The following primers ( Table S3 ) were used to analyze transcript levels: rrs ( 3023 , 3024 ) , pbgP ( 6522 , 6523 ) , pmrC ( 3007 , 3008 ) and pmrD ( 4491 , 4492 ) . The relative amount of cDNA was determined using a standard curve obtained from PCR with serially diluted genomic DNA , and results were normalized to the levels of 16S ribosomal RNA . Data correspond to the mean values of at least three independent experiments . The ability of bacteria to grow in the presence of polymyxin B was determined as follows . Bacteria were streaked onto N-minimal media plates , pH 5 . 8 , containing 1% agarose , 38 mM glycerol , 10 µM MgCl2 and 2 . 5 µg/ml polymyxin B with or without 100 µM FeSO4 and incubated at 37°C overnight before examination of the plates for bacterial growth . The ability of S . enterica strains to form biofilms on cholesterol-coated microcentrifuge tubes was determined as described [42] . Bacteria from overnight cultures grown in LB were added into fresh LB medium with 1∶50 dilution and grown at 37°C to OD600 0 . 5 . 100 µl of cells were added to cholesterol-coated Eppendorf tubes ( Fisher Scientific ) and incubated on a Nutator shaker at room temperature for 6 days . Each day , spent medium was removed , and the tubes were washed twice with LB medium before fresh LB medium was added . On day 6 , after incubating tubes at 60°C for 1 hr to fix the attached bacteria , 200 µl of 0 . 1% crystal violet was added to stain cells for 5 min at room temperature . The tubes were then washed with 1 ml 1× PBS until the solution ran clear , and 200 µl of 33% acetic acid was added to extract the crystal violet dye , which was quantified at OD570 using a Victor 3 1420 Multilabel counter ( Perkin Elmer ) . Data correspond to the mean values of three independent experiments performed in duplicate . The pmrA gene was amplified with high fidelity AccuPrime Taq DNA polymerase ( Invitrogen ) by using primers 2876 and 2877 ( Table S3 ) , which are upstream and downstream the pmrA ORF respectively . PCR products were purified with the QIAquick PCR purification kit ( Qiagen ) . Sequencing reactions were initiated by using primers 2878 , 2879 or 2880 , performed using Big Dye 3 . 1 ( Applied Biosystems ) and analyzed on a 310 Genetic Analyzer ( Perkin-Elmer ) . DNA sequences were translated by using Editseq 3 . 92 ( DNASTAR ) . The sequences of these pmrA genes as well as those previously determined in [18] were aligned using ClustalX [77] . The pmrA gene sequences have been deposited at GenBank under the accession numbers listed in Table S4 . | Horizontally acquired genes are typically viewed as independent units that confer new traits when introduced into different bacterial species . However , preexisting proteins in a bacterium can impact the ability of horizontally acquired gene products to bring about new functions when they target ancestral pathways . Here , we establish that a single amino acid difference in the ancestral transcription factor PmrA alters its dependence on the horizontally acquired gene product PmrD to promote gene expression within closely related Salmonella serovars . Consequently , S . enterica serovar Typhimurium , which infects a wide range of animals , expresses PmrA-dependent genes and displays antibiotic resistance in conditions that activate the PmrA and/or PmrD proteins . By contrast , the human-adapted S . enterica serovar Paratyphi B only does so in the presence of both PmrA- and PmrD-activating conditions . Bacteria harboring the Paratyphi B pmrA gene also exhibited enhanced biofilm formation , which may contribute to serovar Paratyphi B's persistent infection of the gallbladder . Our findings demonstrate that the ability of horizontally acquired genes to confer new traits can be affected by ancestral proteins , even within one bacterial species . Therefore , a protein's function in a given organism must be appreciated in the context of other proteins operating within the same genetic network . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbial",
"evolution",
"biology",
"microbiology",
"bacterial",
"pathogens"
] | 2012 | An Allele of an Ancestral Transcription Factor Dependent on a Horizontally Acquired Gene Product |
Cell-fate specification is typically thought to precede and determine cell-cycle regulation during differentiation . Here we show that endoreplication , also known as endoreduplication , a specialized cell-cycle variant often associated with cell differentiation but also frequently occurring in malignant cells , plays a role in maintaining cell fate . For our study we have used Arabidopsis trichomes as a model system and have manipulated endoreplication levels via mutants of cell-cycle regulators and overexpression of cell-cycle inhibitors under a trichome-specific promoter . Strikingly , a reduction of endoreplication resulted in reduced trichome numbers and caused trichomes to lose their identity . Live observations of young Arabidopsis leaves revealed that dedifferentiating trichomes re-entered mitosis and were re-integrated into the epidermal pavement-cell layer , acquiring the typical characteristics of the surrounding epidermal cells . Conversely , when we promoted endoreplication in glabrous patterning mutants , trichome fate could be restored , demonstrating that endoreplication is an important determinant of cell identity . Our data lead to a new model of cell-fate control and tissue integrity during development by revealing a cell-fate quality control system at the tissue level .
Many different cell cycle programs can be found in developing multicellular organisms [1] . Typically , embryonic cell cycles are short with a rapid sequence of the DNA-synthesis phase ( S-phase ) and mitosis ( M-phase ) rapidly generating cells or nuclei in a syncytium . At later stages of development , differentiating cells in many animal and plant species frequently enter an endoreplication cycle in which mitosis is skipped and DNA is re-replicated leading to polyploid cells [2]–[4] . However , very little is known about the biological importance of endoreplication and the resulting cellular polyploidy . Progression of mitotic cell cycles is controlled by cyclin-dependent kinase ( CDK ) –cyclin heterodimeric complexes . Their action is in particular required for the entry into S-phase and M-phase [5] . The major determinant of CDK activity is the abundance of the cyclin co-factor and cyclin levels are controlled both at the level of transcription and by protein degradation [6] . In particular two multi-protein complexes , the Skip-F-box-Cullin ( SCF ) complex and the Anaphase-Promoting-Complex/Cyclosome ( APC/C ) ligate ubiquitin moieties to cyclins marking them for subsequent degradation by the proteasome [7] , [8] . The specificity of these ubiquitin ligases is brought about by adaptor proteins , i . e . F-box proteins for the SCF complex and Cdh1/FZR/CCS52 and Cdc20/FZY for the APC/C [8]–[12] . In addition to cyclins , CDKs are also controlled by the binding of inhibitors , for instance p27Kip1 in mammalian cells [13] . Moreover , CDK activity is regulated by posttranslational modifications such that phosphorylation of a conserved threonin residue ( position 161 in Arabidopsis ) in the so-called T-loop is absolutely required for kinase activity [5] , [14] , [15] . Conversely , phosphorylation of two residues in the P-loop ( typically T14 and Y15 ) can block kinase activity . All these control mechanisms appear to be globally conserved in eukaryotes and are present from yeast to plants [16]–[18] . Similarly to the different types of cell division cycles that exist , there are also many different endocycle variants [1] , [2] . However , it appears that endoreplication and mitotic cycles share many of the important regulators , and likely S-phase cyclins and CDKs are also key components of an endoreplication cycle [2] . A well-studied example of endoreplicating plant cells are Arabidopsis leaf hairs ( trichomes ) [19] , [20] . Trichomes are single epidermal cells that develop on almost all aerial structures of Arabidopsis . They are regularly spaced and it has been found that patterning relies on a substrate-depletion and lateral inhibition mechanism [21]–[23] . The trichome pattern is established in the basal part of the leaf where the positive regulators ( activators of trichome fate ) GLABRA1 ( GL1 ) , a R2R3 MYB transcription factor , GLABRA3 ( GL3 ) , a bHLH transcription factor , and TRANSPARENT TESTA GLABRA1 ( TTG1 ) , WD-40 protein , are initially ubiquitously expressed . The current model postulates that due to stochastic fluctuations some cells express these regulators at a higher concentration and these differences in expression levels become greatly enhanced due to a positive feed back loop of the activator complex . In turn , the positive regulators induce the expression of inhibitors , small R3 single repeat MYB transcriptional regulators that are then released from cells that have high levels of activators and inhibit the formation of activator complexes in the surrounding cells [21] , [23] . So far six inhibitors have been identified: TRIPTYCHON ( TRY ) , CAPRICE ( CPC ) , ENHANCER OF TRY AND CPC ( ETC ) 1 , 2 , 3 ( also called CPC-LIKE PROTEIN [CPL] ) , and TRICHOMELESS ( TCL ) [24]–[28] . The current model suggests that once a cell has reached a certain threshold of activator complex , trichome fate is established and the incipient trichome cell starts to express downstream genes , such as the HD bZIP transcription factor GLABRA2 ( GL2 ) . Many downstream trichome-specific genes are then activated that regulate further outgrowth of the formation of typically three to four branches [29]–[31] . One of the earliest signs of trichome differentiation is the entry into an endoreplication cycle and along with further outgrowth trichomes undergo usually three to four rounds of DNA replication leading to a final DNA content of approximately 32C [19] , [20] , [32] . Endoreplication correlates well with trichome growth and typically mutants that have reduced endoreplication levels also display smaller trichomes with fewer branches while mutants with increased endoreplication levels have larger trichomes with more branches [33] , [34] . Two core cell-cycle regulators have so far been shown to be required for the trichome endoreplication cycle . The first one is CDKA;1 , the major regulator of mitotic cycles and the Arabidopsis homolog of the yeast Cdc2/CDC28 kinase . Either a substitution of the CDKA;1 amino acid T161 with D161 ( further on abbreviated as D ) or a mutant in which T14 has been substituted by D14 and Y15 by E15 ( abbreviated as DE ) resulted in reduced CDKA;1 kinase activity and both weak loss-of-function alleles displayed smaller nuclei in trichomes along with a reduction of trichome size [14] , [17] . The other cell-cycle regulator involved in trichome cell-cycle control is SIAMESE ( SIM ) , a putative CDK inhibitor , and in sim mutants endoreplication is partially converted into a mitotic program resulting in multicellular trichomes [35] , [36] . One likely target of SIM is a CDK-cyclin D complex since a paralog of SIM in rice can inhibit cyclin D action when expressed in yeast [37] . In addition , the misexpression of CYCLIN D3;1 ( CYCD3;1 ) in trichomes has resulted in the formation of multicellular trichomes [38] . A second target of SIM could be mitotic B-type cyclins since also the ectopic expression of a constitutive active B-type cyclin resulted in multicellular trichomes [39] , [40] . Remarkably , some of the trichome patterning genes appear to have a function in endoreplication control . Loss of GL3 function results in a reduction of endoreplication levels and conversely , try mutants undergo one additional round of endoreplication [34] . In addition , GL1 might also control endoreplication although the situation is less clear since in one report the overexpression of GL1 was found to result in increased endoreplication levels in trichomes while in another study no such effect in was observed [41] , [42] . Interestingly , it has been found that SIM is a direct early target of GL3 and GL1 pinpointing to a tight interaction between patterning genes and the regulation of the endoreplication cycle [43] . Here we have dissected the relationship between pattern formation and cell-cycle control by using plants with reduced endoreplication levels in trichomes . We found that trichome fate is surprisingly plastic and trichome fate can be changed into an epidermal pavement cell fate even in advanced stages of trichome differentiation . Our data show that progression through an endoreplication cycle is an important aspect of cell fate acquisition and is crucial for cell fate maintenance .
Since several trichome patterning mutants are also affected in endoreplication control , we asked whether there is a functional connection between cell-cycle progression and pattern formation during very early trichome development . We therefore first revisited sim mutants in which trichomes undergo cell divisions leading to multicellular trichome [35] , [36] . Remarkably , we found that under our growth conditions sim mutants develop significantly fewer ( T-test with p = 0 . 0001 ) trichome initiates sites ( TIS ) per leaf in comparison to wild type ( Figure 1G , dark blue bars ) . A patterning defect of sim mutants became even more prominent when we compared the number of trichomes per epidermal cell number ( light blue bars ) , here sim mutants had only approximately half of the trichomes found in wild type . To test whether cell-cycle progression and trichome initiation are functionally linked , we sought for additional possibilities to promote cell proliferation in trichomes . In earlier experiments , we have used the GL2 promotor to drive expression of CYCD3;1 or a N-terminally truncated B-type cyclin resulting in the formation of multicellular trichomes . Both the multicellular trichomes and the remaining single-celled trichomes , displayed a strong reduction in their ploidy level indicating that a mitotic cycle could inhibit and override an endoreplication cycle [38]–[40] . To manipulate cells during the initial patterning process we used here the promoters of the CPC and TRY gene that are active earlier than the GL2 promoter to drive expression of CYCD3;1 [44] . Similar to the expression of CYCD3;1 from the GL2 promoter , expression from the CPC and the TRY promoter resulted in multicellular trichomes . However , in addition , we obtained more than eight transgenic lines that were devoid of trichomes out of more than 20 primary transformants ( Figure 1A and 1B ) . We envisioned two scenarios that could explain these results . First , expression of CDCD3;1 and the loss of the CDK inhibitor SIM might interfere with trichome-fate establishment by promoting/allowing cell division of an incipient trichome cell and , thereby constantly diluting the presumptive activator levels . Alternatively , or in addition , endoreplication cycles may be instrumental for trichome patterning . To discriminate between these two possibilities , we analyzed plants that have reduced endoreplication levels in trichomes . Previously , we had generated plants that express the CDK inhibitor ICK1/KRP1 under the trichome-specific GL2 promoter , which led to strongly reduced endoreplication levels in trichomes [45] . This effect was even stronger when the N-terminally truncated ICK1/KRP1 variant ICK1/KRP1109–191 , which lacks the first 108 amino acids was expressed from the GL2 promoter . The ICK1/KRP1109–191 form of the protein displays increased protein stability and interacts more strongly with CDKA;1 and CYCD3;1 than the full length ICK1/KRP1 protein in yeast two hybrid experiments [45]–[49] . Examination of trichome numbers on leaves of these ICK1/KRP1-misexpressing plants revealed that trichome numbers were reduced ( Figure 1G and Table S1 ) . To complement this set of experiments , we analyzed two weak cdka;1 mutants , D and DE , that were previously found to display reduced endoreplication levels in leaves [14] , [17] . None of these genotypes resulted in multicellular trichomes or would be expected to favor increased cell division , making dilution of cell fate transcriptional activators unlikely as an explanation of the observed reduction in trichome number . Quantification of DAPI-stained trichomes revealed that both D and DE plants have decreased endoreplication levels in trichomes in comparison with wild-type plants ( Figure 2I and 2M ) and consistent with the above obtained results with ICK1/KRP1-misexpressing plants both weak cdka;1 loss-of-function mutants also displayed fewer trichomes on leaves than control plants ( Figure 1D , 1E , 1G , and Table S1 ) . To reduce endoreplication levels further , we combined D plants with plants misexpressing ICK1/KRP1109–191 . These plants displayed the severest trichome effect among the cell-cycle mutants studied and total trichome numbers dropped almost ten fold in comparison to wild type ( Figure 1F and 1G and Table S1 ) . It was previously shown that ICK1/KRP1 can act non-cell-autonomously and thus , PROGL2:ICK1/KRP1-expressing plants have typically fewer but larger epidermal cells that surround trichomes [46] . Plants with a reduction of CDK activity either due to a strong overexpression of CDK inhibitors or due to a compromised kinase , as in D and DE plants , have also generally larger epidermal cells [14] , [17] , [50] , [51] . This increase in cell size makes it difficult to discriminate between a reduction of trichome number due to reduced leaf size and fewer cells versus a bona fide patterning defect . To correct for cell-size and cell-number differences , we determined the ratio of trichomes per epidermal cells . This estimate revealed that there is indeed a true trichome patterning defect in both ICK1/KRP1 overexpressing plants as well as in D and DE plants ( Figure 1G , light blue bars; Table S1 ) . To substantiate that the reduction of trichome number is due to a true patterning defect rather than due to alterations of leaf growth , we analyzed plants expressing a cell-autonomous version of ICK1/KRP1 in trichomes; in this construct , the N-terminally truncated CDK inhibitor ICK1/KRP1109–191 is fused to GUS and GFP [46] , [49] . We could also observe significantly reduced trichome numbers in plants expressing the immobile GUS:YFP:ICK1/KRP1109–191 ( hereafter referred to as ICK1/KRP1im ) under the GL2 promoter ( T-test , p = 0 . 0001 ) confirming that local interference with endoreplication results in a patterning defect proper ( Figure 1C and 1G and Table S1 ) . To dissect the epistasis of the relationship between endoreplication and pattern formation , we introgressed the weak cdka;1 loss-of-function mutant D and DE as well as ICK1/KRP1-overexpressing plants into several trichome patterning mutants . First , we analyzed the effect of reduced endoreplication in cpc-try double mutants that develop large trichome clusters due to reduced lateral inhibition [44] ( Figure 3A ) . The combination with plants expressing the PROGL2:ICK1/KRP1109–191 construct or D and DE plants strongly reduced cluster formation and cluster size ( Figure 3C and 3E ) : 56 percent of trichome initiation sites now contain only one trichome and approximately 2/3 of the clusters form just two trichomes whereas in cpc-try double mutant 95 percent of the sites contain more than one trichome with more than 93 percent clusters containing more than two trichomes . Similar , although somewhat weaker , effects were found when introducing the cell-autonomously acting ICK1/KRP1im construct into cpc-try plants with 39 percent clusters and 18 percent cluster with only two trichomes ( Table 1 ) . This shows that a reduction in endoreplication can override the effect of patterning mutants . Next we analyzed crosses of D , DE , and PROGL2:ICK1/KRP1im with gl3 mutants , in which the trichome activator complex is compromised [22] , [43] , [52] . This combination resulted in a synergistic effect with a dramatic reduction of trichomes on leaf blades ( Figure 3B , 3D , 3F , and 3G ) . Interestingly , we found on these plants a number of rudimentary , aborting trichomes that appeared to be arrested in their development and that we could never observe on wild-type plants ( Figure 3D , 3F , and 3G ) . Taken together , these findings corroborate the importance of endoreplication , and place endoreplication control in an early phase of the trichome patterning process . The trichome pattern in Arabidopsis is established in the youngest part of a developing leaf . Therefore we analyzed the patterning zone of young leaves of plants with reduced endoreplication levels in trichomes . Consistent with previous studies [34] , [41] , [53] , we found that in wild type emerging trichomes appeared with a minimal distance of approximately 3 cells ( Figure 4A and 4B ) . As expected , epidermal cell size in the trichome patterning zone was much larger in D and DE plants than in wild type ( Figure 4C ) . None-the-less , trichomes were patterned with roughly the same distance as in wild type resulting in a similar number of trichomes per cells ( Figure 4C and 4D and Table S2 ) . Thus , while the trichome pattern on old leaves is significantly different between wild-type plants and plants with a compromised endoreplication cycle , the initial pattern of trichomes appears to be rather similar . We therefore analyzed next how the trichome pattern becomes different over time in plants with reduced endoreplication levels and first examined in detail young leaves of these plants by scanning electron microscopy . We found that outside of the trichome patterning zone of leaves of ICK1/KRP1-mixexpressing plants as well as our other mutant lines several trichomes were arrested in their development , i . e . large cells with an outgrowth cone typical of developing trichomes , but with a much wider base ( Figure 5C ) ; these trichomes were never found on wild-type leaves ( Figure 5A and 5B ) . Strikingly , we found a few cases where such an aborted trichome showed several constrictions suggesting recent cell divisions ( Figure 5D ) . Finally , we found unusual patches of cells that displayed common division planes ( Figure 5E ) . Typically , cell divisions in the wild-type leaf epidermis are not coordinated but mosaic whereas in the above identified cell patches division planes were aligned over more than eight cells ( Figure 5E ) . One explanation for this common orientation could be that a large precursor cell would have undergone many successive divisions . However , the cell size of this precursor must have been very large , much larger than the typical epidermal pavement cell . To understand the origin of these cell patches and to test whether they could be derived from aborting trichomes , we followed in vivo the fate of trichome initials on very young leaves . We first monitored wild-type trichomes labeled by GFP expressed from the GL2 promoter . We could track trichome initials developing into mature trichome cells during the time course of two days with pictures being taken every 24 hours; during the entire period the GL2 reporter gave a strong fluorescence signal ( Figure 6A and 6B ) . Since aborting trichomes are difficult to find , especially on young leaves that have a reduced number of epidermal cells in D , DE or KRP-misexpressing plants , we decided to follow trichome development in combinations of ICK1/KRP1-misexpressing plants with gl3 mutants since these plants show one of the largest discrepancies between young and old leaves in terms of trichome numbers ( compare Figure 3F and 3G with Figure 4D ) . In addition , we used for KRP expression an ICK1/KRP1-YFP fusion construct driven from the GL2 promoter to mark trichomes and monitor at the same time the accumulation of the KRP protein . Several leaves were observed over two to three days with pictures being taken every 24 hours . On many leaves we could observe that trichome initials were formed displaying strong YFP fluorescence similar to a fluorescent signal found in wild-type plants that express GFP under the control of the GL2 promoter . In contrast to wild type , we could detect in a number of cases in which trichomes did not grow out further but underwent cell division . Figure 6 shows an example of a leaf in which three trichome initials divide , two initials undergoing one cell division giving rise to two cells and one initial dividing even twice leaving a patch of four enlarged cells that resembled the cluster that we had previously seen by SEM . Moreover , in the aborting trichomes the YFP fluorescence rapidly diminished and was finally absent after two days . This could indicate that the KRP fusion protein would be rapidly degraded in this genetic background . However , non-aborting and outgrowing trichomes , as occasionally found on gl3–PROGL2: ICK1/KRP1-YFP plants , showed a strong YFP fluorescence ( Figure 6C ) . Thus , we conclude that in the aborting and dividing trichomes the GL2 promoter is switched off indicating that along with the entry into a mitotic cell cycle these cells have lost their trichome identity . To corroborate the trichome cell fate loss , we introgressed four trichome markers into D and DE plants , comprising of the 5′ regulatory region of GL2 , NOK , the putative kinase At2g36090 and the F-box protein F9C22 . 2 encoding gene AT2G36090 , respectively [29] , [54] . The GL2 reporter was found to be expressed in young wild-type and aborting trichomes but , while its expression continued in wild-type trichomes , the activity of the reporter ceased in aborting trichomes ( Figure S1 ) . In contrast , the reporter construct for NOK , At2g36090 and F9C22 . 2 were only active in mature , three-branched trichomes and were not found to be expressed in aborting trichomes of mutants with reduced endoreplication levels ( Figure S2 , data not shown ) . Finally , we measured the DNA content of these aborting trichomes . The aborting trichomes were found to have large nuclei , sometime even larger than wild-type trichomes at comparable stages ( see also Figure 6D′ and 6E′ ) . However , nuclear size is well known to not inevitably reflect DNA content and indeed the fluorescence intensity of DAPI-stained trichome nuclei was much weaker of aborting trichomes than of wild type ( Figure 7A–7C ) . Quantification of the exact DNA content of these aborting trichomes was difficult due to strong background fluorescent in young leaf parts and the often not clear distinction between a large epidermal cell and an aborting trichome during early developmental stages . None-the-less , our measurements always gave a similar trend with aborting trichomes having a DNA content between 2 and 4C , i . e . being not endoreplicated , in comparison with trichomes on wild-type plants at comparable stages that were found to have DNA levels between 4 and 8C ( Figure 7D ) . Taken together , our findings represent a case of trans-differentiation . Cells programmed to become trichomes and already expressing trichome identity genes changed their fate to an epidermal pavement cell program and were incorporated into the developing leaf epidermis . Rudimentary trichomes also occur on leaves of gl2 mutants and genetic combinations of gl2 with gl3 have a synergistic effect and were previously reported to display leaves completely devoid of trichomes [34] , [55] . In the light of our above findings , we hypothesized that a decrease of ploidy levels in gl2–gl3 double mutants might be the main factor responsible for the lack of trichomes . Therefore we introduced our set of lines with reduced endoreplication levels in trichomes into gl2 mutants . These genetic combinations with gl2 displayed a strong decline in trichome number and in the most severe cases ( with the gl2 DE double mutant ) all out-growing trichomes were eliminated ( Figure 8A–8C and 8J and Table S1 ) . This raised the question whether trichomes were initiated in gl2–gl3 double mutants but lose their fate similarly to the above made observations . Indeed , analysis by SEM revealed that large and distinct cells reminiscent of initial trichomes can be seen on young leaves but later on no traces of these cells are left ( Figure 8E ) . We hypothesized that stimulation of a mitotic cycle in gl2 mutants might further enhance trichome cell-fate loss . To test this we generated a gl2-sim double mutant and indeed the resulting double homozygous mutants resembled gl2–gl3 mutants with no trichomes left on the leaf epidermis ( Figure 8D ) . Again , we could find out-bulging cells on younger leaf parts indicating that trichomes were initially specified but lost their fate . GL2 and GL3 are transcription factors that play a major role during trichome development and to test how important endocycle control versus a failure of regulating other target genes is , we sought for a way to specifically stimulate an endoreplication cycle and/or block mitosis in gl2 and gl2–gl3 mutants . Since it was recently shown that CCS52 overexpression can induce endoreplication while blocking mitosis [56] , [57] , we generated a PROGL2:CCS52A1 construct and first introduced this into wild-type plants . Similar to the misexpression of CCS52 from the 35S promoter , its overexpressing under the GL2 promoter control resulted in overbranched trichomes with an increase in trichome endoreplication levels but no obvious alteration of the trichome pattern was observed [56] ( data not shown ) . Next , we transformed the PROGL2:CCS52 construct into gl2 and gl2–g3 mutants . Indeed , the number of trichomes and trichome-like structures in gl2 mutants that expressed PROGL2:CCS52 construct was higher than in gl2 mutants ( Figure S3A and S3B ) . The most striking effect was found in gl2–gl3 double mutants expressing CCS52: While old leaves of gl2–gl3 double mutants are devoid of trichomes , the CCS52 overexpression lines displayed distinct cells with an outgrowth cone that resembled trichomes on gl2 mutants ( Figure 8F and 8J ) . These trichome-like structures never branched or developed papillae typical for mature trichomes . However , given that GL2 and GL3 are two major regulators of trichome development , a complete restoration to wild-type like trichomes was not expected . To test the differentiation status of these cells , we introduced the above-used NOK , At2g36090 and F-box protein F9C22 . 2 promoter GUS reporter line into gl2 , gl3 , and into gl2-gl3 mutants that expressed CCS52 under the GL2 promoter control . In all genotypes analyzed , the At2g36090 and F9C22 . 2 reporter were only active in outgrowing and neither in aborting trichomes nor in the trichome-like structures found in gl2 or gl2–gl3 mutants expressing CCS52 ( Figure S2 , data not shown ) . In contrast , the NOK reporter was expressed in outgrowing gl2 mutant trichomes but was neither active in aborting trichomes in gl2 nor in gl2–gl3 mutants . However , in gl2–gl3 mutants expressing PROGL2:CCS52 we observed that almost all rudimentary trichomes displayed a strong and enduring expression of this marker line ( Figure 8G–8I ) . Similarly , gl2 mutants expressing PROGL2:CCS52 construct showed activity of the NOK reporter in the partially restored trichomes that developed in the center of the leaves ( Figure S3C and S3D ) . Thus , the block of the re-initiation of a mitotic program and promotion of endoreplication in gl2 and in gl2–gl3 double mutants is sufficient to at least partially maintain and promote trichome fate .
The data presented here show that endoreplication and the inhibition of mitosis are required for stabilization or maintenance of trichome cell fate , suggesting that there are developmental constraints on the patterning system . The switch from initial patterning to maintenance might be a sensitive phase in cell fate commitment , involving the transition from one major transcriptional program to another major transcriptional program , i . e . from a generic pattern module , also used for root hair patterning , to a cell type-specific readout , i . e . trichome morphology and physiology . Consistent with this view , genome-wide transcript profiling and the analysis of promoter reporter constructs have revealed that the expression of many patterning genes is strongly reduced in maturing trichomes [29] ( and reference list there in ) . At the same time , these profiling studies have shown that GL2 and SIM were among the most strongly expressed genes in mature trichomes in comparison to leaves without trichomes ( 99 . 4 and 28 . 0 fold , respectively ) . Since GL2 was identified as a direct target of a patterning gene complex [58]–[60] ( see also Figure 9 ) , one needs to postulate a second transcriptional input after/or in parallel to the initial pattern formation . Similarly , SIM expression was found to positively correlate with GL3 expression and chromatin immunoprecipitation experiments revealed that GL3 directly binds to the SIM promoter region [29] , [35] , [43] . It seems possible that endoreplication might be important to bridge these different transcriptional programs , for instance by increasing the intracellular concentrations of mRNAs for the proteins of the trichome activator complex ( Figure 9 ) . Additionally , endoreplication is known to influence chromatin dynamics [61] , and recent findings have even presented a molecular link between chromatin organization , pattern formation and cell-cycle control in particular during Arabidopsis root development . The Arabidopsis root epidermis is composed of alternating files of cells that all develop into hair cells ( trichoblasts ) or non-root hair cells ( atrichoblasts ) . Root hair patterning and trichome patterning share many of the same regulators , such as GL2 [21] , [62] . Evidence that the chromatin state of GL2 is connected to the specific cell fate in the root epidermis comes from the demonstration that the genomic region of GL2 was only accessible to FISH hybridization in atrichoblasts where GL2 is also expressed , and not in trichoblasts where the GL2 promoter is not active [63] . Remarkably , the chromatin state was found to be very dynamic and could be established during a single cell cycle , likely between M-phase and G1 phase . It is plausible that endoreplication might influence chromatin accessibility by fixing a certain chromatin state in trichomes . GL2 expression was recently found to be also controlled by protein called GEM for GL2-EXPRESSION MODULATOR and GEM misexpression resulted in reduced levels of GL2 whereas gem mutants displayed higher amounts of GL2 mRNA [64] . Consistently , gem mutants produced more trichomes and GEM misexpression lines produced fewer trichomes . GEM was found to influence histone modifications , i . e . acetylation and methylation , around genes involved in trichome patterning , i . e . GL2 and CPC , presenting one possibility for the expression control of GL2 . GEM was found to interact with CDT1 , a central component of the DNA replication machinery . CDT1 misexpression in turn enhances endoreplication levels in trichomes and trichome branch numbers [65] . In addition , in these CDT1 misexpression lines , GL2 is also upregulated offering a second link between DNA replication and GL2 expression [64] . Thus , chromatin organization and DNA replication might be more intrinsically linked to pattern formation , and could represent the second feedback loop that we have postulated to function during early trichome patterning ( Figure 9 ) . The observation that trichomes can lose their fate and can be completely reintegrated into the pavement layer may shed light on general principles in pattern formation and tissue organization . A key question is which constraint underlies the necessity of a second feed back loop after pattern formation . Two major possibilities might explain this: The first scenario ( cell-autonomous scenario ) is based on the consideration that all epidermal cells , including trichome initials , are formed from an epidermal ground state . In this scenario , pavement cell fate would be the default state that must be overwritten during commitment of cells to the trichome fate . A failure to stabilize this program would reveal the default fate and accordingly , aborting trichomes would return to a pavement cell stage . In this scenario , chromatin regulation might play a key role since it is known from Drosophila that cell fate is fixed and epigenetically inherited over many cell divisions by establishing repressive or activating chromatin states . Of key importance here are the Polycomb repressive complex one and two ( PRC1 and PRC2 ) and the trithorax complex . The PRC2 mediates the tri-methylation of lysine 27 of Histone H3 leading to the recruitment of PRC1 complexes and the stable inactivation of the respective chromatin segment [66]–[68] . Homologs of the animal PRC2 complex have been identified in plants and among other target genes , GL2 was found to be lysine K27 trimethylated in a PRC2-dependent manner [69] ( D . Bouyer and A . S . unpublished data ) . Another not mutually exclusively possibility is that the fate of an aborting trichome could be influenced by its neighboring cells ( non-cell-autonomous scenario ) . Support for non-cell-autonomous influences on maintenance of cell fate come from grafting or ablation experiments where it was found that a cell or its progeny , when invading from one developmental context to another , adapts its fate according to its new position [70]–[73] . The existence of locally acting tissue-specific supervision mechanisms are presumably very important to organize and maintain body architecture by correcting incorrectly oriented cell divisions . However , mutants that affect the organization of tissue layers are very rare , likely due to the fundamental nature of this process and the probable pleiotropic mutant phenotype . One example might be mutants in the receptor-like kinase CR4 from maize or its Arabidopsis homolog ACR4 that have been found to be crucial for epidermis development . Both are expressed in the epidermal cell layer but may receive signals from underlying cell layers , thus also coordinating inter-tissue organization [74]–[78] . Trichome cell fate seems to be determined by a very robust developmental program since trichomes can be initiated and differentiate in subepidmermal layers in try mutants plants that ectopically express GL1 [79] . It seems possible that endoreplication is one of the mechanisms that strongly stabilizes trichome fate and protects it from otherwise observed fate conversions induced by the neighboring cells . Interestingly , introgressing the patterning and endoreplication mutant gl3 into try-PRO35S:GL1 plants dramatically reduced the formation of subepidermal trichomes ( A . S . and M . H . , unpublished data ) . The emerging new tools to precisely study the development of single cell type for instance by laser dissection microscopy and a new round of mutant screens [31] will help to go in future one level deeper in the understanding of endoreplication during pattern formation and tissue integrity and will help to answer long standing questions in developmental biology .
Arabidopsis ( Arabidopsis thaliana ) plants were grown on soil under long-day conditions ( 16 h of light , 8 h of darkness ) between 18°C and 25°C at standard greenhouse conditions . To avoid the possibility of accession-specific variability in trichome development , only plants of the accession Columbia-0 ( Col-0 ) were used with the exception of the cpc-try double mutant , which is a combination of the accessions Landsberg erecta ( Ler ) and Wasilewskaja-0 ( WS-0 ) [44] . The gl2 , gl3 and sim mutants in Col-0 have been described previously[29] , [35] , [80] . For a cdka;1 mutant the previously characterized SALK T-DNA insertion allele was used [81] . The CDKA; 1T161D ( D ) and CDKA; 1T14D/Y15E ( DE ) rescue lines of cdka;1−/− were generated by Dissmeyer et al . ( 2007 , 2009 ) . The PROGL2:ICK1/KRP1 , PROGL2:ICK1/KRP1109–191 and PROGL2:GUS:YFP:KRP1109–191 ( ICK/KRP1im ) misexpression lines are characterized in Weinl et al . and Jakoby et al . [46] , [49] . The PROMOTER-GUS reporter lines for MYB106 At3g01140 and At2g36090 are described in Jakoby et al . [51] . Genotypes were confirmed by PCR , antibiotic selection and/or segregation analysis of the following generation . The PROGL2:ICK1/KRP1109–191 construct was transformed into cpc-try double mutants . For construction of PROCPC:CYCD3;1 , PROTRY:CYCD3;1 , and PROGL2:CCS52A1 the Gateway cloning system was used . The destination vectors pAMPAT-PROCPC and pAMPAT-PROTRY are a kind gift of Martin Pesch , University of Cologne [44] , the pAMPAT-PROGL2 vector has been previously described [46] . Molecular manipulations were performed according to standard procedures and plants were transformed by a modified version of the floral dip method according to Clough and Bent [82] . At least 20 transgenic plants were generated for all expression constructs . A number of representative reference lines displaying a typical phenotype were chosen for further analysis . Genotypes were confirmed by PCR , antibiotic selection and/or segregation analysis of the following generation . Sequence data for material used in this work can be found at TAIR ( www . arabidopsis . org ) and NCBI ( www . ncbi . nlm . nih . gov ) under the following accession numbers . For TAIR: At2g36090 , At3g48750 ( At CDKA;1 ) , At2g46410 ( At CPC ) , At4g34160 ( At CYCD3;1 ) , At2g36090 ( At F9C22 . 2 ) , At4g22910 ( At FZR2/CCS52A1 ) , At1g79840 ( At GL2 ) , At5g41315 ( At GL3 ) , At2g23430 ( At ICK1/KRP1 ) , At3g01140 ( At NOK ) , At5g04470 ( At SIM ) , At5g53200 ( At TRY ) . Germplasm information for deposited T-DNA-lines: cdka;1 ( SALK_106809/Germplasm: 4824368 ) , cpc ( CS6399/Germplasm:1007963690 ) , gl3–3 ( GK 545D05/Germplasm:3510637538 ) , sim-1 ( Germplasm:5529955621 ) , try-EM1 ( Germplasm:3510701804 ) . Light microscopy was performed with an Axiophot microscope ( Zeiss ) and confocal laser scanning microscopy with a TCS SP2 AOBS CLSM system ( Leica Microsystems ) . Scanning electron microscopy was done using a SUPRA 40VP ( Zeiss ) equipped with a K1250X Cryogenic SEM Preparation System ( EMITECH ) . For image processing Leica Confocal Software Lite 2 . 05 , Zeiss AxioVision 4 . 7 , Adobe Photoshop CS2 and Adobe Illustrator CS2 were used . Image analysis was performed with Image J 1 . 43l ( http://rsb . info . nih . gov/ij/ ) . For DNA quantification of trichome nuclei , rosette leaf 4 was vacuum infiltrated for 30 min in formaldehyde solution ( 3 . 7% formaldehyde in PBS , 0 . 1% Tween [PBST] ) followed by incubation at 4°C overnight . Samples were washed two times for 15 min in PBST . Afterwards , leaves were vacuum infiltrated in DAPI solution ( 0 . 25 mg/mL , 5% DMSO in PBST ) for 15 min and incubated overnight in DAPI solution at 4°C; thereafter , leaves were washed twice in PBST . The DAPI intensity was quantified and the background fluorescence was subtracted using the ImageJ software ( rsbweb . nih . gov/ij/ ) . The median value of Columbia and gl3-3 trichomes was set as 32C and 16C respectively . From this value , the corresponding C values of the trichome nuclei were estimated . For a comparison of the DNA content of young trichomes and aborting trichomes in the initiation zone , Z-sacks of the initiation zone were taken with an ApoTome ( Zeiss ) . The exact number and intensity of each pixel of the trichome nuclei were measured using the Z-axis profile plot function in ImageJ ( rsbweb . nih . gov/ij/ ) . The relative fluorescent units ( RFU ) were scaled by comparing to the RFU of trichome nuclei with nuclei of surrounding dividing epidermal cells . The smallest epidermal nuclei were set to 2C . The expression of the GUS protein was visualized as previously described [46] . For cell wall staining , leaves were directly mounted in a saturated solution of propidium iodide ( 100 µg/ml ) in water and incubated for 5 min . Trichomes were counted on leaf 3 and 4 at a leaf length of 4 mm length . Agarose ( 2% in Water ) prints were taken of each leaf allowing an accurate measurement of the total leaf size as well as cell numbers and cell sizes by light microscopy . Total cell numbers and cell sizes per leaf were estimated by counting the number of cells in a square of 10000 µm2 located at ¼ and ¾ of the distance between tip and base of a leaf , halfway between midrib and leaf margin . All measurements were repeated three times on separately grown plants . For analyzing early pattering processes , trichome initiation sites ( TIS ) were counted in the trichome initiation zone of leaf 4 before it reached a length of 800 µm . The trichome initiation zone was defined at the most basal region of a leaf restricted at its distal end by appearance of branched trichomes [41] Total cell numbers and cell sizes in a trichome initiation zone were estimated by counting all pavement cells per square of 961 µm2 . All measurements were repeated three times on separately grown plants . Plants were germinated and grown for 10 days on soil under long day conditions . Single leaves including the petioles and an upper part of the hypocotyls were cut off and put into a block of 1% MS agar so that the hypocotyls were embedded but the leaf blade was not in contact with the agar . These blocks were placed into Petri dishes and stored in a plant growth chamber under long day conditions for up to 72 hours . Each time before fluorescent images were obtained , leaves were stained in 200 µM propidium iodine for 5 min and washed with water . After image taking , the leaves were embedded to a new agar block and placed back into a Petri dish . Every 24 hours fluorescent images were taken by confocal laser-scanning microscopy using a 40x water-immersion objective without a cover slip . | Differentiating cells often amplify their nuclear DNA content through a special cell-cycle variant , called endoreplication , in which cell division is skipped . Although this process is widespread from humans to plants , not much is currently known about the biological importance of endoreplication . Moreover , the control of cell-cycle activities has been thought to follow developmental decisions and the adoption of a specific cell fate . Here we have uncovered a previously unrecognized function of endoreplication in maintaining cell identity , presenting a striking example of how cell fate and cell-cycle progression are linked . Using leaf hairs on the reference plant Arabidopsis as a model , we show that compromising endoreplication leads to dedifferentiation of the newly forming leaf hair cell . Live observations of young Arabidopsis leaves revealed that dedifferentiating leaf hairs underwent repeated rounds of cell division and were re-integrated into the epidermal cell layer acquiring the typical characteristics of the surrounding epidermal cells . Conversely , promoting endoreplication in mutants that fail to develop hairs could at least partially restore their differentiation program . With this , our findings also pinpoint an important role of the social context of a cell , revealing a differentiation control system at the tissue level . | [
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The impact of bacterial morphology on virulence and transmission attributes of pathogens is poorly understood . The prevalent enteric pathogen Campylobacter jejuni displays a helical shape postulated as important for colonization and host interactions . However , this had not previously been demonstrated experimentally . C . jejuni is thus a good organism for exploring the role of factors modulating helical morphology on pathogenesis . We identified an uncharacterized gene , designated pgp1 ( peptidoglycan peptidase 1 ) , in a calcofluor white-based screen to explore cell envelope properties important for C . jejuni virulence and stress survival . Bioinformatics showed that Pgp1 is conserved primarily in curved and helical bacteria . Deletion of pgp1 resulted in a striking , rod-shaped morphology , making pgp1 the first C . jejuni gene shown to be involved in maintenance of C . jejuni cell shape . Pgp1 contributes to key pathogenic and cell envelope phenotypes . In comparison to wild type , the rod-shaped pgp1 mutant was deficient in chick colonization by over three orders of magnitude and elicited enhanced secretion of the chemokine IL-8 in epithelial cell infections . Both the pgp1 mutant and a pgp1 overexpressing strain – which similarly produced straight or kinked cells – exhibited biofilm and motility defects . Detailed peptidoglycan analyses via HPLC and mass spectrometry , as well as Pgp1 enzyme assays , confirmed Pgp1 as a novel peptidoglycan DL-carboxypeptidase cleaving monomeric tripeptides to dipeptides . Peptidoglycan from the pgp1 mutant activated the host cell receptor Nod1 to a greater extent than did that of wild type . This work provides the first link between a C . jejuni gene and morphology , peptidoglycan biosynthesis , and key host- and transmission-related characteristics .
Campylobacter jejuni is a helical , highly motile , Gram-negative ε-Proteobacterium and a prevalent zoonotic organism existing asymptomatically in the intestinal tract of birds and other animal species [1]–[3] . However , ingestion of as few as 500 bacteria can result in human disease [4] . C . jejuni is the leading cause of foodborne gastroenteritis in the developed world , causing an acute self-limiting infection of varying severity that can give rise to severe complications such as inflammatory bowel disease , reactive arthritis , and Guillain-Barré syndrome ( GBS ) [5] . C . jejuni lacks many of the frequently identified virulence factors encoded by other enteric pathogens such as pili , enterotoxins , and specialized secretion mechanisms [6] , [7] . Genes affecting fundamental aspects of C . jejuni biology in hosts other than humans , such as stress survival , transmission , and asymptomatic colonization , also affect virulence in disease models . We found that C . jejuni strain 81-176 binds calcofluor white ( CFW ) , a compound that reacts with β1–3 and β1–4 carbohydrate linkages and fluoresces under long wave UV light [8] , [9] . The carbohydrate responsible for CFW reactivity in C . jejuni has not yet been identified , although it was previously shown not to be one of the well-characterized surface polysaccharides expressed by C . jejuni: the capsular polysaccharide , lipooligosaccharide ( LOS ) , N-linked glycoproteins , or O-linked flagellar glycoproteins [10] . C . jejuni mutants with altered CFW reactivity can be readily identified in screens . All C . jejuni CFW hyper- or hyporeactive mutants characterized to date exhibit changes in pathogenesis , virulence , fundamental , and/or stress survival phenotypes ( [10] , [11]; E . Frirdich and E . C . Gaynor , unpublished ) . For instance , CFW hyper-reactive mutants overproduce biofilms , while hypo-reactive mutants are biofilm-defective . Other attributes associated with altered CFW reactivity have included defects in colonization , host cell interactions , cell envelope components , and stress survival . A CFW hypofluorescent mutant with a lesion in a novel peptidoglycan peptidase gene ( pgp1 ) serves as the basis of this study . The helical shape of C . jejuni has long been postulated to be critical for pathogenic attributes such as the ability to burrow through the mucosal layer during infection of zoonotic and human hosts . However , genetic components involved in modulating C . jejuni morphology had not previously been identified . Morphology is maintained in most bacteria by the peptidoglycan ( PG ) layer [12] . PG is composed of glycan chains consisting of β1–4 linked N-acetylglucosamine ( GlcNAc ) and N-acetylmuramic acid ( MurNAc ) residues that are cross-linked by short peptides . PG synthesis is a highly regulated process [13] , [14] taking place at the bacterial inner membrane , where lipid II precursors are polymerized by glycosyltransferase and DD-transpeptidase reactions . In addition to the synthetic enzymes , PG hydrolases are required to cleave bonds in PG or PG fragments for insertion of nascent PG into the mature layer , regulation of cell wall growth , cell separation , PG turnover and recycling , cell lysis , and the release of PG fragments in host-pathogen interactions [15] . Almost every glycosidic and amide bond in PG can be cleaved by one or more specific PG hydrolases , and this redundancy makes it difficult to assign a specific function to a hydrolase [15] , [16] . It has been speculated that murein synthases and hydrolases are part of a multienzyme complex for PG assembly [16] , and several interactions between PG enzymes have been reported [14] . Campylobacter PG contains meso-diaminopimelic acid ( meso-Dap ) in its peptide side chains and is modified by O-acetylation [17] , [18] , but the detailed muropeptide composition had not previously been elucidated . Bioinformatic analyses identified 3 putative PG synthases in C . jejuni: the penicillin-binding proteins ( PBPs ) PBP1A ( cjj81176_0536 ) , PBP ( cjj81176_0550 ) , and PBP2 ( cjj81176_0680 ) , but there are no predicted homologs of low-molecular weight PBPs such as DD-endo- or carboxypeptidases . C . jejuni does have homologs of the Csd1 and CcmA endopeptidases and of the Csd3/HdpA endo/carboxypeptidase discovered recently in Helicobacter pylori [19] , [20] . Deletion of each of these genes in H . pylori resulted in curved-rod morphologies with reduced levels of PG cross-linking [19] , but preliminary evidence indicates that these enzymes may have a different function in C . jejuni ( E . Frirdich and E . C . Gaynor , unpublished ) . Here , we report the first C . jejuni gene , pgp1 , to be identified with a role in helical morphology . Structural PG analyses of C . jejuni wild-type 81-176 and Δpgp1 mutant strains showed striking alterations in the mutant , with enzyme assays confirming Pgp1 as a novel DL-carboxypeptidase . Furthermore , using the Δpgp1 mutant with a straight morphology , the importance of C . jejuni helical shape and PG in pathogenesis attributes was examined; we identified roles in motility , biofilm formation , chick colonization , and stimulation of host cell proinflammatory mediators Nod1 and IL-8 .
As noted , screening for C . jejuni hypofluorescent mutants was previously shown to identify genes involved in pathogenesis-related phenotypes [10] . However , as the Tn7 transposon ( Tn ) used previously for mutant library construction was reproducibly found to have inserted in intergenic regions ( M . K . McLennan and E . C . Gaynor , unpublished ) , a new random Tn library was generated as part of this study , using the highly efficient mariner system of in vitro Tn mutagenesis developed for C . jejuni [21] . The mariner library was plated onto CFW-containing plates and , from approximately 10 , 000 colonies screened , 400 hypofluorescent mutants were isolated . Of the Tn insertions mapped , 8 were in distinct regions of the gene cjj81176_1344 ( Figure 1; the CFW hypofluorescent phenotype is described below ) . The 1344 gene was named pgp1 ( peptidoglycan peptidase 1 ) to describe its function and identification as the first C . jejuni PG peptidase to be characterized . The pgp1 gene product is highly conserved in mainly helical and vibrioid bacteria , primarily within the ε- and δ-Proteobacteria but also in a few extremophiles outside the ε-Proteobacteria ( Table S1 ) . The H . pylori homolog is described by Sycuro et al . [22] . Pgp1 was annotated as a putative periplasmic protein . However , bioinformatics using conserved domain searches and the threading program PHYRE identified an N-terminal signal peptide , a zinc binding site , and metallocarboxypeptidase catalytic residues and folds . Pgp1 contains a conserved domain at its N-terminus similar to that of the M14 family of metallocarboxypeptidases . To explore functional consequences of loss of Pgp1 function in C . jejuni , a non-polar pgp1 targeted deletion strain was constructed ( designated Δpgp1 above the gene cluster in Figure 1 ) . To verify lack of polar effects on the downstream gene , an insertional mutation was also created in 1343 . The Δ1343 mutant exhibited wild-type phenotypes for shape , motility , biofilm formation and CFW reactivity ( data not shown; these phenotypes for Δpgp1 are described below ) . The level of 1343 mRNA was also determined to be identical in Δpgp1 and in the wild-type strains by semi-quantitative RT-PCR ( data not shown ) The pgp1 gene is located in the middle of a putative operon , and the location of the pgp1 promoter is unclear from sequence analysis ( Figure 1 ) . Therefore , complementation was first attempted by expressing the pgp1 gene from the cat promoter of the vector pRRC ( CmR ) [23] integrated into an rRNA spacer region of Δpgp1 . This construct contained a similar region of pgp1 as shown for pEF20 in Figure 1 ( pEF20 will be described further below ) and did not complement Δpgp1 ( data not shown ) . However , integration of this same construct into the C . jejuni wild type strain also resulted in morphological and phenotypic alterations ( described below ) , suggesting that pgp1 copy number was likely important for complementation , and that the level of pgp1 expression from the cat promoter was higher than optimal . To generate a complementing strain expressing pgp1 from its native promoter , increasing amounts of the region upstream of pgp1 were cloned with pgp1 into pRRC . These regions were cloned in the opposite direction as cat ( to avoid overexpression from the cat promoter ) and integrated into an rRNA spacer region of Δpgp1 . These constructs yielded varying degrees of complementation , with the pEF35R ( Figure 1 ) construct complementing nearly all Δpgp1 phenotypes to wild-type levels . The complemented strain was verified by PCR and designated Δpgp1c . Since the pgp1 gene product is highly conserved in helical and vibrioid bacteria , the morphology of the Δpgp1 mutant was examined . The Δpgp1 mutant displayed a striking change compared to wild type and had lost the characteristic helical shape ( Figure 2A ) , adopting a straight morphology ( Figure 2B ) . Complementation restored the helical shape in approximately 95% of the cells ( Figure 2C ) . Consistent with the phenotype of the mariner insertion mutants , the Δpgp1 mutant was hyporeactive to CFW , with wild-type reactivity restored by complementation ( Figure 2D ) . The Δpgp1 mutant displayed no obvious flagellar structural defects ( Figure 2B ) , but did exhibit a slight motility defect after incubation for 20 h on 0 . 4% agar plates , producing halos that were , on average , about 82 . 5% of wild type ( Figure 2E ) . Motility was restored to wild-type levels in the complemented strain . Biofilm levels , as assessed by a crystal violet assay , were approximately 1 . 2- , 1 . 5- and 1 . 6-fold lower in the Δpgp1 mutant compared to wild type at days 1 , 2 , and 3 , respectively . Biofilm production was partially restored in Δpgp1c ( Figure 2F ) . No differences between wild type and Δpgp1 were observed for growth , stress survival , capsule and LOS migration on acrylamide gels , membrane protein composition , and sensitivity to antimicrobial compounds ( Table S2 ) . To assess the effects of pgp1 overexpression , pgp1 was expressed from either the cat or aphA-3 promoter in pRRC ( data not shown ) or pRRK ( pEF20 in Figure 1 ) , respectively , and integrated at an rRNA spacer region of wild-type C . jejuni . Both pRRC and pRRK derivatives had the same effects on wild type , so only the results for pEF20 are shown . The pgp1 overexpressing strain displayed an altered cell shape in approximately 50% of the population , producing kinked and straight cells among the helical cells ( Figure 3A ) . This strain also exhibited reduced motility and a defect in biofilm formation ( Figure 3B & C ) . Reverse transcriptase-quantitative PCR ( RT-qPCR ) confirmed that the levels of pgp1 mRNA were 5 . 1-fold higher in the overexpressing strain than in wild type . In addition , there was a 1 . 2-fold increase in pgp1 mRNA levels in the complementing strain Δpgp1c in comparison to wild type , which may explain the partial complementation of some phenotypes . Expression of pgp1 in E . coli had no effect on cell shape ( data not shown ) .
Bacterial cell shape programs and their impact on biology are becoming increasingly well-characterized for model organisms like E . coli and Caulobacter crescentus [30] , [31] . However , studies linking bacterial shape and pathogenesis are in their infancy . This is the first report to address the morphogenesis program of the prevalent foodborne pathogen C . jejuni . Furthermore , apart from three annotated uncharacterized PBPs , no other C . jejuni PG-modifying enzymes have been identified . This is in spite of the fact that , as our study demonstrates , C . jejuni produces numerous muropeptide species indicative of additional unidentified and potentially novel PG hydrolases . Our identification and characterization of pgp1 provides the first link between cell shape and PG in C . jejuni . Pgp1 is conserved primarily in bacteria with curvature ( Table S1 ) . Although functions have not yet been ascribed for other putative shape-related proteins in C . jejuni , it is of note that helical ε- and δ-proteobacteria also typically contain the elongase and cytoskeletal components MreB , MreC , and RodA , but not MreD or RodZ . As such , unique PG and morphogenesis programs are likely to be identified both within and among these organisms , which in turn will lend new insight into their biology and pathogenesis . Enzyme assays demonstrated that Pgp1 is a metal-dependent DL-carboxypeptidase cleaving monomeric disaccharide tripeptides to disaccharide dipeptides . This is consistent with Pgp1 harboring an N-terminal M14 metallocarboxypeptidase domain present in two other PG dipeptidyl peptidases , E . coli MpaA and Bacillus sphaericus ENP1 , which cleave tri- and tetrapeptides to dipeptides [32]–[35] . This also correlates with the reduction in dipeptides and increase in tripeptides in Δpgp1 PG . Decreased tetrapeptides in Δpgp1 may be an indirect consequence of the increased level of tripeptides affecting activity of DD-carboxypeptidases , or an indirect effect on other PG hydrolases via disruption of a putative enzyme complex . Efforts to identify Pgp1 binding partners that would support the latter hypothesis are underway . Our muropeptide analysis of C . jejuni 81-176 wild-type PG also revealed a muropeptide composition expected for a Gram-negative species , with several interesting differences from the related helical organism H . pylori [19] , [36] and the rod-shaped E . coli [14] , [24] ( Table S5 ) . For instance , unlike H . pylori , C . jejuni contains very low levels of pentapeptides , suggesting high DD-carboxypeptidase activity . C . jejuni PG also has a higher level of cross-linking than either other species , and a relatively short average glycan chain length . With pgp1 being the first C . jejuni gene directly linked with helical morphology , it allowed for the first direct comparison between a helical wild type strain and an isogenic targeted rod-shaped mutant in critical aspects of C . jejuni pathogenesis and survival . The modest motility defect of the rod-shaped Δpgp1 mutant may in part account for the modest biofilm defect [37]–[39] . Further corroborating the pgp1-biofilm connection are our observations that pgp1 is up-regulated in the hyperbiofilm-forming ΔcprS strain ( S . Svensson and E . C . Gaynor , unpublished ) and that Δpgp1 is hyporeactive to CFW [10]; the latter observation in turn continues to support a link between C . jejuni CFW reactivity and key biological and pathogenic processes . Isolated PG , which has a β1–4 disaccharide backbone , binds to CFW ( E . Frirdich and E . C . Gaynor , unpublished ) , suggesting that changes in PG architecture in Δpgp1 may account for decreased CFW reactivity . However , as PG isolated from both wild type and mutant strains bound CFW equally well , a related explanation is that PG alterations affect the accessibility of CFW to binding sites on periplasmic PG molecules . The Δpgp1 mutant also allowed us to explore roles for shape and PG composition on host interactions . Surprisingly , the Δpgp1 mutant was not defective for host cell attachment or invasion in vitro and even displayed a slight increase in short-term intracellular survival . Increasing the viscosity of the media did not affect the ability of the mutant to invade epithelial cells ( Figure S2 ) . It is of note that although attachment and invasion to host cells increased for some C . jejuni strains in higher viscosity medium [28] , this was not the case for our highly invasive wild-type strain 81-176 , suggesting varying invasion programs among different C . jejuni strains . Intracellular C . jejuni was previously shown to induce host epithelial cell inflammatory responses via activation of the cytoplasmic Nod receptors [40] , [41] , which recognize distinct PG muropeptide molecules . The minimal molecule recognized by hNod2 is muramyl dipeptide ( MDP ) , a structure common to PG from both Gram-negative and Gram–positive organisms [42] . hNod1 and mNod1 recognize DAP-containing muropeptides restricted to Gram-negative organisms . hNod1 exhibits a preference for tripeptides and mNod1 for tetrapeptides; however mNod1 also , to a lesser degree , recognizes DAP-containing tripeptides ( [43]; J . Lee and S . Girardin , unpublished ) . Luciferase assays are a sensitive method to probe the capacity of a PG preparation to trigger Nod1 or Nod2 and provide complementary information to PG compositional data by HPLC and MS analyses . Using these assays , only mNod1 exhibited a statistically significant difference in activation by Δpgp1 PG compared to wild type . It is possible that C . jejuni modifies its PG in a manner that affects Nod signaling , as modifications of the PG backbone have been shown to affect how PG fragments are sensed by host cell receptors [44] . This poses an interesting question for future work . Our data also indicate that wild-type PG can activate hNod2 , consistent with findings by Al-Sayeqh et al . ( 2010 ) who also showed NF-κB activation in HEK293T cells transfected with Nod2 and infected with live C . jejuni . While Zilbauer et al . ( 2007 ) did not observe C . jejuni activation of Nod2 , this may reflect differences between cell lines used in these assays , and/or differences in reporter sensitivity . Nod activation is an important aspect of innate immunity against C . jejuni [40] , [41]; however , the mechanism by which C . jejuni PG fragments may reach the cytosol to activate Nod1 and/or Nod2 has not been elucidated . C . jejuni can be internalized into intestinal epithelial cells , surviving within an intracellular membrane-bound compartment known as the C . jejuni-containing vacuole ( CCV ) [45] . An oligopeptide transporter expressed in the early endosome has been implicated in the transport of Nod ligands from the endosome to the cytoplasm in HEK293T cells [46]; a similar system could allow cytoplasmic delivery of PG fragments from C . jejuni in the CCV . Another possibility is that during growth in the extracellular environment C . jejuni may shed part of its PG , as shown for other bacteria [44] , [47] , [48]; released Nod1 and Nod2-stimulatory molecules could then be transported to the cytoplasm by oligopeptide transporters or taken up by phagocytosis or clathrin-dependent endocytosis and transported to the cytosol to activate Nods [44] , [49]–[51] . Bacterial outer membrane vesicles may also play a role in delivering C . jejuni Nod ligands to the cytoplasm [52] . Production of IL-8 and other proinflammatory mediators by intestinal epithelial cells infected with C . jejuni is thought to be key to the development of diarrhea and clearance of infection . Zilbauer et al . ( 2007 ) suggested that C . jejuni Nod1 activation is the primary signaling event required for IL-8 expression in Caco-2 intestinal epithelial cells . However , other work indicates that C . jejuni-induced IL-8 secretion can be triggered by other pathways in addition to the activation of Nod1 , such as through Toll-like receptors ( TLRs ) and a pathway independent of Nods and TLRs that has yet to be identified [40] , [53]–[58] . Thus while the increase in IL-8 secretion in response to Δpgp1 may be due to the modest increase in hNod1 activation observed in our luciferase assays , it cannot be ruled out that deletion of pgp1 leads to a change in another , as-yet unidentified factor stimulating IL-8 expression . Future work is planned to address these hypotheses . Our colonization data are in agreement with the longstanding hypothesis that the corkscrew morphology of C . jejuni is critical for burrowing into the mucus layer; however , the impact of shape and PG on colonization may be multifactorial . For instance , motility is a key factor in colonization [59]–[64] , thus the Δpgp1 colonization defect may in part be due to its decreased motility . However , a ΔcarB mutant with a similar motility defect was not deficient for chick colonization ( Figure S1 ) . When the strongly colonizing C . jejuni strain 305/94 was re-isolated from chickens , it displayed a more pronounced motility defect than Δpgp1 despite having normal flagella , and had lost its helical shape , exhibiting a similar straight morphology as Δpgp1 [65] . It is intriguing to hypothesize that not only shape , but also the underlying structure of the PG may play an important role in colonization . We have found that a straight phenotype can arise from various changes in muropeptides based on preliminary analysis of other straight mutants identified in our laboratory ( E . Frirdich , J . Vermeulen , and E . C . Gaynor , unpublished ) . PG changes or changes resulting from the loss of pgp1 could cause as-yet uncharacterized alterations to the cell surface affecting the ability of C . jejuni to survive in the chicken cecum , or affect host cell interactions and stimulation of innate immune receptors . Our understanding of the chicken innate immune system and how it responds to C . jejuni is in its infancy . The chicken genome encodes Nod1 but not Nod2 , although chickens possess an ortholog of the NLRP3/NALP3 Nod-like receptor that is similar to Nod2 and also binds MDP [66] , [67] . NLRP3 may substitute for Nod2 in chickens . Further insight into mechanisms by which C . jejuni survives commensally in the chicken cecum requires additional studies of these and other innate immune system factors . Identification and characterization of pgp1 provides a critical first step in understanding how shape and PG modifications impact C . jejuni pathogenesis . However , the shape program itself in C . jejuni is likely to be somewhat complicated . For instance , to the best of our knowledge , Pgp1 is the only PG modifying enzyme to date where overexpression causes cell straightening , indicating that the precise dose of Pgp1 is important for maintenance of morphology . One interpretation of this finding is that the proper ratio of monomeric tripeptides to dipeptides , which is disrupted in both the Δpgp1 mutant and pgp1-overexpressing strains , may be required for proper shape determination . It is also possible that excess Pgp1 could , as suggested for loss of Pgp1 above , indirectly affect other PG hydrolases by disrupting the stoichiometry of a putative PG biosynthetic/modification complex . Additional complexities in the shape program are evidenced by reports of straight Campylobacter strains in the literature for which the morphological change has not yet been attributed to mutations in specific gene product ( s ) . For instance , some C . jejuni flagellar mutants appeared to have a straight morphology [68]–[70]; however , other mutants with lesions in the same flagellar genes remained helical ( [71] , [72]; E . Frirdich and E . C . Gaynor , unpublished ) . Two reports also cited that passage through the chicken gut or chick embryos resulted in straight C . jejuni isolates [65] , [73] . Conversely , laboratory passage has led to the isolation of poorly colonizing rod-shaped C . jejuni and C . coli strains [74] , [75] . In the 11168-GS ( straight ) and 11168-O ( helical ) strains of the 2004 study , sequence and expression levels of pgp1 were found to be identical ( E . Frirdich and E . C . Gaynor , unpublished ) . Collectively , this suggests not only that genes other than pgp1 affecting morphology remain to be discovered , but also that mechanisms such as phase variation and/or epigenetics may be involved . The pgp1 homolog in H . pylori ( named csd4 ) was identified in a screen for cell shape mutants and is described by Sycuro et al . [22] . Morphology , PG profile , and enzymatic activity indicate conserved functions for the gene products in both organisms . While some enzymes and their effects on morphology are conserved between these related helical bacteria , there are also likely to be differences in the overall PG remodeling and shape determining programs , particularly given their very different wild-type muropeptide profiles . The Δpgp1 mutant will be a valuable tool to continue to study the effects of the loss of C . jejuni helical shape on its biology and pathogenesis . So far , pgp1 has been found to be important for all aspects of the C . jejuni life cycle . As this study represents the first identification of a gene involved in C . jejuni helical shape and of a role for PG in shape determination , it will also provide the basis for work characterizing additional enzymes involved in C . jejuni PG biosynthesis and shape determination . These studies have now been made easier by the availability of PG structural data for the wild-type C . jejuni strain 81-176 published as part of this work . Future detailed biochemical and structural studies on Pgp1 will also provide interesting insight into the function of this key protein in C . jejuni physiology .
Animal experiments were carried out in strict accordance with the University of Michigan Committee on Use and Care of Animals ( UCUCA ) . Animal infection and euthanasia protocols were approved by the University of Michigan UCUCA and assigned approval number 10462 . Oral gavage was carried out under humane guidelines using an approved protocol judged not to cause distress or harm to the animals . Euthanasia was carried out under humane guidelines using a lethal dose of isofluorane . All animal use procedures are in compliance with University guidelines , State and Federal regulations , and the standards of the National Institutes of Health Guide for the Care and Use of Laboratory Animals . The University of Michigan Animal Welfare Assurance Number on file with the NIH Office of Laboratory Animal Welfare ( OLAW ) is A3114-01 , and the University is fully accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International ( AAALAC , Intl . ) . Bacterial strains and plasmids used in this study and their construction are described in Text S1 . C . jejuni strains were grown at 38°C in Mueller-Hinton ( MH; Oxoid ) broth or 8 . 5% ( w/v ) agar supplemented with vancomycin ( 10 µg/mL ) and trimethoprim ( 5 µg/mL ) ( unless otherwise indicated ) under microaerobic/capnophilic conditions ( 6% O2 , 12% CO2 ) in a Sanyo tri-gas incubator for plates or using the Oxoid CampyGen system for broth cultures . Growth media were supplemented with chloramphenicol ( Cm; 20 µg/mL ) or kanamycin ( Km; 50 µg/mL ) , where appropriate . E . coli strains used for plasmid construction were grown at 37°C in Luria-Bertani ( LB; Sigma ) broth or 7 . 5% agar ( w/v ) and supplemented with ampicillin ( 100 µg/mL ) , Cm ( 15 µg/mL ) , or Km ( 25 µg/mL ) , as necessary . RNA was extracted from log phase broth bacteria ( OD 0 . 3 ) , and cDNA was generated from the RNA , as described previously [74] , [76] . The expression ratio of pgp1 was determined relative to the C . jejuni gyrA gene encoding DNA gyrase subunit A . The primer and TaqMan probe sequences used for qPCR are described in Text S1 . Each qPCR reaction mixture consisted of 25 ng of cDNA , 950 nM of each primer , 250 nM TaqMan probe , and 10 µl 2× TaqMan Gene Expression Master Mix ( Applied Biosystems ) in a total volume of 20 µl . Duplicate reactions for the gene of interest and the housekeeping gene were run in a Stratagene Mx3000P real-time PCR system for 2 min at 60°C , 10 min at 95°C , and then 40 cycles of 15 s at 95°C and 1 min at 60°C . The quantitative PCR cycle threshold ( CT ) results were analyzed by the comparative CT method ( ΔΔCT method ) . The C . jejni 81-176::solo ( KmR ) and C . jejuni 81-176::picard ( CmR ) Tn libraries ( Tn library construction is described in Text S1 ) and were screened on CFW , as described previously [10] . Transmission electron microscopy ( TEM ) was carried out on 18 h broth cultures . Samples were fixed in a final concentration of 2 . 5% ( v/v ) of gluteraldehyde for 2–3 h on ice . Cells were then harvested , resuspended in an equal volume of H2O , and stored at 4°C . For imaging , 2 µL of bacteria was spotted onto parafilm to which 4 µL of 0 . 5% uranyl acetate was added for 1 min . A formavar-carbon film on 300 mesh copper grid ( Canemco , Lakefield , Quebec , Canada ) was added to the bacteria-uranyl acetate spot for 2 min . The grid was then removed , dried , washed ten times in sterile water , dried again and visualized on a Hitachi H7600 TEM equipped with a side mount AMT Advantage ( 1 mega-pixel ) CCD camera ( Hamamatsu ORCA ) at the UBC Bioimaging facility ( The University of British Columbia , Vancouver , BC , Canada ) . Phenotypic assays were carried out with strains grown in shaking MH-TV broth or biphasic cultures grown for 18 h . CFW fluorescence was assayed as described previously [10] . For motility , cultures were diluted to an OD600 of 0 . 2 in MH-TV and 2 µl was point inoculated into MH-TV plates containing 0 . 4% agar . Plates were incubated for 20 h and the halo diameter was measured . Biofilm formation was assayed using crystal violet as described previously [11] with the exception that the absorbance was measured at 570 nm . C . jejuni strains were passaged once from frozen stocks and then passaged to 20–25 MH plates and grown for 20 h to obtain log-phase bacteria at a final OD of 200–600 . Cells were collected into cold MH broth by scraping , harvested by centrifugation at 8 000× g for 15 min and then resuspended in 6 mL ice cold H2O . Cells were lysed by dropwise addition to 6 mL 8% SDS boiling under reflux . PG was purified from the cell lysate , digested with the muramidase cellosyl ( kindly provided by Hoechst , Frankfurt , Germany ) , and the resulting muropeptides were reduced with sodium borohydride and separated by HPLC as described [77] . Muropeptide fractions were collected , concentrated in a SpeedVac , acidified by 1% trifluoroacetic acid , and analysed by offline electrospray mass spectrometry on a Finnigan LTQ-FT mass spectrometer ( ThermoElectron , Bremen , Germany ) at the Newcastle University Pinnacle facility as described [25] . Muropeptide structures were assigned based on ( i ) comparison with retention times of known muropeptides from H . pylori , Caulobacter crescentus and E . coli and ( ii ) the obtained MS data ( Table S3 ) and MS/MS fragmentation patterns ( not shown ) . The C . jejuni 81-176 pgp1 gene was cloned for expression in E . coli without its signal peptide ( amino acids 16–464 of the protein ) in frame with the thioredoxin- and His-tag of the pET32a vector to give plasmid pEF46 . A detailed description of the cloning of the expression construct , and the expression and purification protocol is included in Text S1 . For enzyme assays , the purified protein was dialysed against 0 . 05 M Tris-Cl , pH 7 . 5 containing 0 . 01 M ZnCl2 , 0 . 3 M NaCl and 20% glycerol . Purified Δpgp1 PG ( 1 . 0 mg/ml ) was incubated with Trx-His6-Pgp1 ( 5 mM ) in 0 . 02 M NaH2PO4 , pH 4 . 8 , 0 . 005 M ZnCl2 , and 0 . 1 M NaCl for 4 h at 37°C on a Thermomixer at 750 rpm . A control sample received no enzyme and another enzyme sample contained 0 . 01 M EDTA and no ZnCl2 . The samples were incubated with 10 µg of cellosyl ( Hoechst , Frankfurt am Main , Germany ) for 1 h , boiled for 10 min and centrifuged at room temperature for 15 min at 16 000 g . The muropeptides present in the supernatant were reduced with sodium borohydride and analyzed by HPLC , as described [24] . Chick colonization was performed as described previously [61] , [76] , with an infective dose of 104 CFU . Chicken experiments were carried out under protocol #10462 approved by the University of Michigan Committee on Care and Use of Animals ( UCUCA ) . The human epithelial cell lines T84 , Caco-2 and INT407 and the murine RAW 264 . 7 and human Thp-1 macrophage cell lines were used for C . jejuni infections . Carboxymethylcellulose ( CMC ) was added to the tissue culture media to increase viscosity . A detailed description of the tissue culture infections is included in Text S1 . Luciferase asssays were performed as previously described [46] . Briefly , HEK293T cells were transfected overnight with 75 ng of NF-κB luciferase reporter plasmid ( Igκ-luc , Invitrogen ) and either human Nod1 ( hNod1 , 3 ng ) , mouse Nod1 ( mNod1 , 0 . 1 ng ) , or human Nod2 ( hNod2 , 0 . 1 ng ) . The empty vector ( pcDNA3 . 1 , Invitrogen ) was used to balance the transfected DNA concentration . At the same time , either C . jejuni 81-176 or Δpgp1 PG muropeptides at 0 . 1 µg/mL were added , and the NF-κB-dependent luciferase activation was then measured following 18–24 h of co-incubation . Positive controls were TriDAP ( tripeptide l-Ala-γ-d-Glu-meso-DAP , 5 µg/ml ) , FK565 ( a synthetic immunostimulant tetrapeptide , heptanoyl-d-glutamyl-meso-DAP-d-alanine , 5 ug/ml ) , and MDP ( muramyldipeptide , 10 µg/ml ) for hNod1 , mNod1 , and Nod2 assays , respectively . Data are representative of three independent experiments . The INT407 human epithelial cell line was seeded at approximately 1×105 cells/ml in MEM supplemented with 10% FBS into 24-well tissue culture plates and allowed to grow for 20–24 h prior to infection . The cells were washed three times with MEM and either left uninfected or infected with either C . jejuni wild-type strain 81-176 or Δpgp1 at an O . D . 600 of 0 . 002/mL taken from an 18 h shaking broth culture . Supernatants were collected after 8 h and 24 h , centrifuged for 10 min to pellet residual cells and bacteria , and frozen at −80°C until assayed . The concentration of IL-8 present in the supernatants was measured by the human IL-8 ELISA kit ( Invitrogen , Camarillo , CA ) . | Bacterial cell shape is dictated by the composition of the cell envelope component peptidoglycan . Some important pathogens have a characteristic helical corkscrew morphology that may help them burrow into mucus overlaying cells to initiate colonization and pathogenicity . One example is Campylobacter jejuni , the leading cause of bacterial-induced diarrheal disease in the developed world . Direct evidence supporting the hypothesis that C . jejuni shape is related to its pathogenicity traits has not previously been provided . We identified a gene encoding a peptidase modifying peptidoglycan that is essential for maintaining the C . jejuni corkscrew shape . We can now connect a C . jejuni gene with morphology and peptidoglycan biosynthesis . Loss of this gene was also found to affect pathogenic attributes such as chicken colonization , biofilms , motility , and activation of host inflammatory mediators . In addition , this is the first study to thoroughly characterize C . jejuni peptidoglycan structure and to identify a gene involved in peptidoglycan maintenance . Our findings highlight an emerging theme in bacterial pathogenesis research: the connection between bacterial cell biology and pathogenesis . Finally , our characterization of C . jejuni cell shape and peptidoglycan provides a starting point for further work in this area in C . jejuni and other bacteria with curved and helical morphologies . | [
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] | 2012 | Peptidoglycan-Modifying Enzyme Pgp1 Is Required for Helical Cell Shape and Pathogenicity Traits in Campylobacter jejuni |
Identifying sources of variation in DNA methylation levels is important for understanding gene regulation . Recently , bisulfite sequencing has become a popular tool for investigating DNA methylation levels . However , modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples , and because of the computational challenges of controlling for genetic covariance in count data . To address these challenges , we present a binomial mixed model and an efficient , sampling-based algorithm ( MACAU: Mixed model association for count data via data augmentation ) for approximate parameter estimation and p-value computation . This framework allows us to simultaneously account for both the over-dispersed , count-based nature of bisulfite sequencing data , as well as genetic relatedness among individuals . Using simulations and two real data sets ( whole genome bisulfite sequencing ( WGBS ) data from Arabidopsis thaliana and reduced representation bisulfite sequencing ( RRBS ) data from baboons ) , we show that our method provides well-calibrated test statistics in the presence of population structure . Further , it improves power to detect differentially methylated sites: in the RRBS data set , MACAU detected 1 . 6-fold more age-associated CpG sites than a beta-binomial model ( the next best approach ) . Changes in these sites are consistent with known age-related shifts in DNA methylation levels , and are enriched near genes that are differentially expressed with age in the same population . Taken together , our results indicate that MACAU is an efficient , effective tool for analyzing bisulfite sequencing data , with particular salience to analyses of structured populations . MACAU is freely available at www . xzlab . org/software . html .
DNA methylation—the covalent addition of methyl groups to cytosine bases—is a major epigenetic gene regulatory mechanism observed in a wide variety of species . DNA methylation influences genome-wide gene expression patterns , is involved in genomic imprinting and X-inactivation , and functions to suppress the activity of transposable elements [1–3] . In addition , DNA methylation is essential for normal development . For example , mutant Arabidopsis plants with reduced levels of DNA methylation display a range of abnormalities including reduced overall size , altered leaf size and shape , and reduced fertility [4–6] . In humans , DNA methylation levels are strongly linked to disease , including major public health burdens such as diabetes [7 , 8] , Alzheimer’s disease [9 , 10] , and many forms of cancer [7 , 11–15] . Together , these observations point to a central role for DNA methylation in shaping genome architecture , influencing development , and driving trait variation . Consequently , there is substantial interest in identifying the genetic [16–19] and environmental [20–23] factors that shape DNA methylation levels . Progress toward this goal requires statistical approaches that can handle the complexities of real world , population-based datasets . Here , we present one such approach , designed specifically for analyses of differential methylation levels in bisulfite sequencing datasets . High-throughput bisulfite sequencing approaches , which include whole genome bisulfite sequencing ( WGBS or BS-seq ) [24] , reduced representation bisulfite sequencing ( RRBS ) [25 , 26] , and sequence capture followed by bisulfite conversion [27 , 28] , are used to estimate genome-wide DNA methylation levels at base-pair resolution . All such methods rely on the differential sensitivity of methylated versus unmethylated cytosines to the chemical sodium bisulfite . Specifically , sodium bisulfite converts unmethylated cytosines to uracil ( and ultimately thymine following PCR ) , while methylated cytosines are protected from conversion . Estimates of DNA methylation levels for each cytosine base can thus be obtained directly from high-throughput sequencing data by comparing the number of C’s ( reflecting an originally methylated version of the base ) versus T’s ( reflecting an originally unmethylated version of the base ) at that position in the mapped reads . The raw data produced by bisulfite sequencing methods are therefore count data , in which both the number of methylated reads and the total coverage at a site contain useful information . Higher total coverage corresponds to a more reliable estimate of the true DNA methylation level , which , in a typical experiment , can vary dramatically across individuals and sites ( e . g . , by several orders of magnitude: S1 Fig ) . Many commonly used methods for testing for differential methylation ( whether by genotype , environmental predictor , or experimental perturbation ) ignore this variability by converting counts to percentages or proportions ( e . g . , t-tests , Mann-Whitney U tests , linear models , and all tools initially designed for array-based data [29 , 30]; Table 1 ) . Thus , a site at which 5 of 10 reads are designated as methylated ( i . e . , read as a cytosine ) is treated identically to a site at which 50 of 100 reads are designated as methylated . This assumption reduces the power to uncover true predictors of variation in DNA methylation levels , because it treats noisy measurements the same way as accurate ones . To address this problem , several recently introduced methods for differential DNA methylation analysis implement a beta-binomial model ( e . g . , ‘DSS: Dispersion Shrinkage for Sequencing data’ [31] , ‘RADMeth: Regression Analysis of Differential Methylation’ [33] , and ‘MOABS: Model Based Analysis of Bisulfite Sequencing data’ [32] ) . These methods model the binomial nature of bisulfite sequencing data , while taking into account the well-known problem of over-dispersion in sequencing reads . Because these methods work directly on count data , they can reliably account for variation in read coverage across sites and individuals . Consequently , beta-binomial methods consistently provide increased power to detect true associations between genetic or environmental sources of variance and DNA methylation levels [31–33] . However , methods based on beta-binomial models only account for over-dispersion due to independent variation , making them unsuited for data sets containing population structure or related individuals . Accounting for genetic relatedness is important because genetic variation can exert strong and pervasive effects on DNA methylation levels [17 , 19 , 38 , 39] . In humans , methylation levels at more than ten thousand CpG sites are influenced by local genetic variation [18] , and DNA methylation levels in whole blood are 18%-20% heritable on average , with the heritability estimates for the most heritable loci ( top 10% ) averaging around 68% [38 , 39] . As a result , DNA methylation levels will frequently covary with kinship or population structure , and failure to account for this covariance could lead to spurious associations or reduced power to detect true effects . This phenomenon has been extensively documented for genotype-phenotype association studies [35 , 36 , 40–42] , and controlling for genetic covariance between samples is now a basic requirement for genome-wide association studies . Similar logic applies to analyses of gene regulatory phenotypes and studies of gene expression variation often do take genetic structure into account by using mixed model approaches [43–45] . However , despite growing interest in environmental epigenetics and epigenome-wide association studies ( EWAS ) , none of the currently available count-based methods appropriately control for genetic effects on DNA methylation levels in bisulfite sequencing data ( Table 1 ) . Consequently , even though count-based methods have been shown to be more powerful , recent bisulfite sequencing studies have turned to linear mixed models to deal with the confounding effects of population structure [19 , 46] . To address this gap , we present a binomial mixed model ( BMM ) for identifying differentially methylated sites that directly models raw read counts while accounting for both covariance between samples and extra over-dispersion caused by independent noise . We also present an efficient , sampling-based inference algorithm to accompany this model , called MACAU ( Mixed model association for count data via data augmentation ) . MACAU works directly on binomially distributed count data from any high-throughput bisulfite sequencing method ( e . g . , WGBS , RRBS , targeted sequence capture ) and uses random effects to not only model over-dispersion ( as in the standard beta-binomial approach [47] ) , but also to model relatedness/population structure . Hence , MACAU enables users to identify differentially methylated sites in a wide variety of settings , with little cost to power even when genetic effects on DNA methylation levels are negligible . We compared MACAU’s performance with currently available methods under two realistic scenarios , using both real bisulfite sequencing data sets ( WGBS and RRBS ) and simulations parameterized based on properties of real data . In the first scenario , we analyzed publicly available data from Arabidopsis thaliana [48] to show that , when a predictor variable of interest is correlated with population structure , MACAU provides better control of type I error than existing methods . This setting is particularly relevant to understanding geographic variation in DNA methylation levels ( e . g . , [19 , 48–50] ) and for identifying genetic or environmental predictors of DNA methylation in structured samples ( e . g . , [50 , 51] ) . In the second scenario , we used newly generated RRBS data from wild baboons ( Papio cynocephalus ) to demonstrate that MACAU also provides increased power to detect truly differentially methylated sites in the presence of kinship—a condition that often holds in analyses of natural populations ( e . g . , [48 , 52 , 53] ) and in tests for epigenetic discordance between siblings [22 , 53–55] . As interest in epigenome-wide association studies ( EWAS ) , environmental epigenetics , and the epigenetic correlates of disease grows , these types of complex data sets will become increasingly common .
Here , we briefly describe the model and the algorithm . Additional information is provided in the S1 Text , which includes details on the model , inference method , and algorithm ( including descriptions of the data augmentation approach and efficient MCMC sampling steps ) . To detect differentially methylated sites , we model each potential target of DNA methylation individually ( i . e . , we model each CpG site one at a time ) as a function of x , a predictor variable of interest . Here , x could be a genotype value , as in methylation QTL mapping analyses; an environmental predictor of interest , such as temperature , chemical exposure , or social environment; an individual characteristic , such as age or sex; or an experimental perturbation , as in a treatment-control design . For each site , we consider the following binomial mixed model ( BMM ) : yi=Bin ( ri , πi ) , ( 1 ) where ri is the total read count for ith individual; yi is the methylated read count for that individual , constrained to be an integer value less than or equal to ri; and πi is an unknown parameter that represents the underlying proportion of methylated reads for the individual at the site . We use a logit link to model πi as a linear function of several parameters: log ( πi1−πi ) =wiTα+xiβ+gi+ei , ( 2 ) g= ( g1 , ⋯ , gn ) T∼MVN ( 0 , σ2h2K ) , ( 3 ) e= ( e1 , ⋯ , en ) T∼MVN ( 0 , σ2 ( 1−h2 ) I ) , ( 4 ) where , for a data set including c covariates and n individuals , wi is a c-vector of covariates including an intercept; α is a c-vector of corresponding coefficients; xi is the predictor of interest for individual i and β is its coefficient; g is an n-vector of genetic random effects that model correlation due to population structure or kinship; MVN denotes the multivariate normal distribution; e is an n-vector of environmental residual errors that model independent variation; K is a known n by n relatedness matrix that can be calculated based on pedigree or genotype data; I is an n by n identity matrix; σ2h2 is the genetic variance component; σ2 ( 1 − h2 ) is the environmental variance component; and h2 is the heritability of the logit transformed methylation proportion ( i . e . logit ( π ) ) . Note that K has been standardized to ensure tr ( K ) /n = 1 , so that h2 lies between 0 and 1 and can be interpreted as heritability ( see [56]; tr denotes the trace norm ) . Both g and e model over-dispersion ( i . e . , the increased variance in the data that is not explained by the binomial model ) . However , they model different aspects of over-dispersion: e models the variation that is due to independent environmental noise ( a known problem in data sets based on sequencing reads [57–60] , including analyses of read proportions [61] ) , while g models the variation that is explained by kinship or population structure . Effectively , our model improves and generalizes the beta-binomial model by introducing this extra g term to model individual relatedness due to population structure or stratification . In the absence of g , our model becomes similar to other beta-binomial models previously developed for modeling count data [31 , 33 , 47 , 62] . We are interested in testing the null hypothesis that the predictor of interest has no effect on DNA methylation levels:H0: β = 0 . This test requires obtaining the maximum likelihood estimate β^ from the model . Unlike its linear counterpart , estimating β^ from the binomial mixed model is notoriously difficult , as the joint likelihood consists of an n-dimensional integral that cannot be solved analytically [63 , 64] . Standard approaches rely on numerical integration [65] or Laplace approximation [66 , 67] , but neither strategy scales well with the increasing dimension of the integral , which in our case is equal to the sample size . Because of this problem , standard implementations of binomial mixed models often produce biased estimates and overly narrow ( i . e . , anti-conservative ) confidence intervals [68–72] . To overcome this problem , we instead use a Markov chain Monte Carlo ( MCMC ) algorithm-based approach for inference , using un-informative priors for the hyper-parameters h2 and σ2 . After drawing accurate posterior samples of β , we rely on the asymptotic normality of both the likelihood and the posterior distributions [73] to obtain the approximate maximum likelihood estimate β^ and its standard error se ( β^ ) . This procedure allows us to construct approximate Wald test statistics and p-values for hypothesis testing ( note that the p-values from our procedure diff from tail posterior probabilities usually used in purely Bayesian methods , and are more akin to p-values from frequentists tests; thus , they are not “improper” . ) Despite the stochastic nature of the procedure , the MCMC errors are small enough to ensure stable p-value computation across multiple MCMC runs ( S2 Fig ) . We note that with reasonably large sample sizes ( n = 50 or more ) , the resulting p-values are also robust to prior perturbation on hyper-parameters ( S3 Fig ) ; however , all results reported here are based on calculations with un-informative priors . In addition to the approximate inference procedure described above , we also developed a novel MCMC algorithm based on an auxiliary variable representation of the binomial distribution for efficient , approximate p-value computation [74–76] ( see S1 Text File Section 2: Inference Method Overview and S1 Text File Section 3 . 1: Data Augmentation for more details ) . We did so to reduce the heavy computational burden of standard MCMC algorithms , which would otherwise be prohibitive in terms of run time for large datasets . Building on the auxiliary variable representation , our main technical contribution is a new framework that approximates the distribution of the auxiliary variables ( S4 Fig and S1 and S2 Tables ) while simultaneously taking advantage of recent innovations for fitting mixed effects models [34 , 35 , 37 , 77] ( see S1 Text File Sections 3 . 2 and 3 . 3 ) . This framework reduces per-MCMC iteration computational complexity from cubic to quadratic with respect to the sample size , and results in an approximate n-fold speed up in practice compared with the popular Bayesian software MCMCglmm [78] , where n is the sample size ( S5 Fig and S3 Table; we note that this speed-up is generalizable to other GLMM problems as well ) . Our implementation of the BMM is therefore efficient for data sets ranging up to hundreds of samples and millions of sites , as computational complexity scales only linearly with respect to the number of analyzed sites ( S5 Fig ) . Because our model effectively includes the beta-binomial model as a special case , we expect it to perform similarly to the beta-binomial model in settings in which population structure is absent ( we say “effectively” because the beta-binomial model uses a beta distribution to model independent noise while we use a log-normal distribution ) . However , we expect our model to outperform the beta binomial in settings in which population structure is present . In addition , in the presence of population stratification , we expect the beta-binomial model to produce inflated test statistics ( thus increasing the false positive rate ) while our model should provide calibrated ones . Below , we test these predictions using two different bisulfite sequencing data sets . We begin with simulations in which the true value of β is known , and the over-dispersion parameter and genetic covariance between samples are motivated by the real data sets . We also motivate our choice of simulated sample sizes based on real bisulfite sequencing data sets , which currently range from ~20–150 samples [19 , 26 , 46 , 53 , 79–82] . However , because sample sizes are only likely to grow in the future , for the data set types of most direct interest ( i . e . , those that contain population structure and heritable DNA methylation levels ) we further consider sample sizes that are much larger than currently represented in the literature ( n = 500 and n = 1000 ) . Finally , we apply our model directly to the real data . We first compared the performance of the BMM implemented in MACAU with the performance of other currently available methods for analyzing bisulfite sequencing data in the absence of genetic effects . Intuitively , we expected MACAU and the beta-binomial model to perform similarly , and we expected both methods to outperform those that first transform the raw count data . To test our prediction , we simulated the effect of a predictor variable on DNA methylation levels across 5000 CpG sites ( 4500 true negatives and 500 true positives ) . Motivated by our analysis of age effects on DNA methylation levels in the baboon RRBS data set ( below ) , we conducted this simulation by sampling from a distribution of known age values from the same baboon population . For all simulations , we set the effect of genetic variation on DNA methylation levels equal to zero , which is equivalent to setting either ( i ) the heritability of DNA methylation levels to zero ( unlikely based on prior findings [38 , 39] ) , or ( ii ) studying completely unrelated individuals in the absence of population structure . To explore MACAU’s performance across a range of conditions , we simulated age effects on DNA methylation levels across three effect sizes ( percent of variance in DNA methylation explained ( PVE ) = 5% , 10% , or 15% ) and three sample sizes ( n = 20 , 50 , and 80 ) . These values capture the majority of effect sizes and sample sizes documented in recent genome-wide bisulfite sequencing studies ( e . g . , [45 , 52 , 53 , 83] ) . Because age is naturally modeled as a continuous variable , we focused our comparisons only on approaches that could accommodate continuous predictor variables ( comparisons in which we artificially binarized age , which allowed us to include a larger set of approaches , are shown in S6 Fig and S7 Fig for cases excluding and including genetic effects on DNA methylation , respectively; however , binarizing a truly continuous variable consistently results in poorer performance: see S6 Fig versus S9 Fig ) . Specifically , in addition to the BMM implemented in MACAU , we considered the performance of a beta-binomial model , a binomial model , a linear model , and a linear mixed model ( implemented in the software GEMMA [34] ) . For the linear and linear mixed model case , methylation proportions were quantile normalized to a standard normal prior to modeling ( see Methods and S8 Fig for parallel results using logit , M-value , and arcsin ( sqrt ) transformations prior to linear mixed modeling as alternatives to quantile normalization ) . As expected , we found that MACAU performed similarly to the beta-binomial model , and that these two approaches consistently detected more true positive age effects on DNA methylation levels ( at a 10% empirical FDR ) than all other methods ( S9 Fig ) . For example , in the “easiest” case we simulated ( PVE = 15% , n = 80 ) , we found that the beta-binomial model detected 30% of simulated true positives , while the BMM implemented in MACAU detected 27 . 8% . The slight loss of power in the BMM is a consequence of the smaller degrees of freedom caused by the additional genetic variance component . In comparison , the linear model detected 21 . 2% of true positives; the linear mixed effects model , 14%; and the binomial model , 8 . 4% ( S9 Fig ) . Although it is often used to test for differential methylation [53 , 84 , 85] , the binomial model exhibits low power when an empirical FDR is used to control for multiple hypothesis testing due to poor type I error calibration , as has been previously reported [33] . Area under a receiver operating characteristic curve ( AUC ) was also consistently very similar between the beta-binomial and MACAU ( S9 Fig ) , although the advantage of the count-based methods was less clear by this measure . This reduced contrast is because AUC is based on true positive-false positive trade-offs across the entire range of p-value thresholds: methods can consequently yield high AUCs even when they harbor little power to detect true positives at FDR thresholds that are frequently used in practice . Taken together , our simulations suggest a general advantage to count-based models for samples that contain no genetic structure . Further , the differences in performance between the beta-binomial model and the BMM implemented in MACAU were consistently small in this setting ( S9 Fig ) . We next evaluated each model’s performance in a more realistic setting , in which genetic covariance between samples could potentially confound tests for environmental or genetic effects on DNA methylation levels . As a case study example , we drew from publicly available phenotype data and SNP genotype data for 24 Arabidopsis thaliana accessions [86 , 87] in which leaf tissue samples had been recently subjected to whole genome bisulfite sequencing [48] . Among these accessions , a secondary dormancy phenotype ( measured as the slope of the relationship between length of cold treatment and seed germination percentages [88] ) is correlated with population structure ( R2 = 0 . 38 against the first principal component of the genotype matrix for these accessions; p = 7 . 84 x 10−4; S10 Fig ) . Because secondary dormancy is associated with environmental conditions that are experienced after the seed has already dispersed , we have no expectation that secondary dormancy should be associated with DNA methylation levels in leaf tissue . Consequently , this data set provided the opportunity to evaluate calibration of Type I error ( false positives ) using MACAU , which controls for population structure , versus other available approaches . To do so , we first used the true distribution of secondary dormancy characteristics and the true genetic structure among these 24 accessions to simulate a dataset that consisted entirely of null associations . Specifically , we simulated data sets ( containing 4000 sites each ) in which the secondary dormancy had no effect on DNA methylation levels , but the effect of genetic variation on DNA methylation levels was either moderate ( h2 = 0 . 3 ) or large ( h2 = 0 . 6 ) . Thus , in these data sets , population structure could confound the relationship between the predictor variable ( the capacity for secondary dormancy ) and DNA methylation levels if not taken into account . As predicted , we found that the BMM implemented in MACAU appropriately controlled for genetic effects on DNA methylation levels: whether DNA methylation levels were moderately ( h2 = 0 . 3 ) or strongly ( h2 = 0 . 6 ) heritable , MACAU did not detect any sites associated with secondary dormancy at a relatively liberal false discovery rate threshold of 20% ( whether calculated against empirical permutations or calculated using the R package qvalue [32] ) . In addition , the p-value distributions for secondary dormancy effects on DNA methylation levels , in both simulations , did not differ from the expected uniform distribution ( Fig 1; Kolmogorov-Smirnov ( KS ) test when h2 = 0 . 3: D = 0 . 015 , p = 0 . 909; when h2 = 0 . 6: D = 0 . 016 , p = 0 . 874; genomic control factors: 0 . 90 when h2 = 0 . 3 , 0 . 93 when h2 = 0 . 6 ) . In contrast , when we analyzed the same simulated data sets with a beta-binomial model , we erroneously detected 2 CpG sites associated with secondary dormancy when heritability was set to 0 . 3 , and 4 CpG sites when heritability was set to 0 . 6 ( at a 20% FDR in both cases ) . More concerningly , the distributions of p-values produced by the beta-binomial model were significantly different from the expected uniform distribution and skewed towards low ( significant ) values ( KS test when h2 = 0 . 3: D = 0 . 084 , p = 1 . 75 x 10−8; when h2 = 0 . 6: D = 0 . 096 , p = 2 . 80 x 10−11; genomic control factors: 1 . 18 when h2 = 0 . 3 , 1 . 32 when h2 = 0 . 6 ) . These results suggest an increasing problem with false positives as the heritability of DNA methylation levels increases ( see S11 Fig for similar results when comparing a linear model to a linear mixed model ) . Notably , this problem should become more acute with increasing sample size , which provides greater power to detect false positives generated by this type of confounding [89] . Indeed , both increasing the simulated sample size and increasing the simulated correlation between the predictor variable and genetic structure produces increasingly poorly calibrated results . For example , when sample sizes were simulated from 25 up to 1000 individuals ( and the heritability of DNA methylation levels was set to 0 . 6 ) , we observed genomic inflation factors ranging from 1 . 03–3 . 49 for data sets analyzed with a beta-binomial ( Fig 2A ) . Not surprisingly , for a dataset of a fixed size , the beta-binomial genomic control factor increased as the confounding between population structure and the predictor variable of interest became more extreme ( see S12A Fig for comparable results for a linear model ) . In contrast , when we analyzed the same simulated datasets with the BMM implemented in MACAU , the genomic control factors consistently ranged from 0 . 82–1 . 08 , even when sample sizes were large and/or the correlation between population structure and the predictor variable was substantial ( Fig 2B; see S12B Fig for comparable results from a linear mixed model ) . Importantly , these differences in genomic control factors can translate into substantial differences in the results suggested by a given method . For example , when n = 1000 and the predictor variable is highly confounded with population structure ( R2 = 0 . 5 ) , a beta-binomial falsely identified 32% of sites in the data set as differentially methylated ( 10% FDR ) , while MACAU correctly identified no differentially methylated sites ( 10% FDR; S13 Fig ) . To investigate the calibration of test statistics in the real data set , we then analyzed the relationship between the secondary dormancy phenotype and WGBS data for the 24 Arabidopsis accessions in which both phenotype and WGBS data were available ( n = 830 , 676 CpG sites tested [32 , 33 , 34] ) . We again compared the performance of a simple linear model , a binomial model , a beta-binomial model , the BMM implemented in MACAU , and an LMM implemented in GEMMA . Further illustrating its poor handling of Type I error , the binomial model detected more than 100 , 000 secondary dormancy-associated sites at a 10% empirical FDR threshold , respectively , with a genomic control factor of 3 . 81 . A beta-binomial model substantially improved over the binomial model , but still detected 39 secondary dormancy-associated sites at a 20% empirical FDR threshold , and 150 sites and 690 sites at a 10% or 20% FDR qvalue threshold , respectively ( genomic control factor = 1 . 16 ) . Given the clear confounding of population structure and secondary dormancy in this sample , as well as the results of our simulations , these associations are probably largely , if not completely , spurious . In contrast , MACAU , the linear mixed model ( GEMMA ) , and the simple linear model did not identify any CpG sites associated with secondary dormancy , either at a 10% or a 20% false discovery rate threshold ( Fig 1 and S11 Fig; genomic control factors: MACAU– 0 . 89 , GEMMA– 0 . 97 , Linear model– 0 . 99 ) . Based on our earlier simulations , the similarity of performance among the three approaches likely stems from different reasons: the linear model is poorly powered to detect positive hits with this sample size ( either true positives or false positives ) ; the linear mixed model controls for population structure but has low power to detect true associations; while MACAU combines both the increased power conferred by modeling the raw count data with appropriate controls for population structure ( see Fig 1 and results below ) . In other data sets , a predictor variable of interest may not be confounded with genetic structure , but modeling genetic similarity between samples could reduce residual error variance and improve power . To investigate this scenario , we focused on the relationship between age and DNA methylation levels in a wild baboon population . Female baboons remain in their natal groups throughout their lives , producing relatedness values that are primarily due to matrilineal descent . The resulting genetic structure is one in which females tend to be more closely related to each other , on average , than males or male-female dyads [90] , but in which not all females are related ( because multiple matrilines co-reside in a single group ) . Data sets drawn from baboon populations therefore include a substantial number of unrelated individuals , but also some dyads that are genetically non-independent ( i . e . , relatives: S14 Fig ) . To test the relative performance of different modeling approaches in this setting , we first simulated moderate to large genetic effects on DNA methylation levels ( h2 = 0 . 3 and 0 . 6 respectively , as in the Arabidopsis simulation above ) and relatedness values based on the observed distribution of relatedness values within baboon social groups ( n = 80 , 500 , or 1000 baboons ) . We again simulated a range of non-zero effect sizes ( percent variance explained by age = 5% , 10% , or 15% ) for 500 true positive sites , and an effect size of zero for 4500 true negative sites . In simulations in which age had a moderate effect on DNA methylation levels ( PVE = 10% ) , MACAU detected 11 . 4% ( when h2 = 0 . 3 ) and 20 . 6% ( when h2 = 0 . 6 ) of simulated true positives at a 10% empirical FDR , and produced well calibrated p-values for sites with no simulated age effect ( S15 Fig ) . In comparison , the beta-binomial model ( the next best model ) detected 8 . 2% and 10 . 4% of true positives , respectively ( Fig 3 ) . As in the simulations , we again observed that a simple binomial model was prone to type I error , which resulted in failure to detect true age-associated sites when empirical FDRs were calculated against permuted data . Our additional simulations at PVE = 5% or PVE = 15% , and n = 500 or n = 1000 , confirmed MACAU’s advantage over other methods across a range of conditions ( S16 and S17 Figs ) . As expected , the magnitude of this advantage was positively correlated with the heritability of DNA methylation levels . Finally , we analyzed the new baboon RRBS data set for differential methylation patterns by age ( n = 50 , age range = 1 . 76–18 . 01 years in our sample , S4 Table ) . Because age-related effects on DNA methylation levels are well described , this approach allowed us to not only evaluate MACAU’s ability to detect differentially methylated sites , but also to identify known age-related signatures in DNA methylation data [38 , 39 , 91–93] . This data set included 433 , 871 CpG sites , enriched for putatively functional regions of the genome ( e . g . , genes , gene promoters , CpG islands , as expected in RRBS data sets [25 , 26]: S18 Fig; see also S19 Fig and S4 Table for additional information on data quality , including bisulfite conversion rates , MspI digest efficiency , correlation with gene expression levels , and methylation level distributions by genomic regions ) . As in our simulations , we found that MACAU provided increased power to detect age effects in the presence of familial relatedness . We detected 1 . 6-fold more age-associated CpG sites at a 10% empirical FDR using MACAU compared to the results of a beta-binomial model , the next best approach ( 1 . 4-fold more sites at a 20% empirical FDR; Fig 4 and S20 Fig ) . This advantage was consistently observed across all FDR thresholds we considered , except for relatively low ( <7 . 5% ) empirical FDR thresholds , when all of the methods were very low powered as a result of the modest sample size . We performed several analyses to investigate the likely validity and functional importance of the age-associated CpG sites we identified . Based on the results of previous studies , we expected that age-associated sites in CpG islands would tend to gain methylation with age [92 , 93] , while sites in other regions of the genome ( e . g . , CpG island shores , gene bodies ) would tend to lose methylation with age [92 , 93] . In addition , we expected that , in whole blood , bivalent/poised promoters should gain DNA methylation with age , while enhancers should lose methylation with age ( as discussed in [91 , 92 , 94] ) . Finally , we expected that stretches of differentially methylated sites ( i . e . , differentially methylated regions , or DMRs ) would tend to occur in or near CpG islands and CpG shores , potentially altering how steeply methylation levels change between islands and their surrounding shelves ( e . g . , [95] ) . Our results conformed to these patterns: sites in CpG islands tended to gain methylation with age ( 71 . 4% of sites were positively correlated with age ) ; and sites in promoters , CpG island shores , and gene bodies tended to lose methylation with age ( 72 . 7% , 75 . 4% , and 75 . 2% of sites were negatively correlated with age , respectively; Fig 4 ) . In addition , we found that positively correlated , age-associated sites were highly enriched in chromatin states associated with bivalent/poised promoters ( as defined by the Roadmap Epigenomics Project [96] ) . Specifically , age-associated CpG sites in bivalent/poised promoters were 3 . 4 times more likely to show increases in DNA methylation with age , compared to age-associated CpG sites in other regions ( p < 10−10 , Fisher’s exact test ) . Negatively correlated age-associated sites ( i . e . , sites where DNA methylation levels decreased with age ) were strongly enriched in enhancers ( defined as sites either marked by H3K4me1 in human PBMCs [97] or sites within chromatin states annotated as ‘enhancers’ by the Roadmap Epigenomics Project [96] , p = 2 x 10−4 , Fisher’s exact test ) . Finally , we detected 142 age-related DMRs , the majority of which were found in CpG islands , shores , and bridging islands and shores ( S21 Fig and S5 Table ) . We also reasoned that true positive age-associated CpG sites should contain information about age-associated gene expression levels . To test this hypothesis , we turned to previously generated whole blood RNA-seq data [43] from the same baboon population ( n = 63; only four baboons in the RNA-seq data set were also included in the DNA methylation data set ) . Overall , we observed a strong enrichment of differentially methylated CpG sites in or near ( within 10 kb ) blood-expressed genes ( n = 12 , 018 genes ) , compared to the background set of all CpG sites near genes ( Fisher’s exact test , p < 10−10 ) . Further , CpG sites near age-associated genes ( n = 1396 genes , 10% FDR ) were 30 . 5% more likely to be differentially methylated with age compared to the background set of all CpG sites near genes ( Fisher’s exact test , p = 0 . 032; Fig 4 ) . Notably , this enrichment was almost always stronger for the set of differentially methylated sites identified by MACAU than for the same number of top sites identified when running the linear model , linear mixed model , binomial , or beta-binomial approaches , across different FDR thresholds ( S22 Fig ) .
DNA methylation levels can have potent effects on downstream gene regulation , and , in doing so , can shape key behavioral , physiological , and disease-related phenotypes [7 , 20 , 98–100] . These observations have motivated an increasing number of DNA methylation studies in humans and other organisms , highlighting the need for sophisticated statistical methods that can accommodate the complexities of a broad array of data sets [19 , 46] . Here , we demonstrate that the binomial mixed model implemented in our software MACAU can ( i ) effectively control for confounding relationships between genetic background and a predictor variable of interest and ( ii ) provide increased power to detect true sources of variance in DNA methylation levels in data sets that contain kinship or population structure . In addition , MACAU provides increased flexibility over current count-based methods that cannot accommodate biological replicates ( e . g . , Fisher’s exact test ) , continuous predictor variables ( e . g . , DSS , MOABS , RadMeth ) , or biological or technical covariates ( e . g . , MOABS , DSS; see also Table 1 ) . Given the increasing interest in both the environmental [21 , 101 , 102] and genetic [16 , 17 , 19 , 103] architecture of DNA methylation levels , we believe MACAU will be a useful tool for generalizing epigenomic studies to a larger range of populations . MACAU is particularly well suited to data sets that contain related individuals or population structure; notably , several major population genomic resources contain structure of these kinds ( e . g . , the HapMap population samples [104] , the Human Genome Diversity Panel [105] , and the 1000 Genomes Project in humans [106]; the Hybrid Mouse Diversity Panel in mice [107]; and the 1001 Genomes Project in Arabidopsis [108] ) . Indeed , our results suggest MACAU is a useful tool even in data sets that are less affected by genetic structure , or when the heritability of DNA methylation levels is unclear . Because the beta-binomial model is effectively incorporated as a special case , MACAU exhibits only a slight loss of power relative to a beta-binomial model without genetic random effects when h2 = 0 , while conferring better power and better test statistic calibration when h2 > 0 ( S9 and S16 and S17 Figs and Fig 1 ) . Previous studies in humans have shown that , while the heritability of DNA methylation levels varies across loci , an appreciable proportion of loci are either modestly ( h2 ≥ 0 . 3: 21 . 06% of all CpG sites ) or highly ( h2 ≥ 0 . 6: 6 . 95% of all CpG sites ) heritable [39 , 109] . Further , DNA methylation QTLs are widespread across the genome [18 , 38 , 103] . Thus , because investigators will rarely have a priori knowledge of the heritability of DNA methylation levels at a given locus , and because the advantage of a beta-binomial model is small even when heritability is zero , we recommend applying MACAU in cases in which genetic effects on DNA methylation levels are poorly understood . In addition , our model provides a natural framework for incorporating the spatial dependency of DNA methylation levels across neighboring sites [110 , 111] , which we expect to increase power even further [110 , 111] . However , we do note that , even with the efficient algorithm implemented here , fitting the binomial mixed model ( or its extensions ) remains more computationally expensive than other approaches for moderately sized datasets ( S3 Table ) . While it remains appropriate for the sample sizes used in current studies ( e . g . , dozens to hundreds of individuals ) , or even larger with the support of a moderate-sized computing cluster ( because MACAU is easily parallelizable with respect to sites ) , rapid increases in sample size—especially in the context of EWAS—strongly motivate additional algorithm development to scale up the binomial mixed model for data sets that include thousands or tens of thousands of individuals . This is particularly important given that methods tailored for other types of studies ( e . g . , quantile normalization followed by linear mixed modeling or voom + limma , both commonly used for RNA-seq ) do not appear to translate well to bisulfite sequencing data sets ( S8 Fig; see Methods for additional information on the voom + limma comparison ) . Although we developed MACAU with the analysis of bisulfite sequencing data in mind , we note that a count-based binomial mixed model may be an appropriate tool in other settings as well . For example , allele-specific gene expression ( ASE ) can be measured in RNA-seq data by comparing the number of reads originating from a given variant to the total number of mapped reads for that site [77 , 112–114] . Similarly , alternative isoform usage can be represented as a proportion of reads containing a non-constitutive exon versus the total reads for the same gene [47] . The structure of these data are highly similar to the structure of bisulfite sequencing data , which focus on counts of methylated versus total reads . Unsurprisingly , beta-binomial models have also emerged as one of the most popular methods for estimating both ASE values [114–116] and alternative isoform usage [47] . Researchers interested in the predictors of variation in either of these measures—which could include trans-acting genetic effects , environmental conditions , or properties of the individual ( e . g . , sex or disease status ) —might also benefit from using MACAU . Recent work from the TwinsUK study motivates the need for such a model: Grundberg et al . demonstrated a strong heritable component to ASE levels [117] , which could be effectively taken into account using the random effects approach implemented here . Finally , linear mixed models have been recently proposed to account for cell type heterogeneity in epigenome-wide association studies focused on array data [118] . In this framework , the random effect covariance structure is based on overall covariance in DNA methylation levels between samples , which is assumed to be largely attributable to variation in tissue composition . MACAU provides a potential avenue for extending these ideas to sequencing-based data sets .
We downloaded publicly available WGBS data generated by Schmitz et al . [48] , as well as previously published SNP genotype data [87] and secondary dormancy data [86] for 24 Arabidopsis accessions . We used the SNP genotype data ( specifically , 188 , 093 sites with minor allele frequency >5% ) to construct a pairwise genetic relatedness matrix , K , as the product of a standardized genotype matrix X , or K = XXT/p [56] , where genotypes were expressed as 0 , 1 , or 2 depending on the number of reference alleles for that site-sample combination . We used this estimate of K for both the simulations and our analyses of the real WGBS data . In these analyses , we focused on CpG sites measured in ≥50% of accessions , and excluded sites that were constitutively hypermethylated ( average DNA methylation level >0 . 90 ) or hypomethylated ( average DNA methylation level <0 . 10 , following [101 , 118] ) . We also excluded highly invariable sites ( i . e . , sites where the standard deviation of DNA methylation levels fell in the lowest 5% of the overall data set ) and sites with very low coverage ( i . e . , sites where the mean coverage fell in the lowest quartile for the overall data set , below a mean of 3 . 34 reads ) . After filtering , the final data set consisted of 830 , 676 sites . For the analysis of test statistic calibration as a function of sample size ( Fig 2 ) , we also used Arabidopsis data , but simulated the phenotype data as a function of genetic covariance between the accessions . Genotype data were obtained from [87] . To simulate the methylated read counts and total read counts that result from WGBS and RRBS , we performed the following procedure: First , we simulated the proportion of methylated reads for each site . To do so , we drew secondary dormancy values or age values , x , as the predictor of interest , from the actual values for the Arabidopsis accessions or from the baboon population , respectively . For simulations that focused on Arabidopsis data sets of various sizes ( e . g . , Fig 2 ) , we simulated x and varied the degree to which it was confounded with population structure . Specifically , for each dataset ( ranging from n = 25 to n = 1000 accessions ) we performed principal components analysis on the SNP genotype data , and extracted the first principal component to capture the major axis of population structure ( PC1 ) . We then added environmental noise from a zero-centered normal distribution to achieve a correlation ( R2 ) between the simulated phenotype and PC1 that reached the desired value ( ranging from R2 = 0 . 1 to 0 . 5 ) . For each simulated data set , we simulated the DNA methylation level at each CpG site , π , as a linear function of x and its effect size , β . In addition , we included the effects of genetic variation ( g ) and random environmental variation ( e ) , passed through a logit link ( based on the model described in the Results section ) . For the baboon RRBS and the Arabidopsis WGBS simulations , we determined K from 14 highly variable microsatellite loci or from the publicly available SNP data , as described above . For each simulation , we set h2 to 0 , 0 . 3 , or 0 . 6 to simulate non-heritable , modestly heritable , or highly heritable DNA methylation levels . We also estimated the variance term σ2 from the real data sets . Specifically , we took the mean estimate of σ2 across all sites ( calculated in MACAU ) for each real data set , and used this value as the fixed value of σ2 in the corresponding simulations . Next , for each site , we simulated total read counts ri for each individual i from a negative binomial distribution that models the extra variation observed in the real data: ri∼NB ( t , p ) , ( 5 ) where t and p are site specific parameters estimated from the real data . Specifically , we generated 10 , 000 sets of t and p parameters by fitting a negative binomial distribution to the total read count data from 10 , 000 randomly selected CpG sites in the real baboon RRBS data set or the real Arabidopsis data set , using the function ‘fitdistr’ in the R package MASS [129] . To simulate counts for a given CpG site , we randomly selected one of these parameter sets to produce the total number of reads . Finally , we simulated the number of methylated reads for each individual at that locus ( y ) by drawing from a binomial distribution parameterized by the number of total reads ( r ) and the DNA methylation level ( π ) . For all simulated and real data sets , we used raw methylated and total read counts to compare the results of a beta-binomial model ( using a custom R script ) , a binomial model ( implemented via ‘glm’ in R ) , and the binomial mixed model implemented in MACAU . For computation time comparison , we used the MCMCglmm software , which also provides an implementation of a binomial mixed model [78] . In addition , we used the same count data to run a Fisher’s exact test ( implemented in R ) , DSS [31] , and RadMeth [33] in the subset of analyses that utilized these programs . To analyze simulated and real data sets using a linear model ( implemented using ‘lm’ in R ) or the linear mixed model implemented in GEMMA [34] , we estimated DNA methylation levels by dividing the number of methylated reads by the total read count for each individual and CpG site . We then quantile normalized the resulting proportions for each CpG site to a standard normal distribution , and imputed any missing data using the K-nearest neighbors algorithm in the R package impute [130] . In addition to the quantile normalization approach , we also evaluated three other methods for transforming methylation proportions: a logit transformation , following [110]; the “M” value transformation ( log2 ( ( methylated counts + α ) / ( unmethylated counts + α ) ) , where α = 0 . 01 , following [30]; and an arcsin square root transformation , following [131] . All four approaches produced qualitatively identical results ( S8 Fig ) , so we elected to concentrate on the results from quantile normalization in the main text . Finally , we also tested the performance of a powerful , commonly used method for modeling RNA-seq data: the combination of the voom function for data weighting with limma , a linear model approach [132] . Our results indicated that voom + limma performs more poorly than even a simple linear model ( S24 Fig ) , probably because read depth variation is much more complicated in bisulfite sequencing studies than in RNA-seq studies ( S1 Fig ) . Because voom + limma also cannot account for population structure , we report these results in the SI but focus on results from the simple linear model in the main text . To compute empirical false discovery rates in simulated data , we divided the number of false positives detected at a given p-value threshold by the total number of sites called by the model as significant at that threshold ( i . e . , the sum of false positives and true positives ) . To compute empirical false discovery rates in the real data , in which the false positives and true positives were unknown , we used permutations . Specifically , we permuted the predictor variable for each data set four times , reran our analyses , and then calculated the false discovery rate as the average number of sites detected at a given p-value threshold in the permuted data divided by the total number of sites detected at that threshold in the real data . For simulated data sets only , we also calculated the area under the receiver operating characteristic curve ( AUC ) to produce a measure of the overall tradeoff between detecting true positives and calling false positives . Our initial analyses of the baboon RRBS dataset focused only on the relative ability of each method to detect age-associated sites . For these analyses , we therefore did not control for other biological covariates that may contribute to variance in DNA methylation levels ( note that biological covariates cannot be incorporated into several implementations of the beta-binomial model [31 , 32]: see Table 1 ) . However , to investigate patterns of age-related changes in DNA methylation levels , and to compare them to previously described patterns in the literature , we wished to control for such covariates . To do so , we reran the differential methylation analysis in MACAU , this time controlling for sex , sample age , and efficiency of the bisulfite conversion rate estimated from the lambda phage spike-in . First , we investigated whether age-associated sites were enriched in functionally coherent regions of the genome , many of which have previously been identified as age-related [38 , 92 , 93] . To do so , we defined gene bodies as the regions between the 5’-most transcription start site ( TSS ) and 3’-most transcription end site ( TES ) of each gene using Panu 2 . 0 annotations from Ensembl [133] . We defined promoter regions as the 2 kb upstream of the TSS . CpG were annotated based on the UCSC Genome Browser track for baboon [134] , with CpG island shores defined as the 2 kb regions flanking either side of the CpG island boundary ( following [26 , 135 , 136] ) . Finally , because no enhancer annotations are available that are specific to baboons , we used H3K4me1 ChIP-seq data generated by ENCODE ( from human peripheral blood mononuclear cells ) to define enhancer regions [97] . In addition , we used chromatin state annotations from the Roadmap Epigenomics Project ( also generated from human peripheral blood mononuclear cells ) to further investigate biases in the locations of age-associated sites [96] . Using these annotation sets , we performed Fisher’s Exact Tests to ask whether age-associated sites were enriched or underrepresented in specific genomic regions . To identify differentially methylated regions ( DMRs ) , we used the criteria proposed by [137] . Specifically , DMRs contained at least 3 differentially methylated sites with an inter-CpG distance ≤1 kb , with only 3 non-differentially methylated sites permitted in the DMR as a whole . Second , we asked whether differentially methylated sites were more likely to fall close to blood-expressed genes . For this comparison , we drew on previously published RNA-seq data , generated from whole blood samples collected in the Amboseli baboon population [43] . We defined blood-expressed genes as those genes that had non-zero counts in more than 10% of individuals in the RNA-seq data sets , and that had mean read counts greater than or equal to 10 . We then compared the number of differentially methylated CpG sites near blood-expressed genes ( i . e . , within the gene body or within 10 kb of the gene TSS or TES ) to the number of differentially methylated CpG sites near genes that were not expressed in blood , using a Fisher’s Exact Test . Finally , we investigated whether CpG sites that occur near genes that are differentially expressed with age were also more likely to be differentially methylated with age . For this comparison , we defined ‘age-associated genes’ as genes differentially expressed with age ( at a 10% FDR ) in the RNA-seq data set [43] . We compared the number of differentially methylated CpG sites near blood-expressed , age-associated genes to the number of differentially methylated CpG sites near genes that were not within this set of genes , again using a Fisher’s Exact Test . The baboon data used in this study was generated from samples collected from wild baboons living in the Amboseli ecosystem of southern Kenya . This research is conducted under the authority of the Kenya Wildlife Service ( KWS ) , the Kenyan governmental body that oversees wildlife ( permit number NCST/RCD/12B/012/57 to Jenny Tung ) . As the animals are members of a wild population , KWS requires that we do not interfere with injuries to study subjects inflicted by predators , conspecifics , or through other naturally occurring events . Permission to perform temporary immobilizations ( for blood sample collection ) was granted by KWS; further , these immobilizations were supervised by a KWS-approved Kenyan veterinarian , who monitored anesthetized animals for hypothermia , hyperthermia , and trauma ( no such events occurred during our sample collection efforts ) . Observational and sample collection protocols were approved though IACUC committees at Duke University ( current protocol is A020-15-01 to Jenny Tung and Susan C . Alberts ) . The MACAU software and a custom script for implementing a beta-binomial model in R is available at: www . xzlab . org/software . html . Previously published data sets are available at http://bergelson . uchicago . edu/regmap-data/regmap . html/ ( Arabidopsis SNP genotype data ) ; http://www . ncbi . nlm . nih . gov/geo/ ( Arabidopsis WGBS data: GSE43857 ) ; http://www . nature . com/nature/journal/v465/n7298/full/nature08800 . html#supplementary-information ( Arabidopsis phenotype data ) ; and http://www . ncbi . nlm . nih . gov/sra ( Baboon RNA-seq data: GSE63788 ) . Baboon RRBS data generated in this study are deposited in NCBI ( project accession SRP058411 ) . | DNA methylation is an important epigenetic modification involved in regulating gene expression . It can be measured at base-pair resolution , on a genome-wide scale , by coupling sodium bisulfite conversion with high-throughput sequencing ( a technique known as ‘bisulfite sequencing’ ) . However , the data generated by such methods present several challenges for statistical analysis . In particular , while the raw data generated from bisulfite sequencing experiments are read counts , they are often converted to proportions for ease of modeling , resulting in loss of information . Furthermore , although DNA methylation levels are known to be heritable—and are thus affected by kinship and population structure—existing approaches for modeling bisulfite sequencing data fail to account for this covariance . Such failure can lead to spurious associations and reduced power . Here , we present a new approach that models bisulfite sequencing data using raw read counts , while also taking into account population structure and other sources of data over-dispersion . Using simulations and two real data sets ( publicly available data from Arabidopsis thaliana and newly generated data from Papio cynocephalus ) , we demonstrate that our model provides well-calibrated p-values and improves power compared with previous methods . In addition , the DNA methylation patterns identified by our method agree with those reported in previous studies . | [
"Abstract",
"Introduction",
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"Methods"
] | [] | 2015 | A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data |
Forward genetic screens represent powerful , unbiased approaches to uncover novel components in any biological process . Such screens suffer from a major bottleneck , however , namely the cloning of corresponding genes causing the phenotypic variation . Reverse genetic screens have been employed as a way to circumvent this issue , but can often be limited in scope . Here we demonstrate an innovative approach to gene discovery . Using C . elegans as a model system , we used a whole-genome sequenced multi-mutation library , from the Million Mutation Project , together with the Sequence Kernel Association Test ( SKAT ) , to rapidly screen for and identify genes associated with a phenotype of interest , namely defects in dye-filling of ciliated sensory neurons . Such anomalies in dye-filling are often associated with the disruption of cilia , organelles which in humans are implicated in sensory physiology ( including vision , smell and hearing ) , development and disease . Beyond identifying several well characterised dye-filling genes , our approach uncovered three genes not previously linked to ciliated sensory neuron development or function . From these putative novel dye-filling genes , we confirmed the involvement of BGNT-1 . 1 in ciliated sensory neuron function and morphogenesis . BGNT-1 . 1 functions at the trans-Golgi network of sheath cells ( glia ) to influence dye-filling and cilium length , in a cell non-autonomous manner . Notably , BGNT-1 . 1 is the orthologue of human B3GNT1/B4GAT1 , a glycosyltransferase associated with Walker-Warburg syndrome ( WWS ) . WWS is a multigenic disorder characterised by muscular dystrophy as well as brain and eye anomalies . Together , our work unveils an effective and innovative approach to gene discovery , and provides the first evidence that B3GNT1-associated Walker-Warburg syndrome may be considered a ciliopathy .
A powerful , tried and true approach to identify which genes function in a particular biological process is to create collections of organisms harbouring multiple mutations via random mutagenesis , followed by screening the mutant library for organisms that exhibit the desired altered phenotypes . Although such forward genetics strategies have produced numerous fundamental discoveries , a significant limitation of this approach in metazoans is the prolonged time required to identify the causative mutations . The bottleneck typically arises from the required genetic mapping , complementation tests to exclude known genes , and sequencing of candidates genes . To circumvent the major disadvantage of forward genetics , reverse genetic approaches have been employed . Various strategies for disrupting a collection of known genes ( e . g . , RNAi , homologous recombination , transposon mutagenesis , etc . ) are combined with phenotypic screening to identify candidates . Reverse genetics approaches also have drawbacks , however , including the need to handle and process tens of thousands of strains to assay the entire genome , off-target effects in the case of RNAi , and omission of essential genes . We hypothesised that we could use whole-genome sequencing in combination with statistical genetics to inaugurate a novel gene discovery approach which retains the advantages of both forward and reverse genetics , and yet minimises their downsides . To do this , we employed the Million Mutation Project ( MMP ) [1] , a collection of 2007 Caenorhabditis elegans strains harbouring randomly-induced mutations whose genomes are fully sequenced ( data is publicly available: http://genome . sfu . ca/mmp/about . html ) . This mutant library represents an unprecedented genetic resource for any multicellular organism , wherein the strains collectively contain one or more potentially disruptive alleles affecting nearly all C . elegans coding regions . On average , each strain contains ~ 400 non-synonymous mutations affecting protein coding sequences . We postulated that this whole-genome sequence information would allow an “eyes wide open” approach when performing a genetic screen , such that pairing this resource with a high-throughput assay would enable rapid discovery of genes not previously associated with our biological process of interest . Here , we demonstrate that testing for association between variants from the MMP library and phenotype data with the Sequence Kernel Association test ( SKAT ) [2] allows us to effectively and efficiently predict novel genes important for our chosen biological process: the development and function of the amphid and phasmid sensillum , which includes both ciliated sensory neurons as well as glial-like neuronal support cells . Primary ( non-motile ) cilia arise from a modified centriole ( basal body ) and act as 'cellular antennae' that transduce environmental cues to the cell [3] . They enable sensory physiology ( such as olfaction/chemosensation , mechanosensation , vision ) and are central to signalling pathways essential for metazoan development [4] . Dysfunction of cilia is implicated in a number of human diseases , including polycystic kidney disease , congenital heart disease , and an emerging group of genetic disorders termed ciliopathies ( e . g . , Bardet-Biedl , Meckel-Gruber and Joubert Syndromes ) . In these ciliopathies , disruption of many , if not all , cilia in the human body results in a plethora of defects , including retinal degeneration , organ cyst formation , obesity , brain malformations , and various other ailments [5][6] . In C . elegans , the uptake of a fluorescent lipophilic dye , DiI , from the environment is used to probe the integrity of the amphid and phasmid sensillum , which includes cilia and ciliated sensory neurons , as well as glial-like sheath cells . DiI is selectively incorporated into six pairs of ciliated amphid channel sensory neurons in the head ( ADF , ADL , ASH , ASI , ASJ , and ASK ) , and two pairs of ciliated phasmid channel sensory neurons in the tail ( PHA and PHB ) , via environmentally-exposed cilia present at the tips of dendrites ( S1 Fig ) [7 , 8] . Many dye-filling ( dyf ) mutants known from genetic screens [8 , 9] harbour mutations in genes influencing ciliated sensory neuron development and function , including ciliogenesis [10] , cilia maintenance [11] , axon guidance [9] , dendrite anchoring/formation [10] , as well as cell fate [12] . Importantly , non-cell autonomous effects from disruption of neural support ( glial ) cells can also result in dye-filling defects [10 , 13] . When we applied SKAT to the phenotype data we collected from screening the MMP strains for dye-filling , we found that a previously uncharacterised C . elegans gene , bgnt-1 . 1/F01D4 . 9 , plays an essential role in this process . We found that the ciliated sensory neurons of bgnt-1 . 1 mutants fail to fill with a lipophilic dye , a phenotype indicative of their dysfunction , and that BGNT-1 . 1 localises specifically to the trans-Golgi network of the amphid and phasmid sheath cells . These are glial-like neuronal support cells , which are critical for the development and function of ciliated sensory neurons . Interestingly , bgnt-1 . 1 is the orthologue of human B3GNT1/B4GAT1 , a gene implicated in Walker-Warburg syndrome [14 , 15] , a disorder with clinical ailments resembling a ciliary disease ( ciliopathy ) .
We screened 480 randomly-chosen whole-genome sequenced multi-mutation MMP strains , ~25% of the library , for defects in DiI uptake in amphid and phasmid ciliated sensory neurons ( Fig 1 ) . We found 40 MMP strains which exhibit significant amphid dye-filling defects and 40 MMP strains which exhibit significant phasmid dye-filling defects; the strains with amphid and phasmid dye-filling defects are not necessarily identical ( Fig 1C , Table 1 , S1 Table ) . We identified 11 completely dye-fill defective strains , where all worms sampled failed to take up dye . A preliminary look at the data indicates that of these , 10 contained deleterious ( “knockout” ) mutations in previously identified dye-filling genes ( e . g . , nonsense and frameshift-inducing deletions; S2 Table ) . Additionally , we uncovered 47 partially dye-fill defective strains , where a proportion of the population failed to fill with dye significantly more often than wild-type worms . Of these partially dye-fill defective strains , 1 harbours a nonsense mutation and 10 display missense mutations in known dye-filling genes ( S2 Table ) . Despite the fact that we can identify some strains with mutations in genes previously shown to cause dye-filling defects , it is not clear that it is the mutations in these genes which are necessarily the cause of the dye-filling defects in these strains . Furthermore , there are 38 strains where we cannot generate a hypothesis as to what genetic variation is responsible for the dye-filling defect . To facilitate identification of genes responsible for the observed dye-fill defects , we hypothesised that a recently developed statistical genetics approach commonly used in human genetics , but underutilised in model organisms , would allow for the rapid prioritisation of candidate genes . Specifically , we chose to employ the sequence kernel association test ( SKAT ) to uncover genes associated with the dye-filling phenotype . SKAT is a regression method to test for association between rare and/or common genetic variants in a region and a continuous or dichotomous trait [2] . We chose SKAT over other statistical analyses for several reasons . For our dataset , it was imperative that we chose an association test that deals effectively with rare variants , as 800 , 000/850 , 000 of the non-synonymous variants in the MMP library are unique—meaning that they are present in only a single isogenic strain in the library . Hence , genome-wide association study ( GWAS ) approaches , which typically test for an association between common variants ( generally defined as a minor allele frequency > 5% ) and a trait of interest , would be unsuitable for analysis of phenotype datasets derived from the MMP library . We also viewed SKAT as an optimal method to use for our dataset because it permits the use of prior information to assign weights to genetic variants . For example , nonsense mutations might be expected to be more deleterious than other variants which may cause more modest changes to the protein , such as missense mutations and in-frame deletions . The C-alpha test [16] , which is quite similar to SKAT in the absence of covariants ( e . g . , age , sex , etc . ) , could have also been used for our dataset , but we chose to employ SKAT because it facilitates implementing and assigning biologically relevant weights to variants . Finally , SKAT was chosen over other related burden tests , such as the cohort allelic sums test ( CAST ) [17] and the combined multivariate and collapsing ( CMC ) method [18] , because unlike these tests , SKAT does not assume that all ( common ) variants will affect the trait in the same direction . Given that the groups of worms which have amphid dye-filling and phasmid dye-filling defects do not necessarily overlap , we performed SKAT separately for each dataset . We chose to perform the linear regression version of SKAT in combination with log transformation of the response ( phenotype ) variable , as opposed to a logistic regression version of SKAT because in its current implementation , the logistic regression version of SKAT does not work with proportion data , and takes only dichotomous traits coded as 0 or 1 . Quantile-quantile ( QQ ) -plots were used to choose the appropriate constant to add to the response ( phenotype ) variable before log transformation ( S2 & S3 Figs ) . Finally , we performed SKAT with biologically relevant weights assigned to the variants . We assigned mutations which would likely result in the creation of a null mutation ( nonsense and splicing mutations , as well as frameshift causing deletions ) a weight of 1 , mutations which would result in truncation of the protein ( in-frame deletions ) a weight of 0 . 75 , and mutations which would result in a change in amino acid sequence ( missense mutation ) a weight of 0 . 25 . We hypothesised these were reasonable weights to assign to each class of mutation based on the current knowledge in the field of genetics . Genome-wide SKAT analyses using biologically relevant weights on the amphid dye-filling dataset reveal 5 genes that reach significance when we adjust for multiple testing using a false discovery rate ( FDR; Benjamini-Hochberg procedure ) of 5% ( FDR adjusted p-value was < 0 . 05 , Table 2 , S3 Table ) . SKAT analyses using biologically relevant weights on the phasmid dye-filling dataset uncovered 3 genes which reached significance , again using a FDR of 5% ( FDR adjusted p-value was < 0 . 05 , Table 3 , S4 Table ) . Dye-filling defects of both amphid and phasmid ciliated neurons is significantly associated with genes encoding intraflagellar transport proteins ( OSM-1 and CHE-3 ) , and a glycosyltransferase ( BGNT-1 . 1; Tables 2 and 3 ) . Amphid-specific dye-filling defects are found to be associated with genes encoding an Arf-GAP related protein , CNT-1 , as well as a mitotic spindle assembly checkpoint protein , MDF-1 ( Table 2 ) . No gene was found to be significantly associated with only phasmid dye-filling defects ( Table 2 and Table 3 ) . Of the three genes associated with both amphid and phasmid dye-filling defects , namely osm-1 , che-3 , and bgnt-1 . 1 , the first two are well characterised genes whose dye-filling defective phenotypes are ascribed to their key roles in intraflagellar transport ( IFT ) . OSM-1 is the orthologue of mammalian IFT172 , an IFT-B subcomplex component which functions as an adaptor to link ciliary cargo ( e . g . , tubulin , receptors and signaling molecules ) to the anterograde IFT kinesin motors , and is necessary for ciliogenesis [10] . CHE-3 , the orthologue of mammalian DYNC2H1 , is a cytoplasmic dynein heavy chain which powers the retrograde IFT-dynein motor . This molecular motor recycles IFT machinery from the growing ciliary tip back to the ciliary base and is also necessary for proper cilium formation/maintenance [19 , 20] . These two known dye-fill/cilia genes represent excellent positive controls for our screen , and indicate that other genes found to be significantly associated with these phenotypes may be novel dye-fill genes that influence cilia function . Interestingly , one of the other amphid dye-filling gene hits , cnt-1 , encodes a protein that play roles in membrane trafficking/dynamics by influencing small GTPase function , via GTPase-activating protein ( GAP ) activity . The general involvement of small GTPases of the Arf , Arf-like ( Arl ) and Rab families in cilium formation/development is well established [3] . cnt-1 encodes the orthologue of human ACAP2 , which interacts with both Rab35 [21] and Arf6 [22] to mediate crosstalk between these two proteins , at least in the context of PC12 cell neurite outgrowth , and potentially through endocytic recycling [23] . Another amphid dye-filling gene hit , mdf-1 , is homologous to Mad1 , and encodes a mitotic spindle assembly checkpoint protein [24] . To the best of our knowledge , our findings are the first to directly implicate mdf-1/Mad1 as being important for cilia development and/or function but other mitotic spindle assembly checkpoint proteins have previously been linked to cilia , including BUBR1 [25] and APC-Cdc20 [26] . The third , putative novel dye-filling gene significantly associated with both amphid and phasmid dye-fill phenotypes is bgnt-1 . 1 ( Tables 2 & 3 , S3 and S4 Tables ) . bgnt-1 . 1 encodes an unstudied C . elegans glycosyltransferase 49 family member homologous to human B3GNT1/B4GAT1 ( S4 Fig ) . B3GNT1 catalyses the addition of β1–3 linked N-acetylglucosamine to galactose [27] . In HeLa cells , its subcellular localisation is concentrated at the trans-Golgi [28] . B3gnt1 knockout mice exhibit axon guidance phenotypes [29 , 30] and deficient behavioural responses to estrous females [31] . In humans , mutations in B3GNT1 are associated with a congenital muscular dystrophy with brain and eye anomalies , Walker-Warburg syndrome ( WWS ) [14 , 15] . WWS is a suspected , but unconfirmed ciliopathy; it exhibits 6 core features common to ciliopathies , including Dandy-Walker malformation , hypoplasia of the corpus callosum , mental retardation , posterior encephalocele , retinitis pigmentosa and situs inversus [5] . Additionally , one patient is reported to exhibit dysplastic kidneys [14] , a developmental disruption which leads to cyst formation , illuminating a potential 7th core ciliopathy feature to this disorder , renal cystic disease . To divulge a potential connection between B3GNT1 and cilia and/or ciliated sensory neuron function , we sought to confirm the role of C . elegans BGNT-1 . 1 in dye-filling , and analyse its involvement in ciliated sensory neuron development . Of the eight MMP strains harbouring mutations in bgnt-1 . 1 , three ( VC20615 , VC20628 and VC20326 ) exhibit severe dye-fill phenotypes ( S1 Table ) . The C -> T missense mutation in bgnt-1 . 1 in VC20615 corresponds to P194S alteration in the protein sequence , while VC20628 and VC20326 each harbour an identical G -> A missense mutation in bgnt-1 . 1 , which leads to a G205E amino acid change in the protein sequence . Both of these mutations alter conserved amino acid residues ( S5 Fig ) . In the screen we encountered 5 additional strains that harbour missense mutations in bgnt-1 . 1 but did not exhibit dye-filling defects . Close examination of the predicted effects of these missense mutations on the amino acid sequence of the protein indicates that these alleles do not lead to amino acid changes in conserved residues ( S5 Fig ) , and thus it is not surprising that these strains do not exhibit dye-fill defects . To confirm that the mutations in bgnt-1 . 1 is responsible for the dye-filling phenotypes in bgnt-1 . 1 mutants , we rescued the dye-fill defects by expressing a fosmid containing a wild-type copy of bgnt-1 . 1 in an extrachromosomal array ( Fig 2 ) . Another way to confirm that disruption of bgnt-1 . 1 causes dye-fill defects would be to observe this phenotype in a strain harbouring a knock-out mutation in bgnt-1 . 1 . Although there are 49 bgnt-1 . 1 alleles available , a knock-out allele of bgnt-1 . 1 did not yet exist . There are two insertion/deletion alleles available , gk1221 and tm4314 , but both fall within introns and thus unlikely to affect protein function . Thus , we also tested the causality of bgnt-1 . 1 via a relatively efficient SNP mapping approach . We established that the dye-fill phenotypes from VC20615 and VC20628 strains map to the bgnt-1 . 1 locus , on chromosome IV between -5 cM and 8 cM ( S6 Fig ) . Notably , in both VC20615 and VC20628 strains , bgnt-1 . 1 is the only gene in this region harbouring a mutation which is common to both of these strains . Finally , we used CRISPR-Cas9 genome engineering [32] to independently generate three bgnt-1 . 1 knockout alleles . All three alleles delete the first and second exon of bgnt-1 . 1 and insert a selectable marker , Pmyo-2::GFP , in their place . When tested for dye-filling defects , we observe an identical dye-filling phenotype as found in the 6X outcrossed bgnt-1 . 1 ( gk210889 ) G205E allele from the Million Mutation Project ( Fig 3 ) . In all of these mutants , we observe a great decrease in the amount of dye that enters the amphid ciliated sensory neurons , which is often undetectable , as well as a complete absence of dye-filling of the phasmid ciliated sensory neurons . Together , these findings indicate that loss of bgnt-1 . 1 function results in dye-filling defects . To shed light on how bgnt-1 . 1 affects dye-filling , we studied expression of GFP-tagged BGNT-1 . 1 constructed via fosmid recombineering ( https://transgeneome . mpi-cbg . de/transgeneomics/index . html ) , and thus containing all of this gene’s endogenous regulatory elements . We find that in C . elegans , the protein localises to discrete structures in the cell body of the amphid and phasmid glial-like sheath cells ( AMsh and PHsh , respectively ) in the head and tail of the animal ( Fig 4A ) . These cells are intimately associated with the ciliated sensory neurons in the pore region where cilia are exposed to the external environment ( S1 Fig ) . Mammalian B3GNT1 is found at the trans-Golgi network in HeLa cells [28] . To assess whether this is also where C . elegans BGNT-1 . 1 localises , we performed antibody staining for SQL-1 , an established cis-Golgi marker [33] , in the strain expressing BGNT-1 . 1::GFP . We observe that in both the head and tail , the localisation of BGNT-1 . 1::GFP is always proximal to the discrete SQL-1 puncta , indicating that C . elegans BGNT-1 . 1 is concentrated at the trans-Golgi , as expected ( Fig 4B ) . Next , we queried whether loss of bgnt-1 . 1 function in the amphid and phasmid sheath cells leads to any gross ciliary morphology defects by expressing a ciliary marker in bgnt-1 . 1 mutants , namely the GFP-tagged IFT-B subcomplex protein , CHE-2 ( IFT80 ) . This experiment indicates that although the cilia of bgnt-1 . 1 mutants fail to fill with dye , their ciliary structures appear superficially wild-type ( S7A Fig ) . Since modest cilia structure defects may be more difficult to observe using pan-cilia markers , due to overlapping ciliary signals , we also characterised the phenotype of cilia and dendrites in bgnt-1 . 1 mutants within a single ciliated amphid cell , the ADL neuron . For this purpose , we used the primarily cell-specific ADL promoter , Psrh-220 , to drive expression of another cilia marker , IFT-20 ( IFT20 ) tagged with tdTomato . In this strain , we also expressed cytoplasmic GFP in the amphid socket cells so that we could evaluate whether or not the ADL cilia were correctly associated with the surrounding glial support cells and the pore where DiI has access to the amphid ciliated sensory neurons from the environment . Similar to the experiment with the CHE-2::GFP pan-cilia marker , the Psrh-220::IFT-20::tdTomato marker revealed that the ADL cilia and amphid socket ( Amso ) cell morphology also appear superficially wild-type in bgnt-1 . 1 mutants ( S7B Fig ) . We then sought to assay for potential phenotypes involving ADL cilia length ( Fig 4C ) ; length of socket cell penetration by ADL ( proxied by the distance from the distal tip of ADL cilia to the distal end of the socket cell tip; S7C Fig ) ; ADL guidance ( proportion of double rod cilia/amphid; S7D Fig ) ; and finally , ADL dendrite blebbing ( structural alteration where dendrites take bead on a string appearance; S7E Fig ) . Our analyses reveal that ADL cilia in bgnt-1 . 1 mutants are wild-type in most aspects except for a modest cilia length defect . Specifically , bgnt-1 . 1 mutants were observed to have significantly longer cilia compared to wild-type worms ( Fig 4C; p < 0 . 01 , Kruskal-Wallis test ) . BGNT-1 . 1 therefore influences amphid and phasmid neuron development and function , as well all modestly affects cilium length , without overtly affecting the gross structure of neurons or cilium formation . The localisation of BGNT-1 . 1 at the trans-Golgi network of sheath cells signifies that its effect on ciliated sensory neurons is non-cell autonomous . Interestingly , when the bgnt-1 . 1 amphid ciliated sensory neurons do fill with dye , we observe bright accumulations of dye along and/or beside the dendrites ( Fig 3C ) . These are often brighter than the staining of the cell bodies . Accumulations of dye have been observed in wild-type worms and have been attributed to the dye-filling of the amphid sheath cells [34] , but these are qualitatively much smaller than what we observed in the bgnt-1 . 1 mutants . This suggests a potential alteration in the ability of the sheath cell to take up , or intracellularly distribute dye when BGNT-1 . 1 is disrupted . In humans , mutations in B3GNT1 cause Walker-Warburg syndrome . Given that mutations in B3GNT1 lead to WWS and that it is classified as a dystroglycanopathy [14 , 15] , a group of muscular disorders whose etiology is hypothesised to be caused by aberrant glycosylation of dystroglycan [35] , we tested whether or not the C . elegans dystroglycan homologs , dgn-1 , dgn-2 and dgn-3 , exhibited dye-filling phenotypes . We find that all dgn mutants exhibit dye-filling indistinguishable from wild-type worms ( S8 Fig ) , indicating that BGNT-1 . 1 function in dye-filling is likely independent of dystroglycan . Interestingly , as highlighted earlier , the WWS congenital muscular dystrophy exhibits 6 features beyond muscle structure/function disruption which are core ciliary disorder ( ciliopathy ) features [5] . Our findings that C . elegans bgnt-1 . 1 is expressed in glial cells directly associated with , and necessary for the function of ciliated sensory neurons , is consistent with its role in cilium-dependent dye-filling .
We confirmed that bgnt-1 . 1 , a gene identified by SKAT as being associated with the dye-filling phenotypes but not previously implicated in cilia or amphid-sensillum function , is a bona fide dye-filling gene . We observed that: ( 1 ) two missense mutations in bgnt-1 . 1 result in severe dye-fill defects in three MMP strains , and three CRISP-Cas9-mediated bgnt-1 . 1 gene disruptions also cause dye-fill defects; ( 2 ) a fosmid containing full-length wild-type bgnt-1 . 1 rescues the dye-filling phenotype in bgnt-1 . 1 mutants; ( 3 ) the dye-filling phenotypes in the MMP strains with mutations in bgnt-1 . 1 map to the bgnt-1 . 1 locus; ( 4 ) BGTN-1 is expressed in sheath cells , which are directly implicated in dye-filling; and finally; and ( 5 ) mutations in bgnt-1 . 1 result in a small but statistically significant ciliary length defect . Together , these data strongly indicate that BGNT-1 . 1 , which we find localises as expected to the trans-Golgi network , functions in sheath ( glia-like ) cells to influence dye-filling . The power of genome-wide rare-variant association analysis ( e . g . , SKAT ) augments as the number of strains increases ( the probability of additional mutations in specific genes is increased ) , and thus , screening the entire MMP library would likely uncover many additional genes associated with dye-filling defects . To try to assess the minimal number of strains that should be assayed with this approach we performed a power analysis . Raw amphid dye-filling phenotype and genotype data was randomly sub-sampled ( without replacement ) and analysis was performed via SKAT with , and without , biologically relevant weights . This was done 100 times for each sample size ( 50 , 100 , 200 , 300 , 400 ) . For each sample size , power was calculated as the proportion of times the analysis found at least one gene to be significantly associated with the phenotype . We find that there is 40% power to detect a single gene as being associated with the amphid dye-filling phenotype at N = 400 for both SKAT with and without biologically relevant weights ( S9 Fig ) . Thus , we recommend that future studies using this method should use a sample size close to what was used in this present study ( ~ 500 ) to maximize the probability that one or more gene ( s ) will be found that is significantly associated with the phenotype of interest . We performed SKAT analyses via two methods , 1 ) while applying biologically relevant weights to the variants ( S3 & S4 Tables ) , and 2 ) while weighting all variants equally ( S5 & S6 Tables ) . SKAT analysis of the 480 strains without weights was less powerful , and resulted in identifying only 3 genes as being significantly associated with the amphid dye-filling phenotype and 1 gene as being significantly associated with the phasmid dye-filling phenotype; compared to 5 genes and 3 genes , respectively , when biologically relevant weights were used . However , there appears to be no difference in power when SKAT is performed with or without weights at smaller sample sizes ( S9 Fig ) . Thus , to maximize the ability to detect genes associated with the phenotype of interest , in addition to recommending a minimum sample size of ~ 500 , we also recommend assigning biologically relevant weights when using SKAT with the MMP library . The weight assignment could be simple , as done here , or more complex , calculating , for example , the SIFT [36] or Polyphen-2 [37] scores for assessing the severity of each variant in the MMP library . The genome-wide statistical genetic approach presented here has several advantages over traditional screening approaches . It generates a prioritised list of candidate genes likely responsible for the phenotype of interest . After this list is generated via screening and SKAT analysis , candidates can be tested for their causality of the phenotype through several standard genetic approaches in C . elegans . Candidates could be confirmed , for example , by ( i ) testing for the phenotype in knock-out mutants or RNAi; ( ii ) genetic rescue experiments; ( iii ) performing a genetic complementation test between two loss of function alleles; or , ( iv ) mapping the mutation to the gene locus . This strategy may work for phenotypes where the traditional polymorphic SNP-mapping strain , CB4856 , diverges from the reference wild-type strain , N2 , from which the MMP library was generated [1] , as well as partially-penetrant or other difficult-to-score phenotypes . In the case of bgnt-1 . 1 , we performed genetic rescue experiments , SNP mapping and created CRISPR-Cas9 knockout strains to support our the SKAT findings , which together confirm that bgnt-1 . 1 mutations cause dye-filling defects . Another potential extension and utility of this approach that could work for some ( non-neural ) phenotypes would be pairing the screening of the MMP strains with RNAi to look for enhancing , suppressing or synthetic phenotypes , and then using SKAT to prioritise a list of candidate genes . Furthermore , as more data from multiple phenotypes are collected on the MMP strains , these could be combined to perform multi-variate genome-wide statistical analysis on whole-genome sequence data . Such approaches are more powerful than univariate approaches in the case of SNP array data [38–40] , and such tests can also indicate which variants are pleiotropic , or specific to a single phenotype . How to perform this multivariate phenotype analysis on whole-genome sequences is currently an active area of research and tools to make this possible are being developed , with [41] looking promising . There are also challenges and limitations to the statistical genetic approach presented here . First , this approach of performing a “medium”-scale screen of the MMP strains is limited to assays that can be done without genetic manipulation of the strains . For example , introducing a functional ‘reporter’ ( transgene ) into 480 strains would require a prohibitive amount of work , although this has been done for 90 MMP strains [42] . Second , the statistical analysis presented here is only possible for genes which have > 1 variant in the population of worms screened . In practice , we found it works optimally for genes with at least 7 variants . This is due to the distribution of p-values when attempting to control for multiple testing; in our dataset , fewer than 7 variants led to a skewed p-value distribution and an inflation of False-discovery rate adjusted p-values . This strict rule demanding high-coverage for our SKAT analysis leads to only 1150 genes in the 480 MMP strains being considered here . This is due to the distribution of variant counts per gene in the MMP strains ( S10 Fig ) , which exponentially decreases from 1 to N . How disrupting BGNT-1 . 1 abrogates dye filling remains uncertain . One possibility is that the glycosyltransferase regulates the association of cilia with the sheath and socket glial-like cells which envelop them ( S1 Fig ) . Specifically , we hypothesise that BGNT-1 . 1 functions in the trans-golgi network of the amphid and phasmid sheath to glycosylate key unidentified protein ( s ) important for the association of this sensillum organ . This defect will not be visible at the level of light microscopy , and could perhaps result from changes to the lamellar membrane that surround the amphid/phasmid cilia , or the secreted extracellular material lining these channels . Which substrate ( s ) the β1 , 3-N-acetylglucosaminyltransferase , BGNT-1 . 1 ( B3GNT1 ) , glycosylates , and how this influences sensory neuron/glial cell development and function , remains to be determined in a future , detailed study of the gene . In conclusion , we demonstrated the utility and efficiency of using deep-sequenced multi-mutant strains in combination with SKAT to rapidly uncover novel genes required for a biological process of interest—here , ciliated sensory neuron development and/or function . The role of BGNT-1 . 1 in this process , seemingly independent of dystroglycan , supports the notion that B3GNT1/B4GAT1-associated Walker-Warburg syndrome may result at least in part from ciliary dysfunction , and thus could be considered a novel ciliopathy . Our findings also underscore the importance of identifying novel dye-filling genes , some of which might be implicated in human ciliopathies . For all new putative dye-filling genes highlighted in this study , we had no prior knowledge of their importance in ciliated sensory neuron function , and may not have ( easily ) uncovered them using alternative methods . Our approach therefore reduces the hurdle of traditional forward genetic methods , namely identifying the causative allele , and improves upon reverse genetics by allowing high gene/mutation coverage in a relatively small number of strains . Lastly , we propose that our approach is applicable not only for C . elegans , but any organism with a small genome that can be quickly sequenced and where numerous mutant strains can be isolated and phenotyped with relative ease , including Drosophila and Arabidopsis .
Worms were cultured on Nematode Growth Medium ( NGM ) seeded with Escherichia coli ( OP50 ) at 20°C as described previously [43] . The following strains were obtained from the Caenorhabditis Genetics Center ( University of Minnesota , Minneapolis , MN ) : N2 Bristol , CB4856 , CH1869 , CH1878 and PR813 . VC2010 , the wild-type reference strain used during the dye-filling screen , was derived from N2 [44] . The Million Mutation Project strains were isolated and their genomes’ sequenced by Thompson et al . [1] . The 480 Million Mutation Project strains used in this study are listed in S1 Table . For native rescue of VC20628 bgnt-1 . 1 ( gk361915 ) , 25 ng/μl of fosmid WRM065bB05 containing bgnt-1 . 1 was injected into bgnt-1 . 1 mutants along with 80 ng/μl of pRF4 rol-6 ( su1006dm ) as a co-injection marker . bgnt-1 . 1 ( gk361915 ) ; Ex[CHE-2::GFP; pRF4] was created by crossing bgnt-1 . 1 ( gk361915 ) with wild-type worms expressing Ex[CHE-2::GFP; pRF4] . The translational Psrh-220::IFT-20::tdTomato fusion was generated as described in [11] , except that tdTomato was used in place of GFP . 1 μl of the PCR product was microinjected into germline of gravid worms along with a co-injection markers ( pRF4 rol-6 ( su1006dm ) , final concentration of 100 ng/μl ) . Stable lines expressing this extrachromosomal array were crossed into DM13283 dpy-5 ( e907 ) ; sIs12964[Pgrd-15::GFP; pCeh361] to create the strain MX1924 dpy-5 ( e907 ) ; Ex[Psrh-220::IFT-20::tdTomato; pRF4]; sIs12964[Pgrd-15::GFP; pCeh361] . bgnt-1 . 1 ( gk361915 ) was also introduced to this line via genetic crossing to create MX2236 bgnt-1 . 1 ( gk361915 ) ; dpy-5 ( e907 ) ; Ex[Psrh-220::IFT-20::tdTomato; pRF4]; sIs12964[Pgrd-15::GFP; pCeh361] . The BGNT-1 . 1::GFP recombineered fosmid construct ( Construct # 6821068113870966 H08 ) was obtained from the TransgeneOme ( https://transgeneome . mpi-cbg . de/transgeneomics/index . html ) ) . To generate a strain expressing this construct , 25 ng/μl of the BGNT-1 . 1::GFP recombineered fosmid was injected into N2 worms along with 4 ng/ul of Posm-5::XBX-1::tdTomato as a cilia-marker , and 80 ng/μl of pRF4 rol-6 ( su1006dm ) as a co-injection marker . VC3671 ( gk3637 ) , VC3674 ( gk3638 ) , and VC3675 ( gk3639 ) for bgnt-1 . 1/F01D4 . 9 were generated using the CRISPR-Cas9 system as described by [32] in an N2 VC2010 background [1] . The 20 bp guide sequence for bgnt-1 . 1 was designed to include a 3’GG motif , as guides with GG at the 3’ end are purported to give higher integration efficiency [45] . 500 bp homology arms ( ordered as gBlocks from IDT ) were designed to flank exons 1 and 2 of bgnt-1 . 1 . The homology arms were inserted into a Pmyo-2::GFP-neoR-loxP disruption/deletion vector ( provided by the Calarco Lab ) using Gibson Assembly . The guide sequence and homology arms sequences are available in S7 Table and S8 Table , respectively . Of the three null mutations , gk3637 was generated using purified Cas9 protein , while gk3638 and gk3639 were generated using a plasmid-encoded version of the protein . The Cas9 protein was prepared according to the procedure described in [46] Paix et al . ( 2015 ) . The protein injection mix was assembled as described in [46] and used tracrRNA and bgnt-1 . 1 crRNA ordered from IDT . Putative integrants were validated by generating PCR amplicons spanning the junction between genomic DNA and the inserted cassette . Primer F01D4 . 9-1-L was used in conjunction with primer pMyo-2-SEC to validate the region just upstream of the putative insertion . This generated a 1075 bp product that covers genomic DNA as well as a region within the insertion . Primer F01D4 . 9-1-R was used in conjunction with primer NeoR-SEC to validate the region just downstream of the putative insertion . This generated a 1632 bp product that covers genomic DNA as well as a region within the insertion . Sanger sequencing of the PCR amplicons was conducted by the Nucleic Acid Protein Service Unit ( NAPS , UBC ) . To rough-map the dye-filling defects of Million Mutation Project strains to an arm of a chromosome we used the high-throughput SNP mapping approach created by Davis et al . The following SNPs used by Davis et al . [47] were omitted from our analysis because the whole genome sequence data from Thompson et al . [1] could not safely deduce that the SNPs from parental strain subjected to mutagenesis , VC2010 ( from which the Million Mutation Project strains were generated ) , matched those of Bristol N2 but not Hawaiin CB4856 ( mapping strain ) : W03D8 , F58D5 , T01D1 , Y6D1A , Y38E10A , T12B5 , R10D12 , F11A1 , and T24C2 . Dye-filling assays were performed using the fluorescent dye DiI ( Molecular Probes; DiIC18 Vybrant DiI cell-labelling solution , diluted 1:1000 with M9 buffer ) . Mixed stage C . elegans cultures were stained for 30 minutes , and Dil uptake into the amphid and phasmid neurons was visualised using either a Zeiss fluorescent dissection scope ( dye-filling screen ) or spinning disc confocal microscope ( WaveFX spinning disc confocal system from Quorum Technologies ) using a 25X oil ( N . A 0 . 8 ) objective and Hammamatsu 9100 EMCCD camera . Volocity software ( PerkinElmer ) was used for acquisition . The completely dye-filling defective ( dyf ) mutant strain PR813 osm-5 ( p813 ) was used as a positive control for the dye-filling phenotype . For the dye-filling screen , two plates of mixed-stage C . elegans were dye-filled for each Million Mutation Project strain , and defects were quantified by counting the number of worms exhibiting amphid and/or phasmid dye-filling defects . A worm was classified to have a dye-filling defect if: i ) no fluorescence was observed , ii ) fluorescence was observed to be greatly reduced ( minimum of an estimated 3x fluorescence reduction compared to wild-type staining from the experiment at the same magnification and laser intensity ) and/or iii ) fluorescence staining pattern was abrogated ( e . g . , accumulations of fluorescence at tips of dendrites with little to no staining in cell bodies ) . Fifteen worms were scored from each plate . If the dye-filling of a Million Mutation Project strain appeared qualitatively dimmer than wild-type worms across both plates or if ≥ 25% of the population exhibited a dye-filling defect the assay was repeated for that strain . A Fisher’s exact test followed by p-value adjustment using false discovery rate of 5% ( Benjamini–Hochberg procedure ) was used to if they exhibited a significant dye-fill defect compared to wild-type ( N2 ) . This was done separately for both amphids and phasmids . For visualisation of fluorescent-tagged proteins , worms were immobilised in 1μl of 25mM levamisole and 1μl of 0 . 1μm diameter polystyrene microspheres ( Polysciences 00876–15 , 2 . 5% w/v suspension ) on 10% agarose pads and visualised under a spinning disc confocal microscope ( WaveFX spinning disc confocal system from Quorum Technologies ) using a 100X oil ( N . A 1 . 4 ) objective and Hammamatsu 9100 EMCCD camera . Volocity 6 . 3 was used to deconvolve images as well as measure ADL cilia length and distal tip of ADL cilia to distal end of amphid socket cell length . The researcher was blind while performing the quantisation of ADL cilia/dendrite phenotypes . Worms were permeabilised , fixed and stained according to standard methods [48] . To mark the cis-Golgi , two anti-SQL-1 antibodies , one directed against the N terminus of SQL-1 and one affinity purified antibody against the C terminus of SQL-1 , were used . These antibodies have been characterised previously [33] . Both were visualised with secondary goat-anti rabbit Alexa 594 ( Molecular Probes , Eugene , OR; 1:800 ) . Localisation of BGNT-1::GFP and SQL-1 was imaged using a SpinD1454 Roper/Nikon spinning disk microscope with a 100x objective . We performed SKAT using the SKAT package ( version 1 . 0 . 9 ) [2] in R ( version 3 . 2 . 4 ) . No covariates were used . Given that the MMP library was created via random mutagenesis of the same isogenic parental strain [1] we did not have to control for population stratification . We chose to perform SKAT using a linear regression framework to take full advantage of the proportion data we had collected , as the logistic regression framework for SKAT only allows for a dichotomous response variable . To apply a linear regression framework to our proportion data we added a small constant to all the data points for the response variable , and then log transformed them . We used probability plots to choose the best constant ( S2 & S3 Figs ) , and thus used a constant of 0 . 005 for the amphid phenotype data , and a constant of 0 . 05 for the phasmid phenotype data . Custom , biologically relevant weights were assigned to the variants . Nonsense , splicing mutations and frameshift causing deletions were assigned a weight of 1 , in-frame deletions were assigned a weight of 0 . 75 , and missense mutations were assigned a weight of 0 . 25 . Gene-based tests for all genes with a minor allele count > 6 were performed . A false discovery rate ( Benjamini-Hochberg procedure ) of 5% was used to determine genes which were significantly associated with the phenotype . Make , Bash , Perl and R scripts used to perform the analysis can be found at: https://github . com/ttimbers/Million-Mutation-Project-dye-filling-SKAT . git To estimate power and recommend a minimum sample size for future experiments we performed a bootstrap power analysis using the amphid dataset . To do this , we randomly sampled ( resampling = FALSE ) N strains from the dataset we collected , and performed the SKAT analysis presented in this paper . We did this 100 times for N = 50 , 100 , 200 , 300 and 400 . We then estimated power as the proportion of times we observed a gene to be significantly associated with the phenotype . This was also done for two , three , four and five genes . The code used to perform this analysis can also be found in the Github repository for this study: https://github . com/ttimbers/Million-Mutation-Project-dye-filling-SKAT . git Protein sequences ( obtained from: http://www . cazy . org/ ) were aligned using MUSCLE 3 . 7 [49] . The phylogenetic tree was built using PhyML 3 . 0 aLRT [50] and viewed using FigTree version 1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . | Model organisms are useful tools for uncovering new genes involved in a biological process via genetic screens . Such an approach is powerful , but suffers from drawbacks that can slow down gene discovery . In forward genetics screens , difficult-to-map phenotypes present daunting challenges , and whole-genome coverage can be equally challenging for reverse genetic screens where typically only a single gene’s function is assayed per strain . Here , we show a different approach which includes positive aspects of forward ( high-coverage , randomly-induced mutations ) and reverse genetics ( prior knowledge of gene disruption ) to accelerate gene discovery . We paired a whole-genome sequenced multi-mutation C . elegans library with a rare-variant associated test to rapidly identify genes associated with a phenotype of interest: defects in sensory neurons bearing sensory organelles called cilia , via a simple dye-filling assay to probe the form and function of these cells . We found two well characterised dye-filling genes and three genes , not previously linked to ciliated sensory neuron development or function , that were associated with dye-filling defects . We reveal that disruption of one of these ( BGNT-1 . 1 ) , whose human orthologue is associated with Walker-Warburg syndrome , results in abrogated uptake of dye and cilia length defects . We believe that our novel approach is useful for any organism with a small genome that can be quickly sequenced and where many mutant strains can be easily isolated and phenotyped , such as Drosophila and Arabidopsis . | [
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] | 2016 | Accelerating Gene Discovery by Phenotyping Whole-Genome Sequenced Multi-mutation Strains and Using the Sequence Kernel Association Test (SKAT) |
Prader-Willi syndrome ( PWS [MIM 176270] ) is a neurogenetic disorder characterized by decreased fetal activity , muscular hypotonia , failure to thrive , short stature , obesity , mental retardation , and hypogonadotropic hypogonadism . It is caused by the loss of function of one or more imprinted , paternally expressed genes on the proximal long arm of chromosome 15 . Several potential PWS mouse models involving the orthologous region on chromosome 7C exist . Based on the analysis of deletions in the mouse and gene expression in PWS patients with chromosomal translocations , a critical region ( PWScr ) for neonatal lethality , failure to thrive , and growth retardation was narrowed to the locus containing a cluster of neuronally expressed MBII-85 small nucleolar RNA ( snoRNA ) genes . Here , we report the deletion of PWScr . Mice carrying the maternally inherited allele ( PWScrm−/p+ ) are indistinguishable from wild-type littermates . All those with the paternally inherited allele ( PWScrm+/p− ) consistently display postnatal growth retardation , with about 15% postnatal lethality in C57BL/6 , but not FVB/N crosses . This is the first example in a multicellular organism of genetic deletion of a C/D box snoRNA gene resulting in a pronounced phenotype .
The human genetic locus 15q11-q13 is subject to genomic imprinting that is controlled by a bipartite imprinting center ( IC ) [1] . Imprinting defects or chromosomal rearrangements/deletions within this locus ( Figure 1 ) are responsible for the development of two clinical disorders—Angelman ( AS ) and Prader-Willi ( PWS ) syndromes . AS ( MIM 105830 ) is a complex neurogenetic disorder characterized by mental retardation , severe limitations in speech and language and abnormal behavior , and results from loss of maternal expression of the Ube3A gene [2] . PWS ( MIM 176270 ) is a complex neurogenetic disorder with a population prevalence of 1 in 10 000 to 50 000 [3–5] that is characterized by decreased fetal activity , muscular hypotonia , failure to thrive , short stature , obesity , mental retardation , and hypogonadotropic hypogonadism , for review , see [2 , 6] . PWS results from lack of paternal expression of one or several imprinted genes within the PWS/AS locus . Several paternally expressed protein-coding genes map to this locus , including NECDIN ( NDN ) , MAGEL2 , MKRN3 , and the bi-cistronic SNURF-SNRPN ( Figure 1B ) . There are also numerous paternally expressed C/D box snoRNA genes located downstream from the SNURF-SNRPN gene . Most of them are organized into two main clusters of HBII-85 and HBII-52 snoRNAs , containing 29 and 47 copies , respectively . Other snoRNAs are present as single ( HBII-436 , HBII-13 and HBII-437 ) or double copy ( HBII-438a/438b ) genes ( Figure 1B ) . Most , if not all , snoRNAs are processed from a long , non-protein-coding RNA ( npcRNA ) transcript designated U-UBE3A-ATS in human and Lncat ( large paternal non-protein-coding RNA , encompassing Snurf-Snrpn and Ipw exons together with the Ube3a antisense transcript ) in mouse [7–9] . U-UBE3A-ATS extends ∼450 kb from the untranslated U exons upstream of the small nuclear ribonucleoprotein N ( SNURF/SNRPN ) gene to the UBE3A gene ( Figure 1B ) . In mouse the syntenic PWS/AS locus is located on chromosome 7C and contains all the aforementioned protein coding and non-protein-coding gene orthologs , except for the presence of protein-coding gene Frat 3 and absence of HBII-437 and HBII-438a/438b snoRNA genes ( Figure 1C ) . Several mouse models for PWS have already been generated ( Figure 1D ) . They can be divided into 3 groups: 1 ) transgenic mouse lines with disruptions of the PWS/AS locus; 2 ) mice with targeted elimination of the imprinting center ( IC ) controlling transcription of PWS genes , or targeted elimination of individual , single genes from the PWS locus; and 3 ) mice with uniparental paternal disomy ( UPD ) [10 , 11] . In all existing PWS mouse models ( Figure 1D ) that involve large deletions comprising several paternally expressed , imprinted genes , severe phenotypes ( failure to thrive and early postnatal lethality ) were observed [10 , 12–15] . Targeting of the single Mkrn3 and Snrpn genes , or some of the Ipw exons together with the MBII-52 snoRNA genes cluster [13 , 15–17] or deletion of an analogous region in human [18] all produced no PWS-like phenotypes . Elimination of the Magel 2 gene caused altered behavioral rhythmicity ( Figure 1D8 ) [19] . However this gene is unlikely to be a main “PWS player” [20] . Currently , only elimination of the single Necdin gene leads to the development of early postnatal lethality and neurological abnormalities resembling the PWS , although the phenotypic effect depends on the targeted region and genetic background of the mice , some of which had no apparent phenotype [21–23] . The PWS critical region ( PWScr ) was narrowed to the locus containing the MBII-85 small nucleolar RNA ( snoRNA ) gene cluster based on existing mouse models [16] and gene expression analysis in PWS patients with chromosomal translocations [18 , 24 , 25] . These studies indicate that SNURF/SNRPN , MKRN3 , NECDIN and MAGEL2 genes are unlikely to play a primary role in the pathogenesis of PWS . However , the question of their possible functional contribution to more severe phenotypic expression seen in typical PWS patients remains open [16 , 18 , 24] . We have applied the “chromosome engineering” technique [26] to delete the PWScr in mice . When the deleted allele is inherited maternally ( PWScrm−/p+ ) , no phenotypic abnormalities are visible . When it is inherited paternally ( PWScrm+/p− ) , we consistently observe postnatal growth retardation in mice and less than 15 percent postnatal lethality in 129SV x C57BL/6 genetic crosses .
To test the hypothesis that the PWScr is the most probable candidate region for neonatal lethality , failure to thrive and postnatal growth retardation , we devised the following strategy for producing PWScrm+/p− mice ( Figure 1E and 1F ) . Hypoxanthine-guanine phosphoribosyltransferase ( HPRT ) -deficient ES cells , AB2 . 2 , were modified through homologous recombination ( HR ) using targeting constructs 5′HPRT/PWScr_targ and 3′HPRT/PWScr_targ to place loxP sites proximal to the 5′ flanking region of the MBII-85 snoRNA gene cluster and distal to the Ipw exon C , respectively ( Figure 1E ) . Deletion of the PWScr harboring the entire cluster of MBII-85 snoRNA genes together with Ipw exons A-C was accomplished by expressing CRE recombinase in ES-targeted cells ( Figures 1F and 2A ) and injecting these into blastocysts . We identified two chimaeras derived from one of the PWScr-deleted ES clones with successful germ-line transmission . Deletion of the PWScr allele was confirmed several ways: 1 ) Resistance of ES cells to HAT media requires a functional HPRT gene . Restoring the intact HPRT gene through deletion of the PWScr leads to resistance of ES cells to HAT media . 2 ) Southern blot analysis of all HAT-resistant ES colonies ( data not shown ) , as well as all PWScr-deleted mice , using 5′HR and 3′HR probes ( Figure 2A and 2B ) , revealed identical bands corresponding to the correctly deleted PWScr allele ( 19998 bp ) . 3 ) We PCR amplified and sequenced flanking regions of the PWScr locus together with the inserted HPRT gene ( Figures 1G , 2C , and S1 ) using PCR primers MB85seqD1 and MB85seqR1 ( Table S1 ) , and confirmed the deleted PWScr allele as well . PWScrm+/p− pups born from chimaeras were significantly smaller than their wild-type siblings , such that on postnatal day 10 , the PWScrm+/p− individuals ( Figure 3 ) could be reliably predicted prior to DNA analysis . We monitored the weights of all mice over several weeks after continuous breeding and found that only the PWScrm+/p− and PWScrm−/p− mice displayed postnatal growth retardation compared to the PWScrm+/p+ siblings . Statistical analyses were performed on mice separated by genotype , genetic background and gender ( Figures 4A–4D and S2 ) , as well as separately for litters ( data provided upon request ) . Postnatal growth retardation in PWScrm+/p− mice was observed , thus far , over six generations , independent of genetic background [e . g . , 129SV x C57BL/6 ( >85% C57BL/6 genetic background contribution , Figure 4A and 4B ) , 129SV x C57BL/6 x FVB/N ( ∼50% FVB/N contribution , Figure S2A and S2B ) , and 129SV x C57BL/6 x BALB/c ( ∼50% BALB/c contribution , Figure S2C and S2D ) ] . Differences in growth dynamics between PWScrm+/p+ and PWScrm−/p− or PWScrm+/p− mice continued to be statistically significant into adulthood ( up to 1 year in BALB/c crosses ) ( Figure 4A , 4B , and S2 ) . Moreover , not a single case of obesity was detected . Interestingly , when growth dynamics for mice were analyzed by gender , deficiencies in the PWScrm+/p− or PWScrm−/p− female mice tended to be less pronounced than those in the male deletion mice ( Figure 4A , 4B , S2C , and S2D ) . This observation is well correlated with a statistical analysis of PWS patients indicating that the degree of short stature is more prominent in males than in females , irrespective of ethnic groups ( genetic background ) [27] . Furthermore , mice with the maternally transmitted PWScr-deleted allele were indistinguishable from their wild type littermates ( Figure 4C ) . In addition , males ( 5′HPRT ) derived from 5′HPRT/PWScr_targ-targeted ES cells were crossed with C57BL/6 females . As expected , we did not observe growth retardation in the 5′HPRTm−/p+ mice ( p=0 . 275 ) ( Figure 4D ) . Notably , the observed postnatal growth retardation phenotype became apparent during the first week of life starting from postnatal day 5 in males and 6 in females ( Figure 4A and 4B; Table S2 ) , while there were no growth differences at early postnatal ages P1-P4 ( Figure 4A and 4B; Table S2 ) . In agreement with the absence of early postnatal growth retardation , no weight differences were observed during embryonal development at E12 . 5 , E15 . 5 and E18 . 5 , or in late gestation ( E15 . 5 and E18 . 5 ) placentas ( Figure 4E , and data not shown ) . Hence , one possible explanation for our observations might be poor breast-feeding in PWScrm+/p− pups as is the case with postnatal PWS patients . Insufficient milk intake resulting in growth retardation is consistent with the Holland hypothesis and observations in TgPWS mice ( transgenic deletion PWS mouse model; Figure 1D1 ) suggesting , that the main basis of the PWS syndrome is not obesity and uncontrollable craving but early postnatal starvation [28 , 29] . The PWScrm+/p− mice exhibit postnatal growth retardation , but contrary to early predictions [16] postnatal lethality was observed in only 15 percent of the cases in 129SV x C57BL/6 genetic crosses ( Figure 4F ) . On the other hand , we did not observe postnatal lethality in the FVB/N crosses . PWScrm+/p− mice were dying from postnatal days 1 to 22 . The surviving PWScrm+/p− and PWScrm−/p− mice are alive and apparently well for at least 1 year . In future experiments , it would be interesting to test the possible role of MBII-13 and MBII-436 snoRNAs and/or the genomic region between the Snrpn and MBII-85 genes for their contributions to more severe phenotypes with higher rates of postnatal lethality . In addition , our PWScrm+/p− mouse model is in good agreement with two recently identified patients with a balanced chromosomal translocation involving SNRPN [24 , 30] . Both patients lack HBII-85 expression and exhibit a mild PWS phenotype , ( e . g . , they did not require gavage feeding , but were growth retarded . ) PWS is also characterized by hypogonadotropic hypogonadism and infertility in patients [6] . We studied the fertility of the PWScrm+/p− and PWScrm−/p+ mice and observed that PWScrm+/p− males and females transmitted the PWScr deleted allele to offspring . To further extend our observations , we established 10 breeding pairs from each of the PWScrm+/p− and PWScrm−/p+ males with 80 wild-type females . All matings resulted in pregnancies leading to successful live births with litter sizes around eight ( 7 . 93 ± 0 . 46 for PWScrm+/p− and 8 . 33 ± 0 . 48 for PWScrm−/p+ males ) , and always included wild-type mice and mice carrying the PWScr-deleted allele in a ratio of 1:1 ( 4 . 17 ± 0 . 34 : 3 . 66 ± 0 . 38 for PWScrm+/p− and 4 . 00 ± 0 . 54 : 4 . 17 ± 0 . 53 for PWScrm−/p+; Table S3 ) . Thus , male mice containing the PWScr-deleted allele inherited maternally or paternally , are transmitting this allele in a Mendelian fashion . The PWS locus includes several paternally expressed , protein coding genes , including Necdin , Magel2 , Mkrn3 , Frat3 , and the bi-cistronic Snurf-Snrpn . To examine whether the deletion of the paternal PWScr from mouse chromosome 7C perturbed the expression of the aforementioned imprinted genes , we analyzed their expression levels in our mice by RT-PCR and real-time PCR . We failed to detect any significant differences in the expression levels of the investigated genes in PWScrm+/p− mice compared to control littermates PWScrm+/p+ ( Figure 5A; Table 1 ) . In addition , the controversial data concerning the involvement of Necdin in the PWS phenotype [21–23] prompted us to also examine its expression in more detail by Northern blot hybridization ( Figure 5B ) . Consistent with the RT-PCR and real-time PCR results , there were no differences in the levels of Necdin mRNA in brains of the PWScrm+/p− , PWScrm−/p+ or control PWScrm+/p+ , 5′HPRTm+/p+ and 3′HPRTm+/p+ mice ( Figure 5B; Table 1 and data not shown ) . We also examined the levels of the maternally expressed , imprinted Ube3a and Atp10a protein coding genes , located at the end of the PWS locus and involved in the development of AS [2] ( Figure 1B and 1C ) . The RT-PCR ( Figure 5A ) , real-time PCR ( Table 1 ) , and Northern blot hybridization ( Figure 5C and 5D ) analyses revealed similar expression levels of both genes in brains of PWScrm+/p− and PWScrm−/p+ , as well as in control mice . The mouse PWS locus on chromosome 7C also contains numerous neuronal , paternally expressed C/D box snoRNA genes , including two single copies of MBII-436 and MBII-13 , and two multiple copy clusters , MBII-85 and MBII-52 [7 , 31] . Vertebrate snoRNAs are embedded in introns of protein coding genes , which are posttranscriptionally processed to yield mature mRNA and snoRNA . Occasionally the spliced exons are devoid of open reading frames , as if the sole function of the transcript is the expression of the snoRNA [32] . This is apparently the case for most , if not all , snoRNAs from the PWS locus that are co-transcribed with the large paternally expressed polycistronic Lncat npcRNA [7–9] . The Lncat transcript is complex and generates , by alternative splicing and other processing events , numerous RNA products ( e . g . , those represented by expressed sequenced tags ( ESTs ) and mature snoRNAs ) . The corresponding Ipw exons are a subset of Lncat-derived ESTs and map to the MBII-85 and MBII-52 snoRNA clusters that are interspersed with repeated exons A1/A2 and G1/G2 , respectively [31 , 33] . The Ipw exons B , C , H , E , F map between both clusters . Northern blot analyses of RNA samples extracted from brains of PWScrm+/p− mice revealed the complete absence of MBII-85 snoRNA while expression of all other snoRNA genes in the PWS locus were unaffected ( Figure 6A ) . We then analyzed the expression of the Ipw exons using Northern blot hybridization with a cDNA probe containing the F and G exons , and found it to be slightly decreased in PWScrm+/p− mice ( Figure 6B ) . However , expression of MBII-52 snoRNA was not altered , presumably because the primary Lncat transcript can undergo different processing pathways to yield mature MBII-52 snoRNA ( Figure 7 ) . Recently it was reported that lack of HBII-52 snoRNA genes together with most of the IPW exons did not result in a PWS phenotype in either human individuals [18] or in a mouse model [15 , 16] . Therefore , it is less likely that deletion of alternatively spliced Ipw exons A1/A2 , B and C are responsible for the phenotype we obtained here , although we cannot completely exclude the possibility that lack of those exons in a long npcRNA can contribute to it . In future experiments , we will address the question , whether expression of MBII-85 snoRNA in a different host gene is sufficient to compensate the observed phenotype . Lack of MBII-85 snoRNA expression in PWScrm+/p− compared to PWScrm−/p+ mice , along with the unaltered expression of paternally and maternally expressed genes from the PWS/AS locus in both heterozygous mice , indicates that deletion of PWScr does not affect imprinting of neighboring genes . Moreover , patients with translocations leading to a similar deletion encompassing the genes encoding HBII-85 and HBII-438A snoRNA exhibit normal biparental methylation patterns [24 , 30] . Thus , absence of MBII-85 snoRNA is the most likely cause for the phenotype observed in PWScrm+/p− and PWScrm−/p− mice ( Figure 6A ) . In Eukarya , most C/D box npcRNAs guide site-specific 2′-O-methylation in rRNAs and small nuclear RNAs ( snRNAs ) by complementarity to defined sites within these RNA targets [34] . However , most if not all C/D box small npcRNAs that map to the PWS locus , including MBII-85 and MBII-52 snoRNAs , lack significant complementarities to any rRNA or snRNA targets [7 , 31] . Although a major role for MBII-52 in the etiology of the PWS can be excluded , cell culture experiments have suggested that MBII-52 snoRNA might play a role in A to I editing and/or alternative splicing of the 5HT-2c serotonin receptor pre-mRNA [35 , 36] . In spite of these discrepancies , one might still be tempted to propose that MBII-85 snoRNA interacts ( by complementarity ) with a yet unidentified RNA target . However , other less orthodox functions for MBII-85 snoRNA and its role in postnatal growth retardation must also be seriously entertained . In any event , MBII-85 is the first example of a C/D box snoRNA gene , whose deletion results in an obvious phenotypic change in a multicellular organism . Future experiments might reveal additional , less obvious defects and deficiencies in PWScrm+/p− mice . Our mouse model will serve as an important tool for further investigations of the molecular pathogenesis of PWS in man .
For construction of the 5′HPRT/PWScr_targ and 3′HPRT/PWScr_targ targeting vectors , we isolated clones RPCIP711K19517Q6 and RPCIP711J18414Q6 , respectively , from the RPCI21 mouse PAC library ( RZPD German Resource Center for Genome Investigation ) using the MBII-85 oligonucleotide ( Table S1 ) . The 5′HPRT/PWScr_targ construct was generated using the 5′ flanking region of the MBII-85 gene cluster as a template for PCR-amplification of 1075 bp and 7387 bp DNA fragments with primer pairs 5′FLAdir/5′FLArev and 5′FLBdir/5′FLBrev , respectively . The PCR fragments were used as homologous arms in the targeting vector . The cassette containing the 5′ portion of the HPRT gene , the loxP site , and the neomycin resistance gene was subcloned from a λ phage vector kindly provided by A . Bradley ( Baylor College of Medicine , Houston , USA ) . The thymidine kinase ( TK ) gene was placed outside of the homologous arm ( Figure 1E ) . The 3′HPRT/PWScr_targ construct was cloned in a similar way . The 3′ flanking region of the MBII-85 gene cluster was used as a template to PCR-amplify 1098 bp and 6720 bp DNA fragments using primer pairs 3′FLBdirN/3′FLBrev and 3′FLAdir/3′FLArev , respectively . The insertion cassette , containing the 3′ portion of the HPRT gene , the loxP site , and the puromycin resistance gene was subcloned from the λ phage vector kindly provided by A . Bradley . The TK gene was placed outside of the homologous arm . The 5′ HR probe for Southern blot hybridization was generated by using the PAC DNA containing the 5′ region of the MBII-85 gene cluster in two PCR reactions with oligonucleotide pairs 5′PRAdir/5′PRArev and 5′PRBdir/5′PRBrev , and ligating and cloning the resulting PCR products into the pSL300 vector [37] . The 3′ HR probe was cloned in a similar fashion . PCR products were obtained with oligonucleotide pairs 3′PRAdir/3′PRArev and 3′PRBdir/3′PRBrev ( Table S1 ) . HPRT-deficient embryonic stem cells AB2 . 2 ( from A . Bradley ) , passage 17 , were expanded in HEPES-buffered , Dulbecco's modified Eagle's medium supplemented with 15% fetal bovine serum ( HyClone ) , nonessential amino acids , L-glutamine , β-mercaptoethanol , 1000 U/ml recombinant LIF ( Chemicon ) and antibiotics ( penicillin 100 U/ml and streptomycin 100 μg/ml ) on a γ-irradiated monolayer of SNL6 . 7 cells ( from A . Bradley ) or mouse primary fibroblasts . For electroporation , 2 × 107 ES cells were resuspended in 20 mM HEPES pH 7 . 4 , 173 mM NaCl , 5 mM KCl , 0 . 7 mM Na2HPO4 , 6 mM dextrose , and 0 . 1 mM β-mercaptoethanol [38] . The NotI linearized replacement targeting vectors 5′HPRT/PWScr_targ , 3′HPRT/PWScr_targ , and intact CRE expressing cassette pOG231 ( 55 μg DNA of each ) were electroporated at 25 μF and 400V ( Gene Pulser; Bio-Rad ) . After electroporation , cells were plated onto 100 mm culture dishes containing a γ-irradiated monolayer of primary , G-418-resistant , or SNL6 . 7 ( G-418 , puromycin- and HAT-resistant ) fibroblast feeder cells . Thirty-eight hours later , 350 μg/ml G418 ( Invitrogen ) and 0 . 2 μM 2′-deoxy-2′-fluoro-β-D-arabinofuranosyl-5-iodouracil ( FIAU ) ( Moravek Biochemicals and Radiochemicals , USA ) or 1 . 0 - 0 . 5 μg/ml puromycin ( Sigma ) and 0 . 2 μM FIAU , or HAT ( Sigma ) were added to the culture medium . The medium was replaced every day and colonies were picked and analyzed eight days after plating . 5′HPRT loxP-targeted ES cells were analyzed using oligonucleotide pairs 85–5′screen2d/85–5′screen2r and 85–5′screen3d/85–5′screen3r for a nested PCR approach . For analysis of 3′HPRT loxP-targeted cells we employed the 85–3′screen1d/85–3′screen1r and 85–3′screen2d/85–3′screen2r nested PCR primer pair combinations . DNA blot analysis was performed as described [39] . Membranes were hybridized with 32p−labeled 5′HR and 3′HR probes ( Figure 2A ) . Several independent ES clones containing the CRE-mediated PWScr-deletion were injected into 3 . 5-day-old B6D2F1 ( C57BL/6 x DBA ) blastocysts , and the resulting embryos were transferred to CD-1 foster mice . Chimeras were identified by their agouti coat color . The 5 . 7 kb DNA fragment containing flanking regions of PWScr together with the inserted HPRT gene was amplified and sequenced using PCR primers MB85seqD1 and MB85seqR1 . Sequencing reactions were performed using the BigDye terminator cycle sequencing reaction kit ( PE Applied Biosystems ) and resolved on an ABI Prism 3100 ( Perkin Elmer ) capillary sequencing machine . To genotype mice we performed PCR analysis of genomic DNA from tail biopsies using the primer pair MB85deld1/MB85delr1 ( Figure 2A and 2C; Table S1 ) . PCR cycling was done at 93 °C – 2 min; 7 cycles ( 93 °C – 40 sec , 70 oC – 20 sec , touch down −1 °C , extension 67 oC – 1 min 40 sec ) ; 35 cycles ( 93 °C – 40 sec , 55 oC – 20 sec , 67 oC – 1 min 40 sec ) . The final extension was performed at 67 °C for 5 min . Total RNA was isolated from mouse brains using TRIzol reagent ( Invitrogen ) according to the manufacturer's instructions . RNA samples , 20 μg each , were treated with RNase-free DNase I ( Roche ) . First strand cDNA synthesis was performed using Transcriptor reverse transcriptase ( Roche ) and hexamer oligonucleotides , followed by PCR amplification with gene specific oligonucleotides ( Table S1 ) . The cDNA probes for necdin , Ube3A , Atp10a mRNAs , and Ipw exons were PCR amplified , cloned in the pCRII vector ( Invitrogen ) , and subsequently sequenced using gene specific oligonucleotides ( Table S1 ) . Approximately 20 μg of total RNA was denatured , fractionated on 1 . 2% agarose formaldehyde gels , and transferred to GeneScreen nylon membranes ( NEN DuPont ) . Hybridization was performed with 32P-labeled cDNA probes . Northern blot analysis of snoRNAs was performed with specific oligonucleotides ( Table S1 ) as described [31] . A PWScr-deficient mouse line was established by breeding male chimeras nos . 2 and 5 from one mutant ES cell line with female C57BL/6 mice to produce heterozygous mice . Subsequently , heterozygous mice were interbred or bred to C57BL/6 mice . All breeding occurred at the ZMBE animal facility of the University Clinics , Münster in a controlled ( 21 °C , 30–50% humidity ) room with a 12:12 hour light-dark cycle , and mice were housed under non-enriched , standard conditions in individually ventilated ( 36 ( l ) x 20 ( w ) x 20 ( h ) cm ) cages for up to five littermates . Pups were weaned 19 – 23 days after birth and females were kept separately from males . Statistical analysis was performed using the StatView software package . Body weight was analyzed using Student's t-test , or ANOVA for each day of postnatal age . Weights of placentas and embryos were analyzed using Mann-Whitney nonparametric statistics . Postnatal lethality was analyzed with the chi-square test . | Prader-Willi syndrome , or PWS , is a complex neurogenetic disorder and the most common genetic cause of life-threatening childhood obesity . Newborns have poor muscle tone , making suckling difficult , which leads to poor weight gain . After infancy , they experience extreme hunger , leading to obesity . Other symptoms include short stature , mental retardation , and often infertility . In PWS patients , a complex set of genes on the paternal chromosome 15 ( in the PWS region ) is missing or unexpressed . In an attempt to understand this disorder , various protein-coding genes in this region have been deleted in mice , but none of the resulting phenotypes consistently correlated with the human disease . This region also contains a cluster of genes that encode functional non-protein-coding RNAs . We deleted specifically the MBII-85 small nucleolar RNA ( snoRNA ) gene cluster on the parental mouse chromosome , which did not affect expression of any of the other snoRNA or protein-coding genes in the PWS region . These mice consistently displayed postnatal growth retardation starting from day 5 to 6 , low postnatal lethality only in certain genetic backgrounds ( <15% ) , and no adolescent obesity . Thus , this mouse model , with the deletion of a small , brain-specific non-protein-coding RNA , should prove useful for teasing out the various molecular pathologies of PWS . | [
"Abstract",
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] | [
"mus",
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] | 2007 | Deletion of the MBII-85 snoRNA Gene Cluster in Mice Results in Postnatal Growth Retardation |
In Bombyx mori ( B . mori ) , Fem piRNA originates from the W chromosome and is responsible for femaleness . The Fem piRNA-PIWI complex targets and cleaves mRNAs transcribed from the Masc gene . Masc encodes a novel CCCH type zinc-finger protein and is required for male-specific splicing of B . mori doublesex ( Bmdsx ) transcripts . In the present study , several silkworm strains carrying a transgene , which encodes a Fem piRNA-resistant Masc mRNA ( Masc-R ) , were generated . Forced expression of the Masc-R transgene caused female-specific lethality during the larval stages . One of the Masc-R strains weakly expressed Masc-R in various tissues . Females heterozygous for the transgene expressed male-specific isoform of the Bombyx homolog of insulin-like growth factor II mRNA-binding protein ( ImpM ) and Bmdsx . All examined females showed a lower inducibility of vitellogenin synthesis and exhibited abnormalities in the ovaries . Testis-like tissues were observed in abnormal ovaries and , notably , the tissues contained considerable numbers of sperm bundles . Homozygous expression of the transgene resulted in formation of the male-specific abdominal segment in adult females and caused partial male differentiation in female genitalia . These results strongly suggest that Masc is an important regulatory gene of maleness in B . mori .
Most animal species have two sexes and display various sexual dimorphisms . Mechanisms of sex determination are highly different among phyla [1–3] . In many reptile species , the temperature at which eggs are incubated determines their sex [4] . In Daphnia magna , a shortening of the photoperiod , lack of food , and increase in population density leads to the production of males that are genetically identical to females [5] . Mammals show genotypic sex determination , with sex determined by the expression of a Y-linked gene , Sry [6] . In the African clawed frog , Xenopus laevis , DM-W located on the W chromosome induces female development [7] . Given the different sex-determining switches among species , genes from the Doublesex Mab-3 Related Transcription ( DMRT ) family are highly conserved as components of the vertebrate and invertebrate sex-determining pathways [8] . In insects , doublesex ( dsx ) is a well known Dmrt family gene . The dsx gene , which produces female- and male-specific transcripts by sex-specific alternative splicing , and is situated at the bottom of the sex determination cascade , has been reported in various insects [9] . In Drosophila melanogaster , female-specific splicing of dsx requires both TRA , a protein product of transformer ( tra ) whose functional isoform is produced only in females , and a protein product of transformer-2 ( tra-2 ) [10] . A similar function of tra has been reported in several dipteran , hymenopteran , and coleopteran insects . RNA interference ( RNAi ) -mediated knockdown of tra in Musca [11] , Ceratitis [12] , Lucilia [13] , Nasonia [14] , Bactrocera [15] , and Tribolium [16] caused male-specific splicing of the endogenous dsx pre-mRNAs , leading to masculinization of chromosomal females . Therefore , tra has been considered a consensus upstream regulator that directs female-specific splicing of dsx transcripts in insects . This hypothesis has now been challenged because tra is absent in several species belonging to four insect orders: Lepidoptera , basal Diptera , Strepsiptera , and Coleoptera [17] . A tra ortholog has not been identified in the silkworm , Bombyx mori ( B . mori ) , which is a model organism of lepidopteran insects . The female exon of the Bombyx homolog of dsx ( Bmdsx ) is devoid of putative TRA-TRA2-binding sites [18] . Moreover , RNAi knockdown of the Bombyx ortholog of tra-2 does not influence sex-specific splicing of Bmdsx pre-mRNA [19] . However , the male splicing of Bmdsx transcripts requires the splicing inhibitor ( BmPSI ) and the male-specific isoform of the Bombyx homolog of insulin-like growth factor II , mRNA-binding protein ( IMPM ) . These proteins form a complex that binds to a cis-regulatory element called CE1 , located in the female-specific exon , and inhibit the female mode of splicing in males [20 , 21] . These results support that the sex determination cascade of B . mori is different from the already known sex determination cascade . The femaleness of B . mori is predominantly determined by the presence of the W chromosome and a master regulatory gene at the top of the sex determination cascade thought to exist on the W chromosome [22] . We recently identified a W-linked gene , Feminizer ( Fem ) , which is the master regulatory gene for femaleness . Fem transcripts yield PIWI-interacting RNAs ( piRNAs ) , which are designated as Fem piRNAs and are responsible for femaleness [23] . piRNAs are 23–30 nucleotide ( nt ) -long small RNAs that act as sequence-specific guides for PIWI proteins that cleave target RNAs , mainly to disrupt the activity of transposons in gonads [24] . Fem piRNA-PIWI protein complex targets and cleaves mRNAs transcribed from the Z chromosome-linked gene that encodes a CCCH-tandem zinc-finger protein [23] . Knockdown of the expression of this gene in male embryos leads to the production of the female-type isoform of Bmdsx [23] and decreases the expression level of ImpM [25] . These results demonstrate that this Z-linked gene is essential for silkworm masculinization , which was named Masculinizer ( Masc ) . However , there has been no report showing that knockdown of Masc causes morphological changes in sexually dimorphic structures because knockdown of Masc causes male-specific embryonic lethality presumably due to defects in the gene dosage compensation of Z-linked genes [23] . To examine whether Masc controls male development , we performed ectopic expression analysis of Masc using transgenic silkworm strains . For this purpose , we created a transgene designed to allow expression of Fem piRNA-resistant Masc cDNA ( Masc-R ) under the control of the GAL4-UAS binary expression system [26] . Our results showed that forced expression of Masc-R induced female-specific lethality during the larval stage . Moreover , expression of Masc-R in females strongly repressed femaleness expressed in vitellogenin synthesis , egg production , and ovary formation and , surprisingly , caused spermatogenesis in a testis-like tissue ectopically formed in the ovary . Thus , Masc can induce maleness at the morphological level and is sufficient for spermatogenesis .
As reported previously , forced expression of Masc cDNA in B . mori ovary-derived cell line ( BmN4 ) induces male-specific variants of Bmdsx , but the masculinizing activity was relatively low due to the cleavage of Masc mRNA in the presence of Fem piRNA in female cells [23] . To overcome this problem , we utilized Masc-R cDNA ( Fig 1A ) that is resistant to Fem piRNA-mediated cleavage [23] . In this study , Masc-R cDNA was expressed using a GAL4-upstream activation sequence ( GAL4-UAS ) system specifically arranged for the silkworm [26] . Structure of the transformation vector carrying UAS-Masc-R was illustrated in Fig 1A . The resultant transgenic lines ( Sumi13-1 and 13–3 ) were viable and fertile; however , fertility of Sumi13-3 was very low . Southern blot analysis revealed that the UAS-Masc-R sublines carried only one copy of the transgene ( Fig 1B ) . Inverse PCR analyses showed that the UAS-Masc-R transgenes in Sumi13-1 and 13–3 were integrated into an autosomal region within chromosome 2 and chromosome 9 , respectively . As shown in Fig 1C , the insertions did not disrupt gene structures present near the insertion site . We examined the effects of Masc-R expression on female development . For this purpose , we crossed Sumi13-1 and Sumi13-3 males heterozygous for the UAS-Masc-R transgene with females that ubiquitously express GAL4 through B . mori cytoplasmic actin 3 promoter ( BmA3-GAL4 ) . The BmA3-GAL4 strain was maintained heterozygous for the BmA3-GAL4 transgene . The strain expressed DsRed as a selection marker , which enables identification of genotype by visualizing fluorescence in the eye . The hatch rate of F1 embryos derived from each crossing ( BmA3-GAL4 × Sumi13-1 and BmA3-GAL4 × Sumi13-3 ) was similar to that of control embryos ( Table 1 ) . These results demonstrated that forced expression of Masc-R did not affect embryogenesis . The number of F1 individuals at the fifth instar larvae , and the adult stages in each crossing , were counted . F1 individuals expressing the UAS-Masc-R transgene were obtained by selecting double fluorescent-positive animals that expressed both EGFP and DsRed ( S1 Fig ) . Reverse transcription ( RT ) -PCR analyses confirmed that forced expression of Masc-R occurred in the double fluorescent-positive animals ( Fig 2A ) . Interestingly , Sumi13-3 animals weakly expressed Masc-R independent of GAL4 induction . The survival rate of Masc-R-expressing animals ( genotype R/G in both BmA3-GAL4 × Sumi13-1 crossing and BmA3-GAL4 × Sumi13-3 crossing ) was significantly lower than that of GAL4-expressing animals with other genotypes ( BmA3-GAL4 ) ( Fig 2B ) . The survival curves shown in Fig 2B indicated that half of Masc-R-expressing animals were dead before the third instar larval stage . Notably , surviving animals expressing UAS-Masc-R transgene were all males ( genotype R/G in Fig 2C and genotype R/G in Fig 2D ) , while the sex ratio of animals with all other genotypes ( animals without Masc-R expression ) was approximately 50% ( genotype R/+ , G/+ , +/+ in Fig 2C and genotype R/+ , G/+ , +/+ in Fig 2D ) . These results strongly suggested that Masc-R expression caused female-specific lethality during larval and pupal development . To confirm this hypothesis , we determined the sex of all Masc-R-expressing individuals ( including dead ones ) by PCR with primers that specifically amplify the genomic region located on the W chromosome . The PCR-based sexing demonstrated that all surviving adult moths were male and that 89% of the dead larvae were female ( Fig 2E ) . No surviving males showed any abnormalities in sexual dimorphisms . These results indicated that forced expression of Masc-R caused female-specific lethality during larval stages . We next investigated the splicing pattern of Bmdsx in Masc-R-expressing females when they were still alive ( S2 Fig ) . In addition to the female form of Bmdsx transcripts ( BmdsxF ) , male form of Bmdsx transcripts ( BmdsxM ) was obviously expressed in females that possessed both BmA3-GAL4 and UAS-Masc-R ( S2 Fig ) . This result indicated that forced expression of Masc-R was able to shift the splicing pattern of Bmdsx from female to male mode . However , we could not further investigate the role of Masc in sexual differentiations morphologically using this system because BmA3-GAL4-induced expression of Masc-R caused severe lethality in females . We observed that Sumi13-3 animals weakly expressed the Masc-R gene independent of GAL4 induction ( Fig 2A ) , and thus used this line in further experiments . Silkworm strain Suzu19-1 was used to discriminate females from males . This strain has a W-chromosome-linked transgene that carries a DsRed gene under the control of 3×P3 unit . We crossed Sumi13-3 males heterozygous for the UAS-Masc-R transgene with Suzu19-1 females . We selected the progenies which expressed DsRed and EGFP . The progenies were heterozygous for the UAS-Masc-R transgene females ( hereafter described as Masc-R/+ females ) . Although Masc-R/Masc-R females were also subjected to the analysis , it was extremely difficult to obtain Masc-R/Masc-R females as described in Materials and Methods , Therefore , in this study , Masc-R/+ females were mainly subjected to the following analyses . RT-PCR analyses demonstrated that the Masc-R gene was expressed in several organs dissected from the larvae of Masc-R/+ females ( Fig 3A and 3B ) . Quantitative real-time RT-PCR ( qRT-PCR ) using primers that can amplify cDNAs from both endogenous Masc and the Masc-R transgene revealed a significant increase in the total expression level of Masc mRNA in Masc-R/+ females as compared with that in sister females , which did not have the UAS-Masc-R transgene ( hereafter described as +/+ females ) ( Fig 3C ) . Having demonstrated that expression of the Masc-R gene induces ImpM and BmdsxM in BmN4 cells [29] , we next investigated the expression of ImpM and the splicing pattern of Bmdsx in Masc-R/+ females . RT-PCR analysis with cDNAs prepared from day-1 first instar larvae showed that ImpM expression was induced in Masc-R/+ females ( Fig 3B ) . Notably , qRT-PCR analysis demonstrated that the expression level of ImpM was more than 28-fold higher in Masc-R/+ than in +/+ females ( Fig 3D ) . In addition , the male-specific splice variant of Bmdsx was detected in Masc-R/+ females , while the female form of Bmdsx transcripts alone was observed in +/+ females ( Fig 3B ) . The percentage of the male-specific splicing of Bmdsx relative to the total splicing of Bmdsx was > 18% in Masc-R/+ females ( Fig 3B and 3E ) . These results indicated that expression of Masc-R in females caused ectopic expression of male forms of Imp and Bmdsx . Vitellogenins are precursors of the major yolk proteins in insects . In the silkworm , vitellogenins are predominantly synthesized in the female fat body during larval—pupal ecdysis [30 , 31] . RT-PCR analyses demonstrated that the expression of Masc-R in females caused ectopic expression of male forms of Imp and Bmdsx in fat bodies within 3 hours after pupation ( S3A Fig ) . To investigate whether the expression of Masc-R affects the expression of the vitellogenin gene ( BmVg ) in Masc-R/+ fat bodies , we quantified BmVg mRNA level by qRT-PCR . As shown in Fig 4A , the level of BmVg mRNA was significantly decreased in the fat body of Masc-R/+ females . The expression level of BmVg in the Masc-R/+ females was less than 1% compared to that of +/+ females , and was similar to that in males ( Fig 4A ) . Vitellogenin proteins are synthesized in fat body and released into the hemolymph during larval-pupal ecdysis [30 , 31] . To investigate the vitellogenin level in hemolymph of the Masc-R/+ females , SDS-PAGE analysis of hemolymph was performed . A protein of approximately 203 kDa , which corresponds to the molecular weight of BmVg heavy chain ( BmVg-h ) [31] , was specifically observed in the hemolymph of +/+ females ( Fig 4B , lane 1 ) . Notably , no proteins corresponding to BmVg-h were detected by the same SDS-PAGE in the hemolymph of Masc-R/+ females ( Fig 4B , lane 2 ) . A protein band of approximately 42 kDa , which corresponds to the molecular weight of BmVg light chain ( BmVg-l ) was also very faint in the hemolymph of Masc-R/+ females ( Fig 4B , lane 2 ) . The protein profile displayed by the hemolymph of Masc-R/+ females was similar to that of males ( Fig 4B; compare lane 2 with lane 3 ) . These results suggested that the fat body in the Masc-R/+ female was physiologically masculinized . To investigate the effects of Masc-R expression on female development , we next performed morphological analysis using a stereoscopic microscope . The results showed that the ovaries of Masc-R/+ females had morphological abnormalities at the third instar larval stage . The ovarioles of the +/+ females at the same stage became longer and took on a tubular form ( Fig 5A and 5A’ ) . On the other hand , the ovarioles in the Masc-R/+ females were hypertrophied and much wider than those in the +/+ females ( Fig 5B and 5B’ ) . RT-PCR analysis verified that Masc-R was expressed in the ovaries of Masc-R/+ females ( S3B Fig ) . Consistent with this , both BmdsxM and ImpM were obviously expressed in Masc-R/+ ovaries ( S3B Fig ) . Ovarioles in +/+ females became much longer at the fifth instar larval stage ( Fig 5C and 5D ) , while elongation of each ovariole was not observed in the Masc-R/+ females . Instead , a large globular tissue was developed at the apical end of each ovariole ( Fig 5E and 5F ) . The globular tissues were filled with small follicles ( Fig 5G ) . These follicles showed the appearance of cysts , which were also observed in the testis at the same stage ( Fig 5H and 5I ) . Immediately after pupation , ovarioles of the +/+ females showed intensive growing , and each contained a large number of growing eggs ( Fig 6A ) . In the Masc-R/+ females , the length of each ovariole was greatly shorter than the +/+ females ( Fig 6B ) . The globular tissues observed in Fig 5F were fused and formed an undefined tissue at the apical end of each ovariole ( Fig 6B ) . To examine whether the Masc-R/+ ovaries contain tissues partially developed into testis , we performed RT-PCR analysis using primers that anneal to the B . mori homolog of radial spoke head protein 1 gene ( BmR1 ) , which is specifically expressed in the testis [32] . RT-PCR detected an approximately 850 bp of BmRI cDNA from +/+ and Masc-R/+ testes but not from +/+ ovary ( Fig 5J , compare lane 1 with lanes 3 and 4 ) . Thus , our RT-PCR analysis confirmed the testis-specific expression of BmR1 as reported previously [32] . The RT-PCR analysis also showed that BmR1 was expressed not only in testes , but also in the Masc-R/+ ovaries of the fifth instar larvae ( Fig 5J , lanes 2 and 4 ) . To further confirm that the globular tissues observed in Masc-R/+ ovaries express BmR1 , we performed RT-PCR with cDNAs prepared from the globular tissues and the other parts of the Masc-R/+ ovary . The results suggested that the expression of BmR1 in globular tissues seemed higher than that in the other parts ( Fig 5J , lane 6 ) . These results indicated that expression of Masc-R facilitates partial testis developments . To further verify the structures of the abnormal tissues observed in the Masc-R/+ females , we prepared tissue sections of the abnormal tissues . The results showed that the tissue consisted of four chambers whose shape was very similar to that of the testicular follicle ( Fig 6D and 6E ) . A structure that seemed to be the ovariole was observed in the chambers ( Fig 6D , surrounded by a dotted line ) . Notably , fibrous structures , which likely correspond to the sperm bundles , were also observed ( compare Fig 6F with Fig 6G ) . Examination of contents found within the abnormal tissues using a phase-contrast microscope revealed a considerable number of sperm bundles similar to those observed in the testis of the fifth instar larvae ( compare Fig 6H with Fig 6I ) . Nuclear staining with DAPI revealed that the nuclei were sparsely present in the sperm bundle ( Fig 6K ) . In B . mori , males produce two types of sperm bundles , one of which consists of eupyrene sperms ( Fig 6L and 6M ) and the other of which is composed of apyrene sperms ( Fig 6N and 6O ) . The patterns observed with DAPI staining showed that the sperm bundles in abnormal tissues likely corresponded to the apyrene sperm bundles . These results suggested that formation of sperm bundles was induced in the ovary by ectopic expression of Masc-R . We observed external structures of the Masc-R/+ adult females , and found that they all exhibited the normal adult female phenotype ( Fig 7A ) . Next , we crossed Masc-R/+ females with Masc-R/+ males and observed the offspring . We found all examined females homozygous for the transgene ( Masc-R/Masc-R females ) ( S4 Fig ) had an additional abdominal segment ( eighth abdominal segment , A8 ) ( Fig 7C and 7D ) , which is one of the male-specific external structures in B . mori ( Fig 7B ) . Several abnormal structures , some of which were severely melanized , were also observed around the genital papilla of Masc-R/Masc-R females ( Fig 7E and 7F ) . One of these abnormal structures , which was observed above the genital papilla , was considered to be an uncus ( Fig 7F and 7H ) . These results suggested that the homozygous expression of Masc-R caused partial maleness in the external structures of the adult female . We next investigated the fertility of the females expressing the Masc-R transgene . Ovarioles in the Masc-R/+ female were significantly shorter than those in the +/+ female ( Fig 8A , 8B and 8D ) , and contained a significantly lower number of mature eggs ( Fig 8E ) . Testis-like abnormal tissues were observed at the apical end of ovarioles ( Fig 8C ) . The average number of eggs laid by the Masc-R/+ females was only 57 . 0 , while that of the +/+ females was 483 . 8 ( Fig 8E ) . Hatchability of eggs laid by the Masc-R/+ females was similar to that of the +/+ females ( Fig 8F ) . These results indicated that expression of Masc-R severely repressed the development of ovarioles , reducing egg production .
In the present study , we investigated the biological functions of the Masc gene by transgenic approaches . Two transgenic strains , Sumi13-1 and Sumi13-3 , carrying a construct with Masc-R under the control of UAS were generated ( Fig 1 ) . Forced expression of the UAS-Masc-R transgene mediated by ubiquitously expressed GAL4 caused female-specific lethality in the larval stages ( Fig 2 ) . Masc protein globally repressed gene expression from the Z chromosome to compensate for gene dosage between male ( ZZ ) and female ( ZW ) [23] . The female-specific lethality observed in this study is probably due to decreased levels of Z-linked genes in females caused by forced expression of Masc . This hypothesis is supported by the observation that a failure of dosage compensation is lethal during development in mice , C . elegans , and D . melanogaster [33–35] . Alternatively , high level of Masc expression might either repress expression of a gene ( s ) that is essential for female development or induce expression of a factor ( s ) that has a deleterious effect on normal female development . Research is in progress to further define the link between lethality and defects in dosage competition in Masc-R expressing females . BmA3-GAL4-induced expression of Masc-R resulted in the expression of BmdsxM in females ( S2 Fig ) . Although expression level of Masc-R in these females was much greater than that in normal males , the expression pattern of Bmdsx was not completely shifted from female to male mode . One plausible explanation for this discrepancy is that BmA3-GAL4 driver was not able to induce ubiquitous expression of Masc-R . Alternatively , other factors , in addition to Masc , may be necessary for fully masculinizing the expression pattern of Bmdsx . We found that one of the UAS-Masc-R strains , Sumi13-3 , expressed Masc-R mRNA independent of GAL4 induction in several organs ( Fig 3A ) . Sumi13-3 females expressed both male- and female-specific splice isoforms of Bmdsx and ImpM ( Fig 3B , 3D and 3E ) , consistent with previous reports in which overexpression of Masc-R in BmN4 cells resulted in the expression of both BmdsxM and ImpM [23 , 29] . However , it is possible that insertion of the UAS-Masc-R transgene changed the expression pattern of genes located near the insertion site , affecting the expression patterns of sex-determining genes . To rule out this possibility , we investigated expression levels of genes present within 100 kbp upstream or downstream of the Masc-R insertion site of Sumi13-3 . There were three predicted genes ( Bmgn015522 , Bmgn012518 , Bmgn012517 ) present downstream of the insertion site ( Fig 1C ) . The qRT-PCR analysis with cDNAs prepared from day-1 first instar larvae shown in S5 Fig demonstrated that none of the three genes showed any significant differences in expression levels between Sumi13-3 females and normal females ( S5 Fig ) . Therefore , the abnormal expression of genes near the Masc-R insertion site does not cause maleness in Sumi13-3 females . In Sumi13-3 , females heterozygous for the UAS-Masc-R transgene showed an almost complete blockade of vitellogenin synthesis ( Fig 4B ) and exhibited severe abnormalities in the ovaries ( Fig 5B and 5F ) with testis-like tissues ( Fig 5G ) . Notably , testis-like tissues in the pupal stage contained a large number of sperm bundles ( Fig 6F and 6H ) . In addition to these male-like phenotypes , homozygous expression of UAS-Masc-R resulted in formation of an additional abdominal segment , which is one of the male-specific morphological features , and caused partial male differentiation in female genitalia ( Fig 7C–7E ) . These results demonstrated that Masc-R mRNA expression from the transgene led to the partial female-to male sex reversal . We hypothesized that the expression level of Masc-R in Sumi13-3 females was able to partially induce masculinization but was insufficient to cause lethality . Vitellogenin is predominantly synthesized in the female fat body during larval—pupal ecdysis [30 , 31] . Gel-mobility shift assays demonstrate that BmDSX proteins bind to the sequence ( ACATTGT ) between −95 and −89 nt relative to the transcriptional initiation site of the vitellogenin gene [36] . BmdsxF ectopically expressed in males induces the expression of vitellogenin mRNA in male fat body [36] , whereas the expression of BmdsxM decreases the expression level of the vitellogenin gene [37] . The TALEN ( transcription activator-like effector nuclease ) -based mutation of BmDSXF reduced expression of the vitellogenin mRNA , whose expression level was only 25% compared to that in the normal-type females [38] . These findings suggest that Bmdsx directly controls the transcriptional level of the vitellogenin gene . The dramatic reduction in vitellogenin expression observed in Sumi13-3 Masc-R/+ females could be attributed to the increased expression of BmDSXM caused by Masc-R expression . Females homozygous for the Masc-R transgene exhibited abnormalities in genitalia with partial male structures . This is similar to our previous report that ectopic expression of BmDSXM in females caused partial male differentiation in female genitalia [37] . Moreover , an eighth segment was formed in these females . Sexual dimorphisms have been reported in adult abdominal segments of D . melanogaster . In this species , the female genitalia is developed from the eighth abdominal segment , while the male genitalia originated from the ninth abdominal segment [39 , 40] . It is also known that Abdominal-B ( Abd- B ) is responsible for the specification of these posterior segments [41] . In Drosophila melanogaster , DsxF and DsxM cooperate with Abd-B isoforms to produce sexual dimorphisms in these posterior segments [42] . In B . mori , the expression level of Abd-B in the posterior abdomen differs between males and females [43] . Taken together , in females homozygous for the UAS-Masc-R transgene , interaction between BmDSXM and Abd-B in the posterior abdomen might produce a developmental signal that facilitates the formation of the male-specific eighth segment . In this study , the ectopic expression of Masc-R did not induce complete sex reversal in females . As described above , Sumi13-3 females expressed not only BmdsxM transcripts , but also BmdsxF transcripts ( Fig 3B , S3 Fig ) . This may be due to an insufficient level of Masc-R expression for inducing female-to-male sex reversal because full activation of Masc-R will cause lethality in females during the larval stage . It has been reported that BmDSXF and BmDSXM compete with each other for a target site when both are present [36] . This competition would interfere with the masculinizing activity of the BmDSXM protein and inhibit the feminizing activity of the endogenous BmDSXF protein in Masc-R transgenic females . The incomplete masculinization in the Sumi13-3 females may be considered a result of such competition . Another likely explanation for the incomplete sex reversal is that the dosage of Z-linked genes may mediate sex determination and two doses are required for male development ( ZZ ) . Several lines of evidence strongly support this hypothesis . Mapping of testis-specific full-length cDNA sequences onto chromosomes indicates that the Z chromosome is enriched in testis-specific genes [44] . The mean expression level of the Z-linked genes in testis is approximately 11 times higher than in the ovary . In addition , expression levels of 55% genes on the Z chromosome are at least two times higher in testis than in ovary [45] . It would be possible that the Z chromosome is enriched in genes essential for governing male sexual dimorphisms . In the chicken , whose sex is determined by the ZW system ( similar to the silkworm ) , the Z dosage hypothesis for sex determination is supported based on the observation that two copies of a Z-linked gene , DMRT1 , are required for male sex determination [46] . The most notable results in this study were the ectopic formation of testis that contained a considerable number of sperm bundles in females . Prior experiments show that ovaries of adult females with mutations in the female-specific Bmdsx exon induced by TALEN showed abnormalities closely resembling those observed in the present study [38] . These females have significantly shorter ovarioles containing little or no mature eggs , and abnormal tissues were observed at the apical end of ovarioles . Furthermore , our knockout analysis using somatic TALEN mutagenesis indicated that mutations in the female-specific exon of Bmdsx caused abnormalities in ovaries of 5th instar larvae ( S6 Fig ) . Similar to the Masc-R/+ ovaries , a testis-like globular tissue was developed at the apical end of each ovariole . These findings suggest that loss of function mutation in BmDSXF may be sufficient for inducing the testis-like tissues in females . In D . menalogaster , transgenic analysis showed that female DSX protein functions as a negative regulator of male differentiation [47] . The loss of function mutation in BmDSXF may abolish such negative regulatory effect of BmDSXF on male differentiation , resulting in the formation of the testis-like tissues . Ectopic expression of Masc-R in females partially shifted the splicing pattern of Bmdsx from female to male mode ( Figs 3B and 5J ) . This will not only increase the expression level of BmDSXM , but also reduce the expression level of BmDSXF . Such reduced level of BmDSXF may impair the negative regulatory effect of BmDSXF on male development , leading to the formation of the testis-like tissues . Although loss of function mutation of BmDSXF resulted in the formation of the testis-like tissues ( S6 Fig ) , it still remains unclear whether these testis-like tissues contain mature sperms . In D . melanogaster , dsx is not required within the female or male germ line for normal development [48 , 49] and that female germline cells undergo normal oogenesis when surrounded by a soma masculinized by the dominant male gain-of-function dsx allele , dsxDom [50] . Similarly , Bmdsx may be dispensable for sexual differentiation of germ cells . It is conceivable that Masc-R expression in ZW germ cells may directly promote the development of male germ cells . However , ectopic expression of Masc-R alone is not sufficient to induce ZW germ cells to complete spermatogenesis because sperms observed in Masc-R females were not fully matured ( Fig 6K ) . One possible explanation for this is that piRNAs transcribed from Fem or other W-chromosomal regions may inhibit differentiation of Masc-R-expressing germ cells into mature sperms . In B . mori , W chromosome is a source of numerous female-specific piRNAs [51] . It could be postulated that female-specific piRNAs from W chromosome may disrupt expression patterns of genes crucial for normal spermatogenesis . Alternatively , two doses of Z-linked genes are required for germ cells to achieve spermatogenesis . Recently , a Masc homolog has been identified from Trilocha varians , a species closely related to B . mori [52] . RNAi-mediated knockdown of the Masc homolog ( TvMasc ) shifted the splicing pattern of Trilocha varians dsx from male mode to female mode . Moreover , overexpression of TvMasc cDNA in BmN4 cells induced the expression of BmdsxM and ImpM . These findings suggested that the function of Masc is evolutionarily conserved in the sex determination pathway of Bombycidae . Masc encodes a CCCH-type zinc finger protein [23] . CCCH-type zinc finger proteins directly bind not only to DNA but also to RNA [53 , 54] . It will be important to identify direct target RNAs of Masc protein to explore the molecular mechanisms underlying gene dosage compensation and male development in lepidopteran insects .
B . mori strains were maintained on standard conditions [55] . Silkworms expressing Masc-R was generated by piggyBac element transformation . The Masc-R cDNA sequence was reported previously [23] . Plasmid DNA of the piggyBac transformation vector containing UAS-Masc-R was purified using a Qiagen Plasmid Midi Kit ( Qiagen , Hilden , Germany ) . PiggyBac-mediated germline transformation was conducted to generate transgenic strains by the method of Tamura et al . [56] with minor modifications . Briefly , w1-pnd embryos were microinjected with DNAs ( piggyBac transformation vector [0 . 4 μg/μL] , piggyBac transposase mRNA [0 . 2 μg/μL] , and helper plasmid DNA [0 . 1 μg/μL; pGEMe-pigORF-pA90] ) in injection buffer . Hatched larvae from the injected eggs were grown to the adult stage , and the G0 individuals were crossed by sister-brother mating to obtain G1 progenies . EGFP-positive individuals were selected from G1 embryos to establish UAS-Masc-R strains . Ubiquitous GAL4 expressing strain , BmA3-GAL4 ( [27]; strain name: 193–2 ) , was used for crossing with the Masc-R strains to produce silkworms expressing these genes . Suzu19-1 was obtained from NBRP ( National bioresource project ) -Silkworm Center at Kyushu University and used to discriminate females from males based on DsRed expression . We crossed Sumi13-3 males heterozygous for the UAS-Masc-R transgene with Suzu19-1 females . We selected the progenies that expressed DsRed and EGFP . The progenies were heterozygous for the UAS-Masc-R transgene females ( hereafter described as Masc-R/+ females ) . To obtain Masc-R/Masc-R females , we reared Masc-R/+ females by feeding fresh mulberry leaves harvested during early summer season to help Masc-R/+ females grow well . Then more than 100 females were crossed with Masc-R/+ males , yielding progenies , approximately 300 of which reached on adult stage . We selected Masc-R/Masc-R females from these adults by PCR-based genotyping as described in S4 Fig . It was extremely difficult to obtain sufficient numbers of Masc-R/Masc-R females , and therefore , we were not able to use Masc-R/Masc-R females for all the analyses . In this study , Masc-R/+ females were mainly subjected to the analyses . To prepare the tissue sections , tissues were fixed with 4% paraformaldehyde or Bouin's solution followed by dehydration in a graded ethanol-n-butanol series , and then embedded in paraffin . Cut sections ( 5 μm thick ) were deparaffinized and stained with Mayer's hematoxylin-eosin ( HE ) . Sperm bundles were placed on MAS-coated glass slides ( Matsunami Glass Ind . , Ltd . , Osaka , Japan ) , air-dried and baked at 55°C for 5 min . For nuclear staining , they were covered and mounted with Vectashield containing DAPI ( Vector Laboratories , Peterborough , UK ) . Transformation vector , pBacMCS [UAS-Masc-R-SV40 , 3×P3-GFP] , was produced by the following procedure . Masc-R cDNA was PCR-amplified from pIZ/V5-Masc-R-His [23] using KOD plus DNA polymerase ( Toyobo Co . Ltd . , Ohtsu , Japan ) . The following primer set was used: 5’- GCC TAG TAG ACC TAG CCA AAA TGG ATT ACA AGG ATG ACG ACG-3’ , 5’- GTA TGG CTG ACC TAG CTA TTG AAA CGG CGG TGG TG-3’ . Masc-R fragment was inserted into the BlnI site of the pBacMCS [UAS-SV40 , 3xP3-GFP T to H] vector [55] using In-Fusion HD Cloning Plus ( TaKaRa Bio Inc . , Shiga , Japan ) . In the Southern blot analysis , genomic DNA was isolated from adults using the method described by Sambrook and Russell [57] . Genomic DNA was digested with either BamHI or BglII . Digested genomic DNAs were then separated on a 1 . 0% agarose gel and subsequently transferred to a Hybond-N+ membrane ( GE Healthcare UK Ltd . , Buckinghamshire , UK ) . Southern blot analysis was performed using probes labeled with Amersham AlkPhos Direct Labeling Reagents , and DNA bands were visualized using Amersham CDP-Star Detection Reagent following the manufacturer’s guidelines ( GE Healthcare ) . SDS-PAGE analysis of hemolymph was performed according to the method of Mine et al . [30] . Hemolymph ( 1 μL ) was treated with SDS sample buffer for 2 min at 95°C . Samples were separated by electrophoresis on 10% SDS-polyacrylamide gel and stained with Quick-CBB PLUS ( Wako , Osaka , Japan ) . Total RNA was extracted from each egg using Isogen ( Nippon Gene , Tokyo , Japan ) , as described previously [19] . RT-PCR reactions were performed according to the protocol described previously [19] . The primer sequences and PCR conditions utilized in this study are indicated in S1 Table . qRT-PCR assays were performed according to the protocol described previously [19] . All primer sequences used in this study are listed in S2 Table . The BmEF-2F1 and BmEF-2R1 primers were used to amplify elongation factor-2 ( EF-2 ) as an internal standard for quantification [58] . Genomic PCR was performed with EmeraldAmp PCR Master Mix ( TaKaRa ) according to the protocol described previously [59] . The primer sequences and PCR conditions utilized in this study are indicated in S1 Table . Inverse PCR was performed as described previously [27][55] . Briefly , genomic DNA was isolated from adult legs using a DNeasy Blood & Tissue kit ( Qiagen ) . DNA was digested with either MspI or Sau3AI , and then circularized with T4 DNA ligase ( New England Biolabs , Beverly , MA , USA ) at 4°C overnight . Circularized DNA was used as a template for the nested PCR method . PCR was performed in a total reaction volume of 30 μL using TaKaRa Ex Taq ( TaKaRa ) . In the 2nd PCR , 0 . 5 μL of the first PCR product was used as a template . | In the silkworm , Bombyx mori , a W-chromosome-linked gene Feminizer ( Fem ) determines femaleness . Fem transcript yields a piRNA ( Fem piRNA ) and Fem- piRNA-PIWI complex targets and cleaves mRNAs transcribed from the Masculinizer ( Masc ) . Masc is required for male-specific expression of Bmdsx , which is an important regulatory gene for sexual differentiation , and therefore , Masc is considered to be essential for maleness . However , there has been no direct evidence that Masc indeed causes maleness in sexually dimorphic structures . To clarify this point , we established silkworm strains carrying a transgene that expresses Fem-piRNA-resistant Masc gene ( Masc-R ) . Transgenic expression of the Masc-R induced male mode of expressions of downstream sex-determining genes in females . Notably , ovaries in these females exhibited testis-like structures that contained sperm bundles . Homozygous expression of the Masc-R caused formation of the male-specific abdominal segment in adult females and induced partial male differentiation in female genitalia . Thus , Masc can induce maleness at the morphological level and is sufficient for spermatogenesis . This is the first report to our knowledge on a gene that can masculinize a wide variety of sexual characteristics in lepidopteran insects . | [
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] | 2016 | Transgenic Expression of the piRNA-Resistant Masculinizer Gene Induces Female-Specific Lethality and Partial Female-to-Male Sex Reversal in the Silkworm, Bombyx mori |
Rift Valley fever virus ( RVFV ) causes disease in livestock and humans . It can be transmitted by mosquitoes , inhalation or physical contact with the body fluids of infected animals . Severe clinical cases are characterized by acute hepatitis with hemorrhage , meningoencephalitis and/or retinitis . The dynamics of RVFV infection and the cell types infected in vivo are poorly understood . RVFV strains expressing humanized Renilla luciferase ( hRLuc ) or green fluorescent protein ( GFP ) were generated and inoculated to susceptible Ifnar1-deficient mice . We investigated the tissue tropism in these mice and the nature of the target cells in vivo using whole-organ imaging and flow cytometry . After intraperitoneal inoculation , hRLuc signal was observed primarily in the thymus , spleen and liver . Macrophages infiltrating various tissues , in particular the adipose tissue surrounding the pancreas also expressed the virus . The liver rapidly turned into the major luminescent organ and the mice succumbed to severe hepatitis . The brain remained weakly luminescent throughout infection . FACS analysis in RVFV-GFP-infected mice showed that the macrophages , dendritic cells and granulocytes were main target cells for RVFV . The crucial role of cells of the monocyte/macrophage/dendritic lineage during RVFV infection was confirmed by the slower viral dissemination , decrease in RVFV titers in blood , and prolonged survival of macrophage- and dendritic cell-depleted mice following treatment with clodronate liposomes . Upon dermal and nasal inoculations , the viral dissemination was primarily observed in the lymph node draining the injected ear and in the lungs respectively , with a significant increase in survival time . These findings reveal the high levels of phagocytic cells harboring RVFV during viral infection in Ifnar1-deficient mice . They demonstrate that bioluminescent and fluorescent viruses can shed new light into the pathogenesis of RVFV infection .
Rift Valley fever virus ( RVFV ) is an arthropod-borne member of the Bunyaviridae family , genus Phlebovirus that causes recurrent outbreaks affecting humans and animals . The virus can be transmitted by Aedes and Culex mosquitoes [1] , although it can also be transmitted by inhalation or physical contact with the body fluids from infected animals [2] , [3] . Identified in the 1930s in Kenya , RVFV has spread during recent years to most sub-Saharan African countries , in Egypt and in the Arabian Peninsula , and in the Indian Ocean islands of Grande Comore and Mayotte [4] , [5] , [6] . In humans , RVFV infections are generally either asymptomatic or characterized by a feverish syndrome without any severe sequelae . However , a small percentage of patients exhibit complications , characterized by acute hepatitis with hemorrhage , meningoencephalitis and/or retinitis [7] , [8] , [9] , [10] . A relationship has been demonstrated between high viral load in blood and death of the patient [11] , [12] . RVFV infects domestic ruminants , including sheep , cattle , goats , and camels . It is responsible for massive abortion events in pregnant ruminants and high mortality in lambs and calves . High viremia associated with hepatic necrosis and increase of liver enzymes are hallmarks of severe acute lethal infection in ruminants [13] , [14] . Encephalomyelitis has been described in calves [15] . Laboratory rodents such as mice are also highly susceptible to RVFV infection . In outbred Swiss mice , the survival time was inversely proportional to the logarithm of the viral dose inoculated via the intravenous route [16] . Depending on their genotype , males from various inbred strains of mice inoculated by the peritoneal route with 102 PFU of the virulent Egyptian ZH548 strain die between 4 to 10 days after inoculation , illustrating natural variation in susceptibility of the host to RVF [17] . The main damages of mouse infection with RVFV can be observed early in the liver , with extensive apoptosis of hepatocytes , accompanied in the blood by a peak in liver enzymes , along with increased bilirubin levels [18] , [19] , [20] . It has been recently shown that mice that survive hepatitis develop later infection of the brain , and eventually die from meningoencephalitis [21] . Interestingly , a diverse set of cell types from a number of tissues was found to contain RVFV antigens , including mononuclear phagocytes , but also cardiac myofibers , pancreatic islet cells , and adrenal medullary cells [21] . These data showed that RVFV exhibits a large tropism for a variety of tissues and individual cell types . Quantitative real-time PCR have also been used to study the kinetics of RVFV infection in the blood and organs of infected mice [22] . High amounts of RVFV RNA were found in blood , liver and brain samples shortly after infection with the highest viral RNA levels in the liver . We hypothesized that in vivo imaging might be an alternative method to assess viral replication using a recombinant RVFV carrying a reporter gene that allows the monitoring of viral expression in live animal . RVFV has a tripartite negative-sense , single-stranded RNA genome with large ( L ) , medium ( M ) and small ( S ) segments . The L segment encodes the viral RNA-dependent RNA polymerase , the M segment the two virion glycoproteins ( GN and GC ) and the NSm nonstructural proteins , and the S segment the N nucleoprotein and the NSs nonstructural protein . Reverse genetic systems have been successfully developed for the recovery of recombinant RVFV ( reviewed in [23] ) . These rescue systems rely on transfection with plasmids expressing the three viral RNAs , and the N nucleoprotein and L RNA-dependent RNA polymerase , which are required for the packaging and replication of the viral RNAs . The RVFV RNA genome segments are expressed under the control of either the cellular DNA-dependent RNA polymerase I promoter [24] , [25] , [26] or the bacteriophage T7 promoter in cells that constitutively express T7 RNA polymerase [24] . Such rescue systems have been used to produce recombinant RVFV , including various mutants that lack the NSs , NSm genes , or carry specific mutations [26] , [27] , [28] , [29] , [30] . Viral strains that express reporter genes were also generated [24] , [31] , [32] . Importantly , in these recombinant viruses , the reporter gene activity directly reflects the extent of both viral transcription and replication . In this study , we aimed to detect and quantify viral replication in living animals using two recombinant RVFV strains expressing either humanized version of the luciferase gene of Renilla reniformis ( hRLuc ) or enhanced green fluorescent protein ( GFP ) gene of Aequora victoria . Both RVFV viruses lack a functional NSs gene , which is a main factor of virulence in mice [33] , and are therefore avirulent in immunocompetent mice . However , in mice that are nonresponsive to type I IFN , the virus expressing either hRLuc or GFP caused lethality within 3 days , in agreement with previous data for the NSs-deficient Clone 13 [24] . In these mice , virus infection could be tracked by luciferase imaging in live animals and by the detection of GFP-positive cells from infected animals , by use of flow cytometry . We observed qualitative and quantitative differences in the in vivo tropism of RVFV in mice and show previously unsuspected sites of virus replication and modes of virus spread .
Animals were housed in the Institut Pasteur animal facilities accredited by the French Ministry of Agriculture to perform experiments on live mice , in appliance of the French and European regulations on care and protection of the Laboratory Animals ( accreditation number B 75 15-01 and B 75 15-07 ) . The veterinary staff of the Institut Pasteur animal facility approved protocols . Protocols were performed in compliance with the NIH Animal Welfare Insurance #A5476-01 issued on 02/07/2007 . Inbred 129S2/SvPas mice with knockout at the interferon α and β receptor 1 locus ( Ifnar1−/− ) and control mice ( Ifnar1+/+ ) were bred at the Institut Pasteur [34] . Vero E6 cells were grown in DMEM supplemented with 10% FCS . BHK21/T7 cells [35] were grown in MEM supplemented with 5% FCS and tryptose phosphate broth powder ( Sigma-Aldrich , Gillingham , UK ) . The cell culture media were supplemented with 10 IU/ml of penicillin and 10 µg/ml of streptomycin . Stocks of the virulent RVFV Egyptian ZH548 strain were produced under biosafety level 3 ( BSL3 ) conditions . Plasmids pPol I-LZH , pPol I-MZH , and pPol I-SZH carrying the L , M and S segments of ZH548 , respectively , were cloned in the plasmid pRF108 , [24] , [36] . The plasmid pPol I-SZHΔNSs , derived from pPol I-SZH , carries two BbsI cloning sites in place of NSs [24] . The humanized Renilla reniformis luciferase sequence from phRL-SV40 ( Promega , Charbonnières-les-Bains , France ) was inserted in pPol I-SZHΔNSs to give pPol I-SZHΔNSs-hRLuc . The structure of pPol I-SZHΔNSs-hRLuc plasmid was confirmed by sequence analysis . Recombinant rZHΔNSs-GFP [24] and rZHΔNSs-hRLuc RVFV stocks were produced under BSL3 conditions . Approximately 5×105 BHK21/T7 cells were seeded in triplicate in 35 mm culture dishes . The following day , they were combined with FuGENE®6 transfection reagent ( Roche Applied Science , Indianapolis , IN ) and 0 . 5 µg each of pTM1-L and pTM1-N [25] , and 1 µg each of pPol I-LZH , pPol I-MZH , and either pPol I-SZHΔNSs-GFP or pPol I-SZHΔNSs-hRLuc in OptiMEM ( Gibco , invitrogen , Carslbad , CA ) . One day later , the medium was renewed . Five days later , the supernatant containing the rescued virus was collected and stored at −80°C . To produce viral stocks of rZHΔNSs-GFP and rZHΔNSs-hRLuc , Vero E6 cells were infected with the rescued virus at a MOI of 0 . 001 and 0 . 01 , respectively . At 72 h post-infection , the supernatant was collected and the viral suspension was titered . Vero E6 cells , infected with serial dilutions of viral suspension , were incubated under an overlay of DMEM supplemented with 2% FCS , antibiotics and 1% agarose . Four days later , the plates were stained with 0 . 2% crystal violet in 10% formaldehyde , 20% ethanol and the lytic plaques were counted . To test the luciferase expression within cells after infection with rZHΔNSs-hRLuc , Vero E6 cells were infected using a MOI of 0 . 3 and 3 . Next , every 2 h , for 12 h , luciferase activity was measured in triplicates by Renilla Luciferase Assay System ( Promega , Madison , WI ) . To check the stability of luciferase expression through passages , Vero E6 cells were infected using an MOI of 3 and the luciferase activity was measured in triplicates at 8 h post-infection . To test the GFP expression , Vero E6 cells were infected with rZHΔNSs-GFP using a MOI of 1 . At 15 h post-infection , the cells were fixed for 30 min at room temperature with 4% paraformaldehyde in PBS , permeabilized for 10 min with 0 . 5% Triton X100 in PBS and incubated for 30 min at room temperature in 5% bovine serum albumin ( BSA ) in PBS . The cells were next incubated for 30 min at 37°C with a mouse anti-N antibody diluted in 5% BSA in PBS ( dilution 1∶800 ) , washed with 5% BSA in PBS and incubated for 25 min at 37°C with the secondary antibody Alexa Fluor 555 goat anti-mouse ( Invitrogen , Paisley , UK ) diluted in 5% BSA in PBS ( dilution 1∶1200 ) . Finally , the cells were washed with 5% BSA in PBS and then in water . The slides were mounted with Fluoromont-G ( SouthernBiotech , Birmingham , AL ) . The cells were observed under an Axioplan 2 Imaging microscope ( Zeiss , Le Pecq , France ) using excitation and emission filter allowing simultaneous detection of GFP and Alexa Fluor 555 . One week prior to infection , five to six week-old mice were transferred in BSL3 isolators to allow acclimatization . After this period , they were inoculated intraperitoneally ( i . p . ) , intradermally ( i . d . ) or intranasally with 104 PFU ZH548 , rZHΔNSs-hRLuc or rZHΔNSs-GFP RVFV in DMEM supplemented with 2% FCS , and antibiotics . For i . p . infection , mice were inoculated with 100 µL of viral suspension . For i . d . and intranasal infections , mice were first anesthetized with ketamine ( 150 mg/kg ) and xylazine ( 10 mg/kg ) administered i . p . , then inoculated with either 15 to 30 µL or 15 µL of viral suspension into the ear ( i . d . ) or intranasally , respectively . Mortality was recorded at least twice a day from day 1 to 4 post-infection and once a day after day 5 post-infection until the end of the observation period . Animals were observed for a maximum of 14 days . To improve bioluminescence imaging , hairs were removed [37] . We observed the mice to detect clinical signs due to the infection prior to imaging . Mice that exhibited no severe clinical signs were anesthetized and injected i . p . with 100 µL h-coelenterazine ( 1 mg/ml ) . The h-coelenterazine stock solution provided by Nanolight Technology ( Pinetop , AZ ) was solubilized in ethanol-propylene glycol solution ( 1∶1 ) at 10 mg/ml . This solution was diluted in PBS ( 1∶9 ) just before imaging . The h-coelenterazine-treated mice were immediately placed in a hermetically sealed light-tight-transparent chamber ( TEM Sega , Lormont , France ) equipped with two HEPA filters . One HEPA filter was connected to an air pump , thus allowing air renewal during the imaging . The bio containment chamber allowed simultaneous imaging of 6 mice . The mice were imaged 15 and 20 min after h-coelenterazine injection for the whole body and the thorax , respectively [38] . Imaging was performed with a Xenogen's IVIS 100 system , including a cooled charge-coupled device ( CCD ) camera [39] , [40] . Integration periods ranged from 0 . 5 to 120 s depending on the amounts of light emitted at various infection sites . Images were obtained using Living Image® 3 . 1 software ( Xenogen , Alameda , CA ) . Specific regions of interest ( ROI ) on the images were defined without overlay , using the anatomic location of the different organs and their visual observation through the skin when possible . For each imaging session , a mock-infected mouse was used as a negative control . A signal was considered significant if its intensity in infected mice was at least twofold higher than the background luminescence in the mock-infected mouse . After the last imaging time point , mice were euthanized . For ex vivo imaging , selected mice were euthanized at different times after infection for harvest of the following organs: liver , spleen , thymus , lung , kidney , stomach , small and large intestine , heart , ovary and uterus , testis , epididymis , seminal vesicles and preputial glands . The organs were placed in 6 ( intestine ) or 2 ml ( all other organs ) of PBS . Before imaging , 1 µL/ml h-coelenterazine 5 mM in ethanol and propylene glycol ( 1∶1 ) was added [41] . Imaging was performed in a hermetically sealed chamber to avoid light . Images were acquired 10 min after the addition of h-coelenterazine . Integration period ranged from 0 . 5 to 60 s depending on the amounts of light emitted from various organs . RNA was extracted using Trizol LS reagent ( Invitrogen , Carslbad , CA ) and suspended in RNase free water . RNA was quantified using Nanodrop 3300 ( Thermo Scientific , Courtaboeuf , France ) . The M segment of RVFV was amplified with primers 5′-CATGGATTGGTTGTCCGATCA-3′ and 5′-TGAGTGTAATCTCGGTGGAAGGA-3′ . Quantitative RT-PCR assays were performed using StepOne Plus Real-Time PCR System ( Applied Biosystem , Courtaboeuf , France ) in 96-well plates . Reverse transcription using MultiScribe Reverse Transcriptase ( Applied Biosystem ) at 48°C for 30 min was performed followed by a standard amplification program . The size of the amplification product was 108 pb . A standard curve was generated using duplicates of 10-fold serial dilutions of RNA of the M segment ranging from 109 to 102 copies . Quantification of viral RNA was done by comparison of the threshold cycle ( Ct ) values of the samples to the standards . Histopathological and immunohistochemical analysis of wild-type mice infected with 104 PFU ZH548 RVFV was performed 3 to 5 days after i . p . inoculation . rZHΔNSs-hRLuc-infected Ifnar1−/−mice were euthanized at 8 , 16 and 34 h after i . p . inoculation . For each time point , a complete post-mortem examination was carried out . The lung , brain , kidneys , spleen , liver , pancreas , thymus , testis , uterus and ovaries were removed and immediately fixed for one week in 10% neutral buffered formalin . Samples from each organ were embedded in paraffin and five-micrometer sections were then cut and stained with hematoxylin and eosin ( HE ) . The histological characterization of lesions was completed by an immunohistochemical detection of the RVFV using mouse antibodies against the RVFV ( dilution 1∶100 ) visualized with the Histofine Simple Stain MAX-PO kit ( Histofine Biosciences inc , Cambridge , UK ) . Eleven Ifnar1-deficient 129S2/SvPas mice were either infected i . p . with 104 PFU rZHΔNSs-GFP RVFV ( N = 6 ) or mock-infected ( N = 5 ) . Twenty-four hours later , the spleen was harvested . Erythrocytes were lysed using NH4Cl ( 9 g/L ) buffer . The rat anti-mouse CD16/CD32 , clone 2 . 4G2 antibody ( BD Pharmingen , San José , CA ) was used to block non-antigen-specific binding of immunoglobulins to Fc-receptors . Cells were stained using a combination of the following antibodies: ( i ) PE-conjugated rat anti-mouse NKp46/CD335 ( BD Pharmingen ) . ( ii ) PerCP-Cy5 . 5-conjugated hamster anti-mouse CD3 ( BD Pharmingen ) . ( iii ) APC-conjugated rat anti-CD19 ( BD Pharmingen ) . ( iv ) Pacific Blue-conjugated rat anti-mouse CD11b/Mac-1 ( eBioscience , San Diego , CA ) . ( v ) APC-conjugated hamster anti-mouse CD11c/Itgax ( BD Pharmingen ) . ( vi ) Alexa Fluor 700-conjugated rat anti-mouse MHC Class II ( I-A/I-E ) ( eBioscience ) . ( vii ) PE-conjugated rat anti-mouse Ly6G/Gr-1 ( BD Pharmingen ) . ( viii ) Biotin-conjugated anti-mouse CD115/c-Fms ( eBioscience ) with Streptavidin-PerCP-Cy5 . 5 ( BD Pharmingen ) as second-step reagent . All staining procedures were conducted on ice . Then , the cells were fixed with 4% formaldehyde . Fluorescence was measured using a FACSAria II flow cytometer ( BD Biosciences , San Jose , CA ) , and data analysis was performed using CellQuest ( BD Biosciences ) and FlowJo ( Ashland , OR ) softwares . Dead cells were visualized using the Fixable Aqua Dead Cell Stain kit ( Invitrogen , Carlsbad , CA ) . Fluorescence compensation settings for multicolor flow cytometric analysis were optimized based on single-stained polystyrene microparticles ( CompBeads , BD Pharmingen ) . Clodronate ( Cl2MBP; dichloromethylene-biphosphonate ) -loaded liposomes ( CLL ) were used to deplete phagocytic cells [42] , [43] . Clodronate was a gift of Roche Diagnostics GmbH , ( Mannheim , Germany ) . It was encapsulated in liposomes as described earlier [42] , [43] . Mice were injected i . p . with 300 µL and i . v . with 200 µL CLL . Control mice were treated i . p . and i . v . with PBS-loaded liposomes . Twenty-four hours later , single-cell suspensions were prepared from blood and spleen . FcR blocking reagent mouse ( Miltenyi Biotec , Bergisch Gladbach , Germany ) was used to block non-antigen-specific binding of immunoglobulins to Fc-receptors . Cells were stained using combination of the following antibodies: FITC-conjugated rat anti-mouse CD11b ( BD Pharmingen ) , PE-conjugated rat anti-mouse CD115 ( eBioscience . com ) , APC-conjugated rat anti-mouse F4/80 ( eBioscience . com ) , PE-conjugated hamster anti-mouse CD11c ( BD Pharmingen ) and Pacific Blue-conjugated rat anti-mouse Ly6G/Gr-1 ( eBioscience . com ) . All staining procedures were conducted on ice . Fluorescence data were obtained and analyzed using MACSQuant Analyzer and MACSQuantify software ( Miltenyi Biotec ) . Challenge with 104 PFU rZHΔNS-hRLuc was performed by injection into the ear , 24 h after liposome treatment . The survival curves were compared using the logrank test . The bioluminescence signals and blood plasma viral loads were analyzed with the nonparametric Mann-Whitney test . All tests were performed using the StatView 5 . 0 software ( SAS Institute Inc , Cary , NC ) .
The generation of a recombinant RVFV expressing a green fluorescent protein ( GFP ) , rZHΔNSs-GFP , has previously been described [24] . Our previous attempts to generate a recombinant RVFV expressing a humanized firefly luciferase ( hFLuc ) have been confounded by genetic instability and the rapid emergence of mutants with deletions [24] . Therefore , we generated rZHΔNSs-hRLuc RVFV that carries a humanized Renilla luciferase ( hRLuc ) gene using Pol I based plasmids as previously described [24] . The rescued rZHΔNSs-hRLuc was amplified in Vero E6 cells and stocks produced . The titer reached 8×107 PFU/ml . The plaques formed by rZHΔNSs-hRLuc were fuzzy with a faint staining inside , as those obtained with the rZHΔNSs virus that carries a deletion of the NSs gene [24] . To test the luciferase expression within the infected cells , Vero E6 cells were infected with rZHΔNSs-hRLuc at a MOI of either 0 . 3 or 3 , and lysed every 2 hours for 12 h . Luciferase activity was measured using coelenterazine , a specific substrate of Renilla luciferase , and found to be expressed at significant levels from 2 h post-infection onwards , while uninfected Vero E6 cells showed no luciferase activity . The luciferase activity increased with time and was dependent on the MOI ( data not shown ) . This is consistent with previous reports [31] , [32] . To check the stability of the recombinant virus in vitro , rZHΔNSs-hRLuc was passaged on Vero E6 cells and , at each passage , we measured the viral titer in the supernatant at 72 h post-infection and the luciferase activity within the infected cells at 8 h post-infection with a MOI of 3 . Both the viral titer and the luciferase activity remained stable over at least 8 passages , varying from 107 to 108 PFU/ml and from 107 to 108 raw light units ( RLU ) /s per 3×105 cells , respectively . To test the virulence of the recombinant virus , wild-type 129S2/SvPas mice ( N = 5 ) and 129S2/SvPas mice deficient for IFN-α/β receptor subunit 1 ( Ifnar1−/− ) ( N = 10 ) were infected i . p . with 104 PFU of rZHΔNSs-hRLuc . All wild-type mice survived the infection for 13 days with no signs of disease , as seen following infection with rZHΔNSs in which the NSs gene is totally deleted [24] or Clone 13 , a natural isolate that lacks 69% of the NSs open reading frame [33] . In contrast , all rZHΔNSs-hRLuc infected Ifnar1−/− mice died within 45 h ( Figure 1 ) from severe hepatitis with no signs of neurological disorder . Infections of Ifnar1−/− mice with rZHΔNSs or with Clone 13 gave similar results ( [24] and data not shown ) . To evaluate the stability of the recombinant viruses in live animals , total RNAs were extracted from the liver of rZHΔNSs-hRLuc- or rZHΔNSs-GFP-infected Ifnar1−/− mice at 34 h post-infection and RT-PCR assays were performed using primer pairs flanking the hRLuc or GFP reporter gene . The amplification product sizes were those expected from the structure of pPolI-SZHΔNSs-hRLuc and pPolI-SZHΔNSs-GFP plasmids ( data not shown ) . No amplification products with smaller sizes were observed , suggesting that the recombinant viruses maintained their own genomic stability not only in cultured cells , but also during in vivo infection . Furthermore , to examine the reporter expression stability after in vivo infection , the recombinant rZHΔNSs-GFP was harvested from the liver of an infected Ifnar1−/− mouse at 34 h post-infection and then used to infect Vero E6 cells at a MOI of 1 . The percentage of cells positive for the N viral protein that were also GFP-positive was measured . The percentage of N-positive , GFP-positive cells was almost identical to that of the initial viral stock ( 84%±1 . 90% vs . 85%±7 . 34% ) . Altogether , these results suggest that the recombinant viruses were stable for the time of infection in live mice . To visualize the spread of the virus , Ifnar1−/− mice were infected i . p . with 104 PFU of rZHΔNSs-hRLuc . At 8 , 16 and 34 h post-infection , h-coelenterazine was injected i . p . This route of h-coelenterazine administration was preferred to tail-vein injection due to slower kinetics of light production , as previously reported [39] . Mice were observed with real-time in vivo imaging 15 and 20 min after the injection of h-coelenterazine for the whole body and the thorax , respectively . At 8 h post-infection , luminescence was readily detected . Short integration periods ( 15 s ) were sufficient to acquire a significant signal . We observed strong signals between the forelegs in the thoracic cavity , and below the xiphoid cartilage in the abdominal cavity , respectively ( Figure 2A ) . Imaging of the left profile showed an additional signal in the spleen ( Figure 2B ) . In some experiments , animals were euthanized for ex vivo imaging , the organs of the thorax and abdomen were harvested , and the individual organs were imaged ( Figure 2D ) . In the thorax , the greatest signal originated from the thymus , whereas the signal from the lungs was only slightly above background . In the abdomen , the pancreas was the most luminescent organ . The spleen and the liver also emitted significant luminescence . On average , a ten-fold higher luminescence signal was observed in the harvested pancreas compared to the liver . This suggests that the liver , the critical target organ of the disease , was not among the main replication sites for RVFV at this early stage of infection . At 16 h post-infection , the whole body luminescence was higher than at 8 h post-infection ( Figure 2A ) . The signal spread out the abdominal cavity . The high intensity of luminescence in the abdominal cavity precluded detection in other locations unless integration was limited to the thorax ( Figure 2C ) . Dissection and ex vivo imaging showed a gradual increase of luminescence in the pancreas and in the liver . Additional sources of luminescence were the intestine mesentery , kidneys , ovaries and uterus in females , the seminal vesicles , preputial glands , epididymis and testis in males ( Figure 2D , and data not shown ) . The intensity of these signals was quite similar to that measured in the spleen and in the liver ( data not shown ) . At 34 h post-infection , the intensity of the signal led to saturation of the camera using a 0 . 5 s integration period , thus preventing identification of individual organs ( Figure 2A ) . Ex vivo imaging revealed that the highest signal was in the liver . Other organs with intense luminescence were the spleen , intestine mesentery and pancreas ( data not shown ) . Quantification of the luciferase expression in living mice during the time course of infection showed that the luminescence signal originated from the thymus remained constant from 8 h post-infection onwards , whereas luminescence profiles were increased in the liver and pancreas , suggesting a progressive increase of viral replication in these organs ( Figure 3 ) . To determine whether there was a correlation between the luminescence detected by the camera and the amount of virus genomes in the tissues , rZHΔNSs-hRLuc-infected Ifnar1−/− living mice were subjected to imaging . Next , the animals were euthanized and the organs harvested and imaged . Total RNAs were extracted from the organs and RVFV RNA copy numbers were measured by quantitative real time RT-PCR . We observed a highly significant correlation between the luminescence emitted by the pancreas in living mice and RVFV RNA copy number ( Figure 4A ) . Similarly , luminescence intensity significantly correlated with the RVFV RNA copy number in the harvested pancreas , spleen and liver ( Figure 4B–D ) . The ability of h–coelenterazine to cross the blood-brain barrier is unknown . To determine whether rZHΔNSs-hRLuc can infect the brain , we dissected and soaked the brain in an h-coelenterazine solution and imaged ( Figure 2D ) . At 8 h post-infection , the luminescence intensity was ten-fold higher in the brain from infected mice compared to control ( 104 photons/second/cm2/steradian [p/sec/cm2/sr] vs . 103 p/sec/cm2/sr ) , showing that the RVFV replicated in the brain at an early stage . Light emission increased through 16 h and 34 h post-infection to reach 7×105 and 7×106 p/sec/cm2/sr , respectively . Importantly , the intensity of luminescence in the brain was ten- to hundred-fold lower compared to the intensities in the thymus , pancreas , spleen , liver , and intestine mesentery , suggesting that the viral load was lower in the brain than in the thoracic and abdominal organs . RVFV can be transmitted through injection of infectious saliva from mosquito into the dermis or direct inhalation from body fluids , such as blood of infected animals [2] , [3] . To approximate these two natural routes of infection and to monitor their effects , we compared the light production after intraperitoneal , intradermal or intranasal inoculation of 104 PFU rZHΔNSs-hRLuc into Ifnar1−/− mice . Following intradermal inoculation of the ear pinna ( N = 5 ) , luminescence was first visible in the neck on the side of the injected ear , and in the abdominal cavity at 24 h post-infection ( Figure 5A ) . Histologic analysis established the source of the light in the neck; the neck signal came from the lymph nodes draining the injected ear ( data not shown ) . At 40 h post-infection , organs in the abdominal cavity , including the pancreas and the liver were highly luminescent . All mice succumbed to infection by 69 h post-infection , a survival time significantly longer than after i . p . inoculation ( P<0 . 025 ) . Luminescent virus inoculated intranasally was already detected 24 h post-infection in the abdomen . This mode of inoculation caused an interstitial pneumonia that initiated as a distinctive luminescence signal in the lungs from 48 h post-infection onwards ( Figure 5B ) . Mice infected intranasally ( N = 5 ) survived significantly longer than after i . p . inoculation ( P<0 . 0047 ) ; all were dead by 69 h post-infection . To clarify the identity of the RVFV target cells , we carried out histopathological analysis in RVFV-hRLuc-infected Ifnar1−/− mice at 8 , 16 and 34 h after i . p . inoculation . No significant histological lesions were observed at 8 and 16 h post-infection . By contrast , at 34 h post-infection , moderate to marked lesions were detected in the liver , lung , spleen , thymus , ovaries , and in the mesentery surrounding the pancreas ( Figure 6 , data not shown ) . No histological lesions were detected in the other organs . In the liver , lung and spleen , the lesions were similar to those previously reported in RVFV-infected wild-type mice [18] , [19] , [20] , [21] . Diffuse apoptosis of lymphoid cells have been previously reported in areas with or without RVFV antigen in the thymus of infected BALB/c mice [21] . Accordingly , we identified the thymus as one of the major targets of RVFV by bioluminescence . However , the pancreas and reproductive organs were also luminescent although none of these organs are known as tissue targets of RVFV . Therefore , to identify cell types that support RVFV replication in these organs , we studied tissue samples by histology and immunohistochemistry with antibodies against the RVFV . In the pancreas , no histological lesions were found in the exocrine or endocrine components ( Figure 6A ) . However , a multifocal inflammatory lesion was observed in the mesentery around pancreatic acini ( peritonitis ) , characterized by necrosis of adipocytes associated with infiltration of macrophages and neutrophils ( Figure 6A–C ) . Viral antigens were present only in the cytoplasm of macrophages ( Figure 6D ) and , more rarely , in neutrophils ( Figure 6E ) , confirming that the virus did not target the pancreatic exocrine or endocrine cells but macrophages . Similarly , in the ovaries , viral nucleocapsid-positive macrophages were seen in the stroma ( Figure 6F ) . Thus macrophages appeared as important cell targets for the replication of RVFV-hRLuc in Ifnar1-deficient mice . To examine whether macrophages are also cell targets for the replication of virulent RVFV in wild-type mice , histopathological and immunohistochemical analysis was performed in wild-type 129S2/SvPas mice ( N = 3 ) infected i . p . with 104 PFU ZH548 . Post-mortem analyses were carried out once mice displayed clinical signs , i . e . three to five days after the inoculation . Histopathological analysis of the pancreas and its mesentery revealed no peritonitis . However , numerous macrophages containing intracytoplasmic viral antigens were observed in the sinus of the pancreaticoduodenal lymph node ( Figure 6 , G and H ) . These macrophages occasionally displayed a hyperbasophilic and condensed nucleus , a morphological change that is characteristic for irreversible cell injury ( Figure 6 , I ) . Collectively , these results confirmed that macrophages are important cell targets of the RVFV in the mouse . To further dissect target cells of RVFV replication in Ifnar1-deficient mice , we used the recombinant virus rZHΔNSs-GFP that carries GFP in place of the NSs gene . We have shown previously that cells infected in vitro with rZHΔNSs-GFP are fluorescent upon excitation at 488 nm [24] . Ifnar1-deficient mice were either infected i . p . with 104 PFU rZHΔNSs-GFP ( N = 6 ) or mock-treated ( N = 5 ) . At 24 h post-infection , the spleen was dissected and single-cell suspensions were analyzed by flow cytometry for GFP expression . At this time point , 0 . 54% ( range 0 . 14–1 . 53% ) of the total hematopoietic cell population of the spleen from rZHΔNSs-GFP-infected mice expressed GFP whereas no GFP-positive cells were found in splenocytes after mock infection ( Figure 7A ) . We examined the expression of GFP in various subsets of antigen presenting cells based on the surface expression patterns of CD45 . 2 , CD11b , CD11c , Ly6G , CD19 , CD3 , NKp46 , CD115 and MHCII class II by multicolor flow cytometric analysis . Among the CD11b+ CD115+ Ly6G− ( macrophages ) , CD11c+ CD11b+ MHC II+ ( dendritic cells ) [44] and CD11b+ CD11c− Ly6G+ ( granulocytes ) , on average 5 . 58% ( range 2 . 15–8 . 71% ) , 4 . 5% ( range 0 . 82–8 . 49% ) and 1 . 96% ( range 0 . 05–6 . 07% ) cells expressed GFP , respectively ( Figure 7C [left panels] , B [right panels] , and C [right panels] , respectively ) . The percentage of GFP-expressing cells within the total cell population of spleen varied from one infected mice to another , indicating that the dynamics of RVFV infection progression was not identical in all individuals . However , each of the three subsets of immune cells was infected with the same efficiency in the different mice . This is shown by the fact that the ratio of GFP-expressing macrophages , dendritic cells or granulocytes was highly correlated with the ratio of GFP-expressing cells in the total cell population from the spleen ( Pearson correlation coefficient 0 . 97 , 0 . 85 and 0 . 79 , respectively ) . These findings suggest a distinct pattern of susceptibility to infection by the RVFV-GFP for different immune cells in the following order: CD11b+ CD115+ Ly6G− ( macrophages ) >CD11c+ CD11b+ MHC II+ ( dendritic cells ) >CD11b+ CD11c− Ly6G+ ( granulocytes ) . On average , less than 0 . 4% ( range 0–1 . 19% ) of NKp46+ CD3− natural killer ( NK ) cells were positive for GFP ( Figure 7E , left panel ) . Finally , GFP fluorescence was seen on average in only 0 . 25% ( range 0 . 07–0 . 57% ) of CD19+ CD3− ( B lymphocytes ) cells and 0 . 20% ( range 0 . 05–0 . 43% ) NKp46− CD3+ ( T lymphocytes ) cells ( Figure 7D and E , right panels ) . Thus , at 24 h post-infection , RVFV replicated in cells of the myeloid lineage , primarily in mononuclear phagocytic cells , such as macrophages , dendritic cells and granulocytes . To study the significance of virus replication in phagocytic cells in vivo , we injected intraperitoneally ( i . p . ) and intravenously ( i . v . ) clodronate-loaded liposomes ( CLL ) to Ifnar1-deficient mice prior to infection with RVFV . These liposomes are widely used to deliver clodronate to phagocytic cells , especially macrophages , and the accumulation of clodronate leads to irreversible metabolic damages , which will eventually result in apoptosis [45] . As reported previously [42] , [43] , the i . p . administration of CLL kills macrophages in the peritoneum and spleen of wild-type mice whereas i . v . administration affects mainly macrophages in the spleen and liver . We first analyzed the effect of CLL treatment on the phagocytic cell population of the blood and spleen of Ifnar1-deficient mice 24 h after i . p . and i . v . administration . Flow cytometric analysis was performed to compare the percentage of macrophages/monocytes , dendritic cells and granulocytes in samples from mice treated with CLL ( N = 3 ) and PBS liposomes ( PBSL ) ( N = 3 ) . The CLL treatment resulted in a 23-fold reduction of CD11b+ CD115+ cells ( monocytes ) and a 6-fold reduction of CD11b+ F4/80+-expressing macrophages in the blood and spleen , respectively . In addition , CD11b+ CD11c+ F4/80− cells ( dendritic cells ) were decreased 9-fold in the blood . By contrast , CD11b+ CD11c− Ly6G+ cells ( granulocytes ) were not depleted in the blood and spleen , as previously reported [46] . Altogether this analysis showed that , 24 h after CLL treatment , blood monocytes and dendritic cells and spleen macrophages were efficiently depleted in Ifnar1-deficient mice whereas granulocytes were not affected . Next , CLL- or PBSL-treated mice were infected intradermally with 104 PFU rZHΔNSs-hRLuc at 24 h after liposome administration . To investigate whether the clodronate treatment affected viral replication in vivo , we first observed PBSL- and CLL-administered infected mice using the imaging of whole bodies at 24 and 40 h post-infection . The profile of bioluminescence signals was similar in PBSL- and CLL-administered mice ( N = 5 in each group ) ( data not shown ) . However , we observed weaker signals in CLL-administered mice . Indeed , the signals in the liver region were on average fifteen and four-fold lower at 24 and 40 h post-infection respectively in CLL- administered mice compared to PBSL-treated mice ( P<0 . 05 ) . We then measured the viraemia at 24 and 40 h post-infection in PBSL- and CLL-administered mice . At 24 h post-infection , the CLL-administered mice ( N = 3 ) displayed lower blood plasma viral loads than the PBSL-treated mice ( N = 3 ) ( 4 . 0×103±2 . 9×103 vs . 1 . 2×104±2 . 7×103 PFU/ml , Mann-Whitney test , P = 0 . 0495 ) . Similar observations were made at 40 h post-infection ( 6 . 3×105±3 . 7×105 vs . 2 . 6×107±2 . 2×107 PFU/ml , Mann-Whitney test , P = 0 . 0495; P = 0 . 0039 when data from both time points were combined ) . Finally , the CLL-administered mice ( N = 12 ) survived for a longer period of time after challenge than the PBSL-treated mice ( N = 12 ) ( Figure 8 ) , suggesting that the depletion of monocytes/macrophages and dendritic cells prior to the viral infection affected the spreading and/or the containment of viral infection into cells of other organs . The mice showed no sign of encephalitis and succumbed to hepatitis . These observations confirmed the crucial role of monocytes/macrophages and dendritic cells during RVFV infection in Ifnar1−/− mice .
In vivo imaging studies using reporters , such as hRLuc and GFP , may provide a more complete picture of the spatiotemporal progression of a viral disease [47] , [48] . In this study , we report the use of recombinant RVFV-hRLuc and RVFV-GFP strains to investigate the in vivo dynamics of RVFV infection progression in living mice and identify the virus-expressing cells . The recombinant viruses were generated by replacing the NSs gene with the reporter gene . Hence , these viruses were avirulent in immunocompetent mice when compared with wild-type virus but they were highly pathogenic in mice lacking interferon-α/β receptor , enabling to use them for pathogenesis studies in this mouse model . We were able to detect luciferase reporter expression at early stages of infection in the main known sites of viral replication , the liver , the spleen , the thymus and the brain [18] , [21] , [49] , [50] , [51] . The pancreas appeared as an unexpected site of virus replication . Using ex vivo imaging and histological examination , we primarily identified macrophages infiltrating the adipose tissue surrounding the pancreas as primarily virus-expressing cells . Similarly , RVFV-expressing macrophages were identified in the stroma of the ovary . The RVFV-GFP confirmed the importance of macrophages as specific host cells for the virus in Ifnar1−/− mice . It further allowed the identification of dendritic cells and granulocytes as target cells for RVFV replication . Interestingly , only a low number of B- , T- and NK-cells expressed the GFP reporter . Viral antigens have been previously detected in mononuclear phagocytic cells and dendritic cells in the lymph nodes , spleen and thymus from infected wild-type mice [21] and in macrophages in the lymph nodes from infected rats [51] . Interestingly , Smith and colleagues [21] also noticed that lymphocytes did not appear stained with the RVFV antibody in agreement with our observations . Although RVFV replication in the human macrophage-like cell line U937 [52] and in cultured peritoneal macrophages from susceptible rats [53] have been previously documented , this is , to our knowledge , the first study to evaluate the infection rates of various subsets of cells of the myeloid lineage in vivo . Because the level of fluorescent GFP directly reflects the extent of transcription and replication of the recombinant virus , we assume that the virus is highly expressed in GFP-positive cells . However , since macrophages , dendritic cells and granulocytes are able to uptake cell debris , it is possible that some of these cells are GFP-positive following phagocytosis of debris of RVFV-GFP-infected cells in vivo . Phagocytic cells function as pathogen sensors . Macrophages and neutrophils provide the first line of defense following infections . Macrophages and dendritic cells are antigen presenting cells and play a crucial role in the establishment of the adaptive immune response . Infection with RVFV-GFP showed that phagocytic cells are also target cells for RVFV . We investigated the importance of the in vivo interaction between phagocytic cells and RVFV . We treated Ifnar1-deficient 129S2/SvPas mice with CLL to deplete the population of phagocytic cells , and showed that , following intradermal infection with RVFV , the depleted mice allowed reduced RVFV replication compared to control mice , as assessed both by in vivo imaging and viral titration from blood samples . Accordingly , CLL-treated mice displayed enhanced survival time compared with control mice , indicating that phagocytic cells are involved in the pathogenesis of RVF . Altogether , our data suggest that during the initial stages of infection of Ifnar1−/− mice , the virus replicates inside macrophages and dendritic cells . On the other hand , since the RVFV replicates in diverse cell types in peripheral tissues , the infection may progress rapidly and lead to acute hepatitis and death . RVFV is thought to be transmitted primarily by bites of infected mosquitoes , by direct contact with infected body fluids or through airborne transmission . It has been confirmed that exposure of mice to aerosols containing RVFV is able to induce infection [54] . Following inoculation into a dermal site , RVFV-hRLuc expression was seen in the draining lymph node which became the main site of replication early after infection while the virus was still weakly detected into the abdominal cavity . Later , virus spread and caused severe hepatitis within 69 h post-infection . Following intranasal inoculation , virus replicated in the lung where it caused pneumonia within 48 h post-infection . Its dissemination to the abdominal cavity was rapid and mice succumbed at 69 h post-infection . Thus , typical routes of exposure were associated with clear differences in the spatial and temporal progression of RVFV and caused delayed death compared with i . p . inoculation . Type I interferons ( IFNs ) are essential elements during host antiviral defense [55] . Both recombinant RVFV strains inoculated i . p . were able to kill Ifnar1-deficient 129S2/SvPas mice within 2 days whereas wild-type 129S2/SvPas mice survived infection , indicating that a functional IFN-α/β pathway is critical for the protection of mice from fatal infection with these attenuated viruses . We showed that the recombinant viruses could replicate in known target tissues and cells of RVFV . It is not clear whether in the absence of the IFN-α/β receptor , the reporter RVFV can replicate in tissues and cells that are not normally susceptible to infection with a fully virulent RVFV in wild-type mice . Hence , we infected wild-type 129S2/SvPas with the virulent RVFV ZH548 strain and observed infected macrophages in the spleen and pancreaticoduodenal lymph node . However , we failed to identify the peritonitis seen repeatedly in recombinant RVFV-infected Ifnar1−/− mice . This suggests that , in Ifnar1−/− mice , cells of the macrophage lineage displayed an increase susceptibility to RVFV compared to wild-type mice . The high susceptibility of cells of the macrophage/dendritic lineage to viral infection in the absence of a functional type I IFN system has been previously observed . An increased infection of cells of the macrophage/dendritic lineage was observed in Ifnar1−/− mice infected with either the Sindbis virus [56] , or the mouse hepatitis virus [57] . Similarly , macrophages showed the greatest increase in susceptibility among the different splenocyte populations in West Nile virus-infected Ifnar1−/− mice compared to wild-type mice [58] . More generally , an increase in the replication of viruses in tissues and cells normally susceptible to virus infection has been previously observed in Ifnar1−/− mice . Coxsackievirus replicated dramatically in the liver of Ifnar1-deficient compared with wild-type mice [59] . Finally , previous investigation of poliovirus replication sites in infected Ifnar1−/− mice expressing the human poliovirus receptor showed that nontarget tissues became potentially permissive for virus infection when IFNα/β signaling was disrupted [60] . Therefore , the fact that Ifnar1−/− mice inoculated with NSs-deficient RVFV strains develop acute hepatitis and eventually die , as wild-type mice infected with a virulent RVFV strain , does not mean that the exact mechanisms of the cellular pathogenesis are the same in Ifnar1−/− and wild-type mice . Thus , although Ifnar1−/−mice have proven to be a tractable system in which to study the progression of RVFV infection in vivo , the immunocompromised nature of this mutant strain remains a limitation in translating these results directly to wild-type mice . Additional work needs to be done to develop similar whole-organ imaging and flow cytometry analysis in immune-competent mice . Further studies involving the use of fully virulent RVFV – i . e . carrying the NSs gene – which express a reporter gene , might allow us to give a comprehensive picture of the dynamics of natural infection in mammals . Our work provides the basis for the use of bioluminescent and fluorescent RVFV to study the effects of specific mutations in the viral genome and of host genetic factors on the tissue tropism and replication kinetics in living mice . | Rift Valley fever , caused by a member of the Bunyaviridae family , has spread during recent years to most sub-Saharan African countries , in Egypt and in the Arabian peninsula . The virus can be transmitted by insect vectors or by direct contacts with infectious tissues . The analysis of virus replication and dissemination in laboratory animals has been hampered by the need to euthanize sufficient numbers of animals and to assay appropriate organs at various time points after infection to evaluate the viral replication . By following the bioluminescence and fluorescence of Rift Valley fever viruses expressing light reporters , we were able to track the real-time dissemination of the viruses in live immunodeficient mice . We showed that the first infected organs were the thymus , spleen and liver , but the liver rapidly became the main location of viral replication . Phagocytes also appeared as important targets , and their systemic depletion by use of clodronate liposomes decreased the number of viruses in the blood , delayed the viral dissemination and prolonged the survival of the infected mice . | [
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] | 2011 | Tissue Tropism and Target Cells of NSs-Deleted Rift Valley Fever Virus in Live Immunodeficient Mice |
For the vast majority of species – including many economically or ecologically important organisms , progress in biological research is hampered due to the lack of a reference genome sequence . Despite recent advances in sequencing technologies , several factors still limit the availability of such a critical resource . At the same time , many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map . We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing . We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples , such as in this case gene-bearing minimum-tiling-path BAC clones . The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones ( deconvolution ) so that the assembly can be carried out clone-by-clone . Experimental results on simulated data for the rice genome show that the deconvolution is very accurate , and the resulting BAC assemblies have high quality . Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality . While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project , we show that it is quite successful in reconstructing the gene sequences within BACs . In the case of plants such as barley , this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding .
The second generation of DNA sequencing instruments is revolutionizing the way molecular biologists design and carry out investigations in genomics and genetics . These new sequencing technologies ( e . g . , Illumina , ABI SOLiD ) can produce a significantly greater number of reads at a fraction of the cost of Sanger-based technologies , but with the exception of Roche/454 and Ion Torrent ( ABI ) read lengths are only 50–150 bases . While the number ( and to a lesser extent the length ) of reads keeps increasing at each update of these instruments , the number of samples that can be run has remained small ( e . g . , two sets of eight independent lanes on the Illumina HiSeq ) . Since the number of reads produced by the instrument is essentially fixed , when DNA samples to be sequenced are relatively “short” ( e . g . , BAC clones ) and the correspondence between reads and their source has to be maintained , several samples must be “multiplexed” within a single lane to optimize the trade-off between cost and sequencing depth . Multiplexing is traditionally achieved by adding a DNA barcode to each sample in the form of an additional ( oligo ) adapter , but this does not scale readily to thousands of samples . Although it is possible to exhaustively barcode such a number of objects [1] , the procedure of preparing ( and balancing in multiplexes ) thousands to ten of thousands of barcoded libraries for sequencing is very labor-intensive and can be quite expensive . Additionally , the resulting distribution of reads for each barcoded sample can be severely skewed ( see , e . g . , [2] , [3] ) , necessitating rounds of selective follow-up . Here , we demonstrate that multiplexing can be achieved without exhaustive barcoding by taking advantage of recent advances in combinatorial pooling design ( also known as group testing ) . The essence of this work is to significantly reduce the burden of library production , without severely compromising on the sequencing coverage of each BAC . Combinatorial pooling has been used previously in the context of genome analysis , but here we attempt to use it for de novo genome sequencing . Earlier works use a simple grid design that can be very vulnerable to noise and behaves poorly when several objects are positive; a simple grid design is also far from optimal in terms of the number of pools it produces [4]–[7] . Recent works use more sophisticated pooling designs in combination with second-generation sequencing technology [8]–[12] . The application domain of “DNA Sudoku” is the detection of microRNA targets in Arabidopsis and human genes [9] , whereas [8] , [10] are focused on targeted resequencing ( i . e . , when a reference genome is available ) . Pooling designs have also been used to recover novel or known rare alleles in groups of individuals [11] , [12] . In our approach to de novo sequencing , subsets of non-redundant but overlapping genome-tiling BACs are chosen to form intersecting pools . Each pool is then sequenced individually on a fraction of a flowcell via standard multiplexing . Due to the short length of a BAC ( typically 130 kb ) , cost-effective sequencing requires each sequenced sample to contain thousands of BACs . Assembling short reads originating from a mix of hundreds to thousands of BACs is likely to produce low-quality assemblies , as the assembler is unable to partition the reads according to individual BACs . Moreover , resulting contigs would not be assigned to a specific BAC . If instead reads could be assigned ( or deconvoluted ) to individual BACs , then the assembly could proceed clone-by-clone . We demonstrate that this objective can be achieved by choosing a pooling strategy in which each BAC is present in a carefully designed set of pools such that the identity of each BAC is encoded within the pooling pattern ( rather than by its association with a particular barcode ) . We report experimental results on simulated data on the genome of Oryza sativa ( rice ) and real sequencing data on the genome of Hordeum vulgare L . ( barley ) .
While our method can in general be applied to any set of clones that cover a genome or a portion thereof , the protocol we describe here for selective genome sequencing uses a physical map of ( gene-bearing ) bacterial artificial chromosomes ( BACs ) to identify a set of minimally redundant clones . A physical map is a partial ordering of a set of genomic clones ( usually BACs ) encompassing one or more chromosomes . A physical map can be represented by a set of unordered contigs , where each contig is a set of overlapping clones . A physical map is usually obtained by digesting BAC clones via restriction enzymes into DNA fragments and then measuring the length of the resulting fragments ( restriction fingerprints ) on an agarose gel . The smallest set of clones that spans the region represented by the physical map is called minimum tiling path ( MTP ) . The construction of a physical library and the selection of a MTP from a physical map are well-known procedures , and many organisms now have these resources available . More details can be found in , e . g . [14] , [16]–[19] , and references therein . Once the set of clones to be sequenced has been identified , they must be pooled according to a scheme that allows the deconvolution of the sequenced reads back to their corresponding BACs . In Combinatorics , the design of a pooling method reduces to the problem of constructing a disjunctive matrix [20] . Each row of the disjunctive matrix corresponds to a BAC to be pooled and each column corresponds to a pool . Consider a subset of the rows ( BAC clones ) in the disjunctive matrix , and let be the set of pools that contain at least one BAC in . A design ( or a matrix ) is said to be -decodable if when , , and . The construction of -decodable pooling designs has been extensively studied [20] . The popular 2D grid design is simple to implement but cannot be used for the purposes of this work because it is only one-decodable . Recently , a new family of “smart” pooling methods has generated considerable attention [8]–[10] , [15] , [21] , [22] . Among these , we selected the shifted transversal design [15] due to its ability to handle multiple positives and its robustness to noise . The parameters of a shifted transversal design pooling are defined by three integers , where is a prime number , defines the number of layers , and is a small integer . A layer is one of the classes in the partition of BACs and consists of exactly pools: the larger the number of layers , the higher is the decodability . By construction the total number of pools is . If we set to be the smallest integer such that where is the number of BACs that need to be pooled , then the decodability of the design is . In [15] , the shifted transversal design is defined by parameters where is the number of samples to be pooled , is the prime corresponding to the number of pools in each layer , and is the number of layers . In this paper , we use instead of , and instead of . An important property of this pooling design is that any two BACs share at most only pools . By choosing a small value for one can make pooling extremely robust to deconvolution errors . In our experiments , we use , so that at least ten errors are needed to mistakenly assign a read to the wrong BAC . In contrast , two errors are sufficient to draw an erroneous conclusion with the 2D grid-design . Barley BAC pools were obtained as follows . Escherichia coli strain DH10B BAC cultures were grown individually in 96-well plates covered by a porous membrane for 36 hr in 2YT medium with 0 . 05% glucose and 30 g/ml chloramphenicol at 37°C in a shaking incubator . Following combinatorial pooling of 50 l aliquots from each of 2197 BAC cultures , each of 91 collected pools ( 169 BACs , 8 . 3 ml each ) was centrifuged to create cell pellets . The pellets were frozen and then used for extraction of BAC DNA using Qiagen plasmid DNA isolation reagents . Each BAC pool DNA sample was then dissolved in 225 l of TE buffer at an estimated final concentration of 20 ng/l . For gene-BAC assignment using the Golden Gate assays , a total of 10 l ( 200 ng ) of this DNA was then digested for 1 hour at 37°C by using 2 units of NotI enzyme with 100 g/ml BSA in a volume of 100 l . The NotI enzyme was then heat inactivated at 65°C for 20 min . DNA for three sets of BACs ( HV4 , HV5 , HV6 ) were prepared using a procedure that yields on average about 60% BAC DNA and 40% E . coli DNA . DNA for set HV3 was prepared using a procedure that was expected to yield about 90% BAC DNA and 10% E . coli DNA . Although these DNA samples performed well for SNP locus detection in the GoldenGate assay , we were unaware of the extent of E . coli in the samples until we began BAC pool sequencing , after all BAC pool DNAs had been prepared . The HV3 set reported here replaced an earlier set containing more E . coli DNA . Attempts were made to remove E . coli DNA from the BAC DNA samples through selective digestion by using exonucleases , and to reduce highly repetitive DNA using a denaturation/renaturation and double strand nuclease method . These procedures provided little or no reduction of the proportion of E . coli DNA in the samples . A cost-benefit analysis determined that the cost of replacing all of the BAC pools by applying an alternative BAC DNA purification procedure yielding an average of 90–92% BAC DNA and 8–10% E . coli DNA would be no more advantageous than simply repeating the sequencing of samples for which more DNA sequence information was needed to support the sequence-to-BAC deconvolution . A video showing 76 seconds of the pooling process is available as Video S1 . Sequencing of the barley BAC pools was carried out on an Illumina HiSeq 2000 at UC Riverside . Paired-end reads from each pool were quality-trimmed using a sliding window and a minimum Phred quality of 23 . Next , Illumina PCR adapters were removed with Far ( Flexible Adapter Remover , http://sourceforge . net ) , and the remaining sequence discarded either if shorter than 36 bases or if containing any ‘N’ . Finally , reads were cleaned of E . coli ( DH10B ) and vector contamination ( pBeloBAC11 ) using BWA [23] and custom scripts . According to our simulations , the sequencing depth of each BAC after deconvolution is required to be at least 50x to obtain good BAC assemblies . The parameters of the pooling design should be chosen so that the coverage pre-deconvolution is at least 150x–200x to compensate for non-uniformity in the molar concentrations of individual BACs within each pool , and for losses due to sequencing errors . To understand how deconvolution is achieved , let us make the simplifying assumption that clones in the MTP do not overlap . i . e . , that the MTP BACs form a non-redundant tiling for the genome under study , or a fraction thereof . Let us pool the MTP BACs according to a shifted transversal design with layers and obtain a set of reads from them . Now , consider a read occurring only once in the portion of the genome covered by the BACs . If there are no sequencing errors and sequencing depth is sufficient , will appear in the sequenced output of exactly pools ( see Figure 2 , case 1 ) . To determine the BAC to which read should be assigned , a search is made for the BAC signature that matches the list of positive pools for . For the most realistic scenario where at most MTP clones overlap , the pooling must be at least -decodable for the deconvolution to work . We expect each non-repetitive read to belong to at most two BACs if the MTP has been computed perfectly , or rarely three BACs when considering imperfections , so we set . When a read belongs to the overlap between two clones ( again assuming no sequencing errors ) , it will appear in the sequenced output for pools ( see Figure 2 , case 2 ) . The case for three overlapping clones ( see Figure 2 , case 3 ) is analogous . Recall that in step E each BAC is assigned to pools , thus the signature of a BAC is a set of numbers in the range , where the first number belongs to the range , the second belongs to , … , and the last one belongs to . In our pooling design two BAC signatures cannot share more than numbers ( see Theorem I in [15] ) . One can think of BAC signatures as -dimensional vectors which are rather “far” from each other . Our deconvolution method proceeds as follows . First , recall the notion of k-mer of a string ( read ) as a contiguous substring of of length . If we denote by the length of string , observe that has -mers , not necessarily distinct . Let us call the set of reads obtained by sequencing pool , for all , where ( and in our pooling design ) . For each set , we first compute the number of occurrences of each of its distinct -mers . Specifically , for each -mer ( i . e . , for each appearing in a read ) , if or its reverse complement occurs exactly times in . These counts are stored in a hash table such that , given a -mer , we can efficiently retrieve a count vector of numbers , namely . Once the table is built , we process each read as follows . Given a read in pool , we fetch the count vectors for each of its -mers . Given a -mer , where , let be the number of positive ( non-zero ) entries in its count vector , i . e . , the number of pools where occurs at least once . Several scenarios are possible: At the end of this process , we consider the subset of -mers that have been assigned to one , two or three BACs and compute the union of their signatures , which becomes the signature of read . If a perfect match between the read signature and BAC signatures ( either one , or the union of two or three ) is found , then the read is assigned to the corresponding BAC ( s ) . Reads for which no such match is found are declared non-deconvolutable and saved in a separate file . This algorithm is implemented in the tool HashFilter , which has been extensively tested under Linux and MacOS . Source code and manual can be downloaded from http://www . cs . ucr . edu/stelo/hashfilter/ , under the GNU General Public License . Once the reads have been assigned to individual BACs , sets of single and paired-end reads are assembled clone-by-clone using Velvet [24] . Velvet requires an expected coverage , which can be computed using the amount of sequenced bases assigned to each BAC and the estimated BAC size . For barley , BAC sizes were estimated from the number of bands in the restriction fingerprinting data . First , we computed the average number of bands in the 72 , 055 BACs fingerprinted using high-information-content fingerprinting [16] , [17] ( see also http://phymap . ucdavis . edu/barley/ ) . Assuming that the average BAC length in this set was 106 kb , we computed the multiplier to apply to the number of bands to obtain the estimated BAC length , which turned out to be 1175 bases . We used that constant to obtain estimated sizes for all BACs in HV3 , HV4 , HV5 and HV6 ( see Dataset S4 ) . Note that the average size is over 125 kb , much larger than the library average size of 106 kb; this indicates that the MTP selection favors larger BACs . We also tested SOAPdenovo [25] and Abyss [26] on simulated data , but there were no obvious performance benefits compared to Velvet in terms of assembly quality ( data not shown ) . We evaluated the assembly for several choices of the -mer ( hash ) size , but have reported only the assembly that maximized the N50 ( N50 indicates the minimum length of all contig/scaffolds that together account for at least 50% of the genome ) . We recorded the number of contigs , their N50/median/max/sum statistics , and the number of reads used in the assembly . For rice assemblies , we Blast-ed the BAC contigs to the rice genome . We computed the fraction of the original ( source ) BAC covered by at least one contig , and the number of gaps and overlaps in the assembly . The parameters used for Blast are reported in the legend of Dataset S4 . For barley BAC assemblies , we carried out a validation based on the known BAC-unigene associations from the Illumina GoldenGate assay described in the next section . The validation involved Blast-ing EST-derived unigenes ( Harvest:Barley assembly #35 unigenes , http://harvest . ucr . edu ) against the BAC assemblies . To reduce spurious hits , we applied three filters . First , we masked highly repetitive regions by computing the frequency of all distinct 26-mers in the cleaned/trimmed HV5 data , then masking any -mers that occurred at least 11 , 000 times in the reads used for the assembly ( 80 copies in the genome ) from the assembled contigs , by replacing the occurrences of those repetitive -mers with Xs . Second , we ignored any BAC contig that covered a unigene for less than 50% of its length . Third , we excluded from the hit count any unigene that hit more than ten individual BACs overall . We recorded the number of unigenes hitting a BAC , and compared them with the expected unigenes according to the Illumina assay . Samples for the GoldenGate assay were prepared by combining 5 l of NotI-digested BAC pool DNA ( 10 ng ) with 4 l of sonicated E . coli DNA pre-dialyzed into TE buffer at a concentration of 500 ng/l ( 2000 ng ) and 16 l of TE buffer . The final volume of each sample was thus 25 l , composed of 0 . 4 ng/l of digested BAC pool DNA and 80 ng/l of additional E . coli DNA . These DNA samples were provided to Joe DeYoung at the University of California , Los Angeles , California , or to Shiaoman Chao at the US Department of Agriculture genotyping facility in Fargo , North Dakota . The DNA concentrations were then readjusted to 50 ng/l and a total of 5 l of each DNA sample was used for each GoldenGate assay . Each Illumina GoldenGate oligonucleotide pool assay ( OPA ) allows interrogation of a DNA sample for the presence of 1536 SNP loci . In [27] , five OPAs were designed from approximately 22 , 000 SNPs from EST and PCR amplicon sequence alignments . Details of the development of three test phase ( POPA1 , POPA2 , and POPA3 ) and two production scale ( BOPA1 and BOPA2 ) can be found in [27] . We genotyped the barley BAC pools described in Section “The gene space of barley” on BOPA1 and BOPA2 . The output from the Illumina GoldenGate assay was first converted to binary data by visual inspection of the theta/R space in BeadStudio . A positive reading meant that the SNP locus ( and its corresponding unigene ) is present in at least one BAC within the pool ( refer to Figure S1 for an example ) . Given the genotyping data for all unigene-pool pairs , we designed an algorithm that computes the optimal assignment of unigenes to BACs so that the number of errors is minimized . For a particular unigene under consideration , let be the signature set of corresponding positive pools . Let be an arbitrary set of BACs , where , and be the union of the pools that contain at least a BAC clone in . The number of errors associated with this particular choice of is defined to be the number of extra observations ( equal to ) plus the number of missing observation ( equal to ) . Among all possible choices of , we chose such that the value of is minimized . When the number of errors associated with the final solution was too large ( more than three ) , we declared that unigene to be non-decodable . This procedure resulted in 1849 unigenes mapped to one , two , or three BACs . As a verification step , when a unigene was mapped to more than one BAC , we checked whether all those BACs belonged to the same contig in the barley physical map [18] , [19] . Using the genetic map developed in [27] , [28] we were also able to assign these unigene-anchored BACs to genetic map positions ( Dataset S6 ) and to check whether a BAC was associated with more than one genetic map position . The unigene-BAC error rate from these cross-checking methods appeared to be about 5% . The whole genome shotgun sequencing of barley was carried out at several locations: Ambry Genetics ( Aliso Viejo , California ) sequenced five ( 277 bases ) paired-end lanes and four long-insert paired-end ( LIPE ) lanes ( insert size of 2 , 3 and 5 kb ) ; University of Minnesota ( courtesy of G . Muehlbauer ) sequenced two ( 2100 bases ) paired-end lanes; UC Riverside sequenced seven ( 2100 bases ) paired-end lanes . The number of usable paired-end bases after quality-based trimming was 159 . 31 Gb and 4 . 92 Gb of LIPE , for an overall 31x sequencing depth of the 5 . 3 Gb barley genome . An -mer ( ) analysis showed that the effective depth of coverage of the data was about 24x [29] .
The physical map for Oryza sativa was assembled from 22 , 474 BACs fingerprinted at AGCoL , and contained 1 , 937 contigs and 1 , 290 singletons . From this map , we selected only BACs whose sequence could be uniquely mapped to the rice genome . We computed an MTP of this smaller map using our tool FMTP [14] . The resulting MTP contained 3 , 827 BACs with an average length of kb , and spanned 91% of the rice genome ( which is Mb ) . The overlap between rice BACs is significant: 1555 BACs overlap another BAC by at least 50 Kb , and 421 BACs overlap another BAC by at least 100 Kb ( see Figure S2 ) . In general , our method makes no assumption on the shared sequence content for pooled BACs . We pooled in silico a subset of 2 , 197 BACs from the set above according to the shifted transversal design [15] . This pooling design is defined by three parameters ( see Materials and Methods for a detailed description of the properties of the pooling design ) . First observe that if the MTP was truly a set of minimally overlapping clones , a two-decodable pooling design would be sufficient . We decided that a three-decodable pooling scheme would give additional protection against errors and imperfections in the MTP . Taking into consideration the format of the standard 96-well plate and the need for a 3-decodable design , we chose parameters , and , so that and . Each of the layers consisted of pools , for a total of 91 BAC pools , which left some space for a few control DNA samples on a 96-well plate . In this pooling design , each BAC is contained in pools and each pool contains BACs . We call the set of pools to which a BAC is assigned , the BAC signature . Any two BAC signatures can share at most pools , and any triplet of BAC signatures can share at most pools . Specifically , 57 . 9% of any BAC signature pairs have no pool in common , 30 . 6% share one pool , and 11 . 5% share two pools . For triplets of BAC signatures , 18 . 5% have no pool in common , 32% share one pool , 29 . 6% share two pools , 14 . 8% share three pools , 4 . 5% share four pools , 0 . 6% share five pools , and 0 . 01% share six . The 91 resulting rice BAC pools were “sequenced” in silico by generating paired-end reads of 104 bases with an insert size of 327 bases , and 1% sequencing error distributed uniformly along the read . A total of 208 M usable bases gave an expected x sequencing depth for a BAC in a pool . As each BAC is present in seven pools , this is an expected x combined coverage . The 91 read pools were processed for deconvolution using the -mer based algorithm presented in the Materials and Methods section . We set because we wanted to detect an overlap between two reads of bases with a length of at least 75% ( 78 bases ) and at most two mismatches ( observe that if the two errors are equally spaced along the 78 overlapping bases , a perfect match of length 26 must occur ) . The computation was relatively quick , but required a significant amount of memory . The construction of the hash table required about 120 GB of RAM and 164 minutes running on one core of a Dell PowerEdge T710 server ( dual Intel Xeon X5660 2 . 8 Ghz , 12 cores , 169 GB RAM ) . The deconvolution phase took 33 minutes on 10 cores; sorting the reads into 2 , 197 files took 22 minutes on one core . Figure 3- ( a ) illustrates the distribution of signature sizes for all the distinct -mers in the rice dataset . Observe that the distribution has clear peaks around , around the interval and the interval . These peaks correspond to -mers originating from one , two , and three overlapping BACs , respectively ( see Figure 2 ) . We also have a rather large number of -mers appearing in 1–6 pools . Observe that if the sequencing depth was sufficient , and in the absence of technical errors with BACs for a long -mer to have fewer than occurrences , sequencing errors must have occurred . Figure 3- ( b ) shows the distribution of signature sizes for all the reads in the rice dataset at the outset of our deconvolution algorithm ( presented in the Materials and Methods section ) . Observe that the vast majority of reads now have a signature size in the expected ranges , with the exception of reads that appear in more than 80 pools . This latter set of reads cannot be deconvoluted and is discarded . The set of reads with a signature of size 7 , 12–14 or 15–21 that could be deconvoluted was % of the total ( see Table S2 and Dataset S1 ) . Since we knew the BAC from which each read was generated , we determined that % of the deconvoluted reads were assigned to either the correct BAC or to a BAC overlapping the correct BAC ( see Table S2 and Dataset S4 ) . After deconvolution , the average sequencing depth for each BAC was x , about 50% higher than the expected x . Even if we are losing about % of the reads due to invalid signatures , deconvoluted reads are frequently assigned to multiple BACs , thereby amplifying the sequencing depth . Part of this inflation can be attributed to the overlap between BACs in the rice MTP ( see Figure S2 ) . In the final step of the protocol , we independently assembled the set of reads assigned to each BAC . We carried out this step with Velvet [24] for each of the 2 , 197 BACs , for a variety of choices of -mer size ( hash length ) and reported only the assembly that maximized the N50 . This is an arbitrary choice that does not guarantee the “best” overall assembly . Sheet 1 in Dataset S4 summarizes the results . If we average assembly statistics over all the 2 , 197 BACs , the percentage of reads used in the assembly was 82 . 3% , the average number of contigs was 41 , the average N50 was 47 , 551 bp ( 31 . 4% of the average BAC length ) , the average largest contig was 57 , 258 bp ( 37 . 8% of the average BAC length ) , the average sum of all contig sizes was 137 , 050 bp ( 90 . 7% of the average BAC length ) . The N50 is quite high , and so is the percentage of reads used by the assembler . While these numbers already indicate high quality assemblies , we determined whether BACs were correctly assembled by Blast-ing BAC contigs against the rice genome . Sheet 1 in Dataset S4 reports the results of this analysis . Considering these statistics over all the 2 , 197 BACs , the average BAC coverage was 76 . 8% , the average gap size was 263 bp , the average number of gaps was 138 , the average overlap size was 107 bp , and the average number of overlaps was 75 . To establish a comparison “baseline” for these assembly statistics , we considered the most optimistic scenario of a “perfect deconvolution” , which entails using the provenance annotation of each read to assign it back to the correct BAC with 100% accuracy . Sheet 2 in Dataset S4 reports the same statistics for all 2 , 197 BAC assemblies in this best-case scenario . If we compute the average over all the 2 , 197 BACs , the average fraction of the reads used by Velvet was 82 . 7% and the average N50 was 132 , 865 bp ( 88% of the average BAC length ) . The Blast statistics showed an average BAC coverage of 96 . 3% , an average gap size of 52 bp , an average number of gaps of 97 , an average overlap size of 29 bp , and an average number of overlaps of 54 . While this latter BAC coverage is about 20% higher , the results following deconvolution compare quite favorably with what would be possible sequencing each BAC separately . Barley's diploid genome size is estimated at 5 , 300 Mb and is composed of at least 80% highly repetitive DNA , predominantly LTR retrotransposons [30] . We started with a 6 . 3x genome equivalent barley BAC library which contains 313 , 344 BACs with an average insert size of 106 kb [31] . Nearly 84 , 000 gene-enriched BACs were identified , mainly by the overgo probing method [32] . Gene-enriched BACs were fingerprinted using high-information-content fingerprinting [16] , [17] . From the fingerprinting data a physical map was produced [18] , [19] and a MTP of about 15 , 000 clones was derived [14] . Seven sets of 2 , 197 clones were chosen to be pooled according to the shifted transversal design [15] , which we internally call HV3 , HV4 , … , HV9 ( HV1 and HV2 were pilot experiments ) . We used the same pooling parameters discussed in the previous section ( , and ) . Here we are reporting on four out of seven sets , namely HV3 , HV4 , HV5 , and HV6 . Each is comprised of pools with a total of 2 , 197 MTP gene-rich barley BAC clones . Given the estimated 129 . 5 kb size of a BAC in the barley MTP ( see Materials and Methods ) , the total complexity of each pool of 169 BACs is Mb and of the 2 , 197 BACs is Mb . To take advantage of the high density of sequencing of the Illumina HiSeq2000 , 13–16 pools were multiplexed on each lane , using custom multiplexing adapters . After each sample was sequenced and the reads demultiplexed , we obtained an average of 22 . 9 M , 11 . 3 M , 11 . 5 M , and 10 . 1 M reads per pool in HV3 , HV4 , HV5 , and HV6 , respectively , with a read length of 92 bases . Reads were quality-trimmed and cleaned of spurious sequencing adaptors , and then reads derived from E . coli contamination or the BAC vector were discarded ( see Dataset S2 ) . The percentage of E . coli contamination was rather high , averaging around 41% , 40% , 51% , 65% in HV3 , HV4 , HV5 , and HV6 , respectively . An alternative DNA purification method we used for HV3 showed the potential to lower amount to 8–15% if properly executed ( see some of HV3 pools in Dataset S2 , column I ) . The average number of usable reads after trimming and cleaning was about 13 . 5 M , 6 . 8 M , 5 . 5 M , and 3 . 6 M per pool in HV3 , HV4 , HV5 , and HV6 , respectively , with an average high quality read length of 87–89 bases . The number of reads in the set of 91 pools ranged between about 4 . 2 M–27 . 7 M in HV3 , 2 . 9 M–17 M in HV4 , 2 . 5 M–11 . 3 M in HV5 , and 1 . 6 M–15 M in HV6 . The total number of reads was about 1229 M , 620 M , 503 M , and 327 M in HV3 , HV4 , HV5 , and HV6 , respectively , for a total of about 109 . 1T , 54 . 9T , 44 . 8T , and 28 . 5T usable bases , respectively . The 91 read pools in the barley datasets were processed using the -mer based algorithm ( HashFilter with = 26 ) described in the Materials and Methods section . The computation took slightly longer than on the analogous rice dataset ( i . e . , about 363 minutes on one core of a Dell PowerEdge T710 server to build the hash table for HV5 ) , but used less memory ( i . e . , about 43 Gb of RAM for HV5 ) . For HV5 , the deconvolution phase took 169 minutes on 10 cores , and the sorting of reads into 2 , 197 BAC files took 37 minutes on one core . Due to the higher repeat content of the barley genome compared to rice , we were able to deconvolute a smaller fraction of the barley reads , about 68 . 14% for HV3 , 59 . 9% for HV4 , 71 . 3% for HV5 , and 58% for HV6 ( see Tables S3 , S4 , S5 , S6 and Dataset S3 ) . Figure 3- ( c ) and ( d ) illustrate the distribution of signature sizes for all the distinct -mers and for all reads in HV5 . As expected , the number of reads occurring in over 80 pools is much higher for barley than for rice . After deconvolution , the total number of usable bases was about 97 . 9T for HV3 ( about 90% of the bases before deconvolution ) , 34 . 6T for HV4 ( about 63% ) , 38 . 9T for HV5 ( about 87% ) , and 19 . 3T for HV6 ( about 68% ) , which translated to an average sequencing depth of coverage for each BAC of about 431x in HV3 , 134x in HV4 , 137x in HV5 , and 72x in HV6 ( see Dataset S4 ) . On the set HV5 , we tested HashFilter for different choices of the -mer . For , , and , the memory used by HashFilter was about 35 GB , 40 GB , and 48 GB , respectively ( compared to 43 Gb of RAM for ) . On one CPU core , the time to build the hash table was about 323 minutes for , 338 minutes for , and 915 minutes for , compared to 363 minutes for . On ten CPU cores , the overlap phase took 108 minutes for , 165 minutes for , and 244 minutes for , compared to 169 minutes for . In terms of the number of reads deconvoluted , for , , and , we deconvoluted 62 . 2% , 68 . 2% , and 73 . 6% of the reads in HV5 , respectively ( compared to 71 . 3% when – see Dataset S3 for more details ) . We have collected strong evidence that the deconvolution accuracy was quite high in barley . For instance , six BACs that were assigned less than 20 reads in HV5 were noted as not growing during the pooling ( for a video of the pooling see Video S1 ) . For two HV sets , we realized that we erroneously swapped two adjacent pools after noticing that the percentage of deconvoluted reads for those pools was significant lower than the average . Later we confirmed the swap by mapping the reads in those pools to the unigenes that were known to be present in the pooled BACs . During the same investigation , we also assessed how the overall percentage of deconvolution would be affected if one pool was missing . We removed all the reads in a pool of HV3 , and re-executed the deconvolution algorithm: the number of deconvoluted reads decreased by less than 0 . 5% , a very small loss considering that an entire pool was removed . We also carried out an analysis of deconvoluted paired-end reads for HV5 to determine to what extent the left and the right mate agreed on their BAC ( s ) assignment . The deconvolution treated paired-end reads as two separate single-end reads , which were processed independently . For each paired-end read , we collected in the set of BACs assigned to the left mate , and in the set of BACs assigned to the right mate . Unless , we declared the paired-end read to be concordant when or . For barley HV5 , 68 . 7% of the deconvoluted paired-end reads were concordant , which indicates that the deconvolution was quite accurate ( see the second sheet in Dataset S3 ) . We assembled each set of reads assigned to a BAC using Velvet [24] for a variety of choices of -mer size . From the assemblies obtained for different choices of , we decided to report in Dataset S4 the assembly that maximized the N50 . If we average the assembly statistics over the 2 , 197 BACs , the number of deconvoluted reads used in the assemblies was 84% in HV3 , 86% in HV4 , 87 . 6% in HV5 , and 83 . 2% in HV6 indicating that Velvet took advantage of most of the data; the average N50 was 8 , 190 bp in HV3 ( 7 . 0% of the average BAC length in that set ) , 5 , 883 bp in HV4 ( 4 . 65% of the average BAC length ) , 7 , 210 bp in HV5 ( 5 . 6% of the average BAC length ) , and 6 , 032 in HV6 ( 4 . 67% of the average BAC length ) ; the average longest contig was 18 , 958 bp in HV3 , 15 , 674 bp in HV4 , 19 , 222 bp in HV5 , and 16 , 018 in HV6; the average sum of all the contigs in each assembly was 104 , 578 bp in HV3 ( 89 . 8% of the average BAC length in that set ) , 102 , 502 bp in HV4 ( 81 . 5% of the average BAC length ) , 113 , 678 bp in HV5 ( 87 . 8% of the average BAC length ) , and 98 , 087 bp in HV6 ( 75 . 9% of the average BAC length ) . Barley BAC assemblies were compared against BAC-unigene lists obtained using the Illumina GoldenGate oligonucleotide pool assay ( OPA ) [33] developed for barley [27] . We used the Illumina OPAs on the same four , and three additional , sets of barley pools described above ( 637 pools in total ) and determined which BAC clones were positive for two sets of 1 , 536 marker loci/unigenes ( see Materials and Methods for details ) . The Illumina OPAs allowed us to map a total of 1 , 849 unique unigenes to BACs ( estimated error rate of 5% , see Materials and Methods for details ) . Table S1 summarizes the results of unigene-BAC and BAC-unigene assignment broken down by chromosome and chromosome arms , whereas Dataset S6 contains all the solved BAC-unigene relationships along with their chromosomal location . Analysis of the assembly of the 2 , 197 barley BACs in each of the HV3–HV6 sets was carried out by assuming the results of the OPA as the “ground truth” , although the Illumina OPA assay has an estimated error rate of 5% . For instance in HV5 we extracted a total of 221 marker loci/unigenes that were mapped to a total of 202 distinct BACs . We obtained the sequence of these 221 unigenes from Harvest ( http://harvest . ucr . edu ) and Blast-ed them against the HV5 BAC contigs . Out of 202 BACs that were expected to contain those genes , only 20 BAC assemblies ( 10% ) missed the expected marker loci/unigenes ( see Dataset S4 ) . For the other 90% of the assemblies which contained the expected unigenes , the average coverage of those unigenes was about 90% of their length . Similar results were obtained from the HV3 , HV4 , and HV6 sets ( see Dataset S4 ) . This analysis suggests that these BAC assemblies contain the majority of the barley genes , which is the main objective of this work .
Barley raw sequencing data for the barley BAC set can be obtained from NCBI Sequence Read Archive accession numbers SRA051771 and SRA051780 ( HV3 ) , SRA051535 ( HV4 ) , SRA047913 ( HV5 ) and SRA050074 ( HV6 ) . When all the BAC assemblies will be complete , we will make them available in Harvest:Barley ( http://harvest . ucr . edu ) and GenBank ( http://www . ncbi . nlm . nih . gov/genbank/ ) . The current set of HV3 , HV4 , HV5 , and HV6 BAC assembly as well as the 31x shotgun genome assembly of barley can be accessed via our Blast server hosted at the address http://www . harvest-blast . org/ , by selecting “Morex Barley BACs” or “Barley Genome” from the database menu . These assemblies can also be downloaded from http://www . harvest-web . org/utilmenu . wc . The source code of HashFilter is available from http://www . cs . ucr . edu/stelo/hashfilter/under the GNU General Public License . HashFilter runs under Linux or MacOS . Additional data are available with the online version of this paper . | The problem of obtaining the full genomic sequence of an organism has been solved either via a global brute-force approach ( called whole-genome shotgun ) or by a divide-and-conquer strategy ( called clone-by-clone ) . Both approaches have advantages and disadvantages in terms of cost , manual labor , and the ability to deal with sequencing errors and highly repetitive regions of the genome . With the advent of second-generation sequencing instruments , the whole-genome shotgun approach has been the preferred choice . The clone-by-clone strategy is , however , still very relevant for large complex genomes . In fact , several research groups and international consortia have produced clone libraries and physical maps for many economically or ecologically important organisms and now are in a position to proceed with sequencing . In this manuscript , we demonstrate the feasibility of this approach on the gene-space of a large , very repetitive plant genome . The novelty of our approach is that , in order to take advantage of the throughput of the current generation of sequencing instruments , we pool hundreds of clones using a special type of “smart” pooling design that allows one to establish with high accuracy the source clone from the sequenced reads in a pool . Extensive simulations and experimental results support our claims . | [
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] | 2013 | Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space |
In plants , root system architecture is determined by the activity of root apical meristems , which control the root growth rate , and by the formation of lateral roots . In legumes , an additional root lateral organ can develop: the symbiotic nitrogen-fixing nodule . We identified in Medicago truncatula ten allelic mutants showing a compact root architecture phenotype ( cra2 ) independent of any major shoot phenotype , and that consisted of shorter roots , an increased number of lateral roots , and a reduced number of nodules . The CRA2 gene encodes a Leucine-Rich Repeat Receptor-Like Kinase ( LRR-RLK ) that primarily negatively regulates lateral root formation and positively regulates symbiotic nodulation . Grafting experiments revealed that CRA2 acts through different pathways to regulate these lateral organs originating from the roots , locally controlling the lateral root development and nodule formation systemically from the shoots . The CRA2 LRR-RLK therefore integrates short- and long-distance regulations to control root system architecture under non-symbiotic and symbiotic conditions .
Plant growth requires the continuous development of the root system and its adaptation to changing environmental soil conditions . Mechanisms controlling root system architecture at the whole-plant level , including the systemic coordination of shoot and root development , are key breeding targets for maintaining crop productivity under adverse stress conditions but remain poorly understood [1] . Root system architecture is a consequence of the sustained activity of root apical meristems , leading to indeterminate root growth as well as the de novo formation of lateral organs . In legume ( Fabaceae ) plants , the root system can form two types of lateral organs depending on the environmental conditions: lateral roots and symbiotic nitrogen-fixing nodules [2]–[4] . Lateral root initiation , emergence and growth depend on water and nutrient availability and are regulated by a combination of local and systemic pathways [5] . Symbiotic nodules are formed under nitrogen-deprived conditions when the specific Rhizobium spp . soil bacteria are present in the rhizosphere [6]–[7] . In both types of lateral organogeneses , cell divisions are activated in specific tissues ( pericycle , endodermis and cortex ) above the growing root tip [2]–[4] , [8] . Root tissues contributing to primordium formation are , however , different depending on the plants and organs: in the Medicago truncatula model legume , lateral root primordia mainly develop from pericycle cell divisions , whereas the nodule primordia that are induced by Sinorhizobium meliloti mainly derive from the inner cortex . Both types of primordia will then subsequently emerge from the parental root and establish a meristematic stem cell niche ensuring their indeterminate growth . To control meristematic activity , cell differentiation , and lateral organ initiation , non-cell autonomous cues are essential to carry positional information , which can be informed either by mobile phytohormones , small RNAs or peptides [9]–[11] . Among peptides , several CLAVATA 3/EMBRYO-SURROUNDING REGION ( like ) peptides ( so called CLE peptides; [10] ) that are perceived by Leucine-Rich Repeats – Receptor-Like Kinases ( LRR-RLKs ) are involved in local and long-distance ( systemic ) pathways controlling the development of different plant organs . First , several CLE peptide/LRR-RLK receptor modules carry positional information across a few cell layers to control the cell fate in different Arabidopsis thaliana meristems . The founding example is the CLAVATA3 ( CLV3 ) peptide , which is perceived by the CLV1 receptor to control the shoot apical meristem stem cell niche [12] , [13] as well as columella cell differentiation in the Root Apical Meristem ( RAM; [14]–[16] ) . A second example is the Tracheary element Differentiation Inhibitory Factor ( TDIF ) peptide , which is perceived by the Phloem Intercalated with Xylem ( PXY ) receptor , controlling stem cell proliferation/differentiation transition in the cambium meristem and therefore vasculature differentiation and organ thickening [17]–[19] . In legume plants , an additional CLE/LRR-RLK module controlling root lateral organs number was identified through grafting experiments as performing a long-distance systemic function from the shoots [7] . Mutants affecting orthologous LRR-RLKs that are closely related to CLV1 in Arabidopsis ( SUNN , Super Numeric Nodules in M . truncatula; NARK , Nodule Autoregulation receptor Kinase in soybean; and HAR1 , Hypernodulation and Aberrant Root 1 in Lotus japonicus; [20]–[23] ) form an increased number of symbiotic nitrogen-fixing nodules depending on the receptor function in the shoots . In addition , the Lotus KLAVIER ( KLV ) LRR-RLK , which is closely related to the Arabidopsis RECEPTOR-LIKE PROTEIN KINASE 2 ( RPK2 ) /TOADSTOOL 2 ( TOAD2 ) that is functionally linked to anther and embryo development , is involved in the same systemic AON pathway as is HAR1 [24] . In M . truncatula and L . japonicus , CLE peptides that are specifically produced in nodulated roots can negatively regulate the nodule number depending on these LRR-RLK receptors [25]–[27] . This CLE peptide/LRR-RLK module therefore participates in the systemic “Autoregulation of Nodulation” ( AON; [22] ) pathway , which may involve a direct root-to-shoot CLE peptide transport and receptor binding in the shoots , as recently proposed [28] . Interestingly , an increased number of emerged lateral roots was reported in the Lotus har1 mutant under both symbiotic and non-symbiotic conditions [29] . Similarly , KLV also has a non-symbiotic function in the local regulation of SAM maintenance [24] . Collectively , these results indicate that CLE peptide/LRR-RLK signaling modules regulate the development of various organs using either local and/or systemic pathways .
Forward genetic screens that were performed on a M . truncatula Tnt1 insertional mutant collection [30]–[31] identified seven mutant lines with a wild-type shoot development and a “compact root system architecture” ( so called “cra”; Fig . 1A–C and S1A Fig . ) . Segregation analyses of this root phenotype revealed a 3∶1 WT:mutant ratio ( chi2 test , p<0 . 05 , n = 30 ) , suggestive of a single locus recessive mutation . Allelism tests ( S1B Fig . ) indicated that all of the mutants were affected in the same locus but differed from the previously identified cra1 mutant showing partially similar phenotypes [32] and were therefore named cra2 . Detailed quantitative in vitro analyses revealed that the cra2 phenotype consists , compared to the WT , of shorter roots with an increased number of emerged lateral roots ( Fig . 1D–E ) . Accordingly , lateral roots were observed three days post germination ( dpg; Fig . 1E ) in contrast to WT plants . This root phenotype was observed independently of the growth and nutrient conditions that were used ( greenhouse versus in vitro , Fig . 1A , E; with or without nitrogen or carbon sources , Fig . 1E ) . In addition to the faster emergence of lateral roots , we observed a reduction in the primary root growth , which prompted us to analyze the structure of the RAM ( Fig . 2A–C ) . In contrast to the A . thaliana model , M . truncatula roots have an open meristem , and the transition between cell proliferation and differentiation is more progressive . In addition , cells from diverse files elongate at a slightly different distance from the root apex , leading to a cone-shaped transition zone ( Fig . 2A , detail of the transition zone in S2 Fig . ) . When cra2 RAMs were observed three dpg , both the cell proliferation and elongation zones were reduced , and a lower number of cells was observed in the two zones ( Fig . 2 A–C ) . Root patterning , however , evaluated based on both longitudinal and transversal sections , seemed unaffected ( Fig . 2A–B and D ) . In addition , amyloplast accumulation in differentiated root cap cells and the expression of the RAM stem cell niche marker WOX5 ( WUSCHEL-related homeobox 5; [33]–[35] ) were both detected in cra2 ( S3 Fig . ) , suggesting the maintenance of RAM cell identity . Among several hypotheses that could explain the cra2 root system architecture phenotype , we tested whether a RAM activity defect could indirectly lead to increased branching as compensation or if the reduced meristematic activity could be a consequence of the enhanced formation of lateral roots . An analysis of root apices at one dpg , i . e . , before any lateral root primordium could be detected in the cra2 mutant , revealed no significant difference in the size of the cell proliferation and elongation zones between WT and mutant roots ( Fig . 2E , F ) . Compared to previous observations of roots at three dpg ( Fig . 2A , B ) , this result indicates that a RAM defect does not precede the occurrence of the lateral root phenotype . As an independent approach , we experimentally removed the RAM at one or three dpg and followed the kinetics of lateral root formation ( Fig . 2G , H ) . The cra2 mutant showed an increased ability to form lateral roots whether RAM excision occurred before or after lateral root initiation . Collectively , these results suggest that the cra2 RAM phenotype can be disconnected from its enhanced ability to form lateral roots . In addition to root branching , legume roots can adapt to environmental conditions by developing another root lateral organ , the nitrogen-fixing symbiotic nodule . An analysis of cra2 roots under symbiotic conditions revealed that a similar “compact root architecture” phenotype was observed ( Fig . 3A ) and that mutant shoot growth was similar with or without Rhizobium inoculation ( Fig . 3B , C ) . cra2 plants , however , developed a strongly reduced number of symbiotic nodules ( Fig . 3 D , E ) . This low nodulation phenotype could be either linked to a direct CRA2 function in regulating nodulation or may reflect that the strongly altered “compact root architecture” phenotype ( e . g . , Fig . 3A ) indirectly hampers nodule formation . Using a symbiotic infection kinetic analysis ( 1 to 14 dpg ) , we showed that Rhizobium inoculation as early as 1 or 3 dpg led to reduced nodulation ( Fig . 3E ) , indicating that the cra2 nodulation phenotype was independent of the strength of the lateral root phenotype . The few symbiotic nodules that formed on cra2 roots were elongated ( S4A Fig . ) , revealing that a functional nodule meristem was formed . In addition , an analysis of bacterial nitrogenase activity using an Acetylene Reduction Assay ( ARA ) showed that despite cra2 plants have a strongly reduced ability to fix atmospheric nitrogen due to their lower number of symbiotic organs ( S4B Fig . ) , cra2 nodules have a similar specific ARA activity compared to that of WT nodules ( S4C Fig . ) . Overall , the detailed analysis of the mutant phenotypes indicates that , independently of a potential indirect effect of the increased lateral root formation on nodule formation , CRA2 antagonistically regulates lateral root and nodule formation . As root system architecture is controlled by both systemic and local pathways , we next determined using graftings experiments whether the cra2 phenotypes depend on roots and/or shoots . Under non-symbiotic conditions , the “compact root architecture” phenotype was recovered specifically when the graft combination included cra2 mutant roots ( Fig . 4A–B ) but was independent of the shoot genotype , indicating that CRA2 expression in roots is sufficient to negatively regulate lateral root formation . Under symbiotic conditions , we surprisingly observed a disconnect between the lateral root and nodulation phenotypes ( Fig . 4C–D ) . Indeed , similar to non-symbiotic conditions , the increased density of the lateral roots was associated with cra2 mutant roots , but the low nodulation phenotype was observed only in grafting combinations that included cra2 mutant shoots and was independent of the root genotype . This result indicates that the systemic activity of CRA2 in the shoots positively regulates symbiotic nodule formation in the roots . Strikingly , the cra2/WT grafts had a WT root system architecture but developed more than 10 times fewer nodules ( Fig . 4C–D ) . In addition , when the nodule numbers were related to the root dry weight to counterbalance the strong differences existing between WT/WT and WT/cra2 root systems , the nodulation efficiency became strictly equivalent between these two grafting combinations ( Fig . 4E ) . These results therefore unambiguously demonstrate that the low-nodulation phenotype is not an indirect consequence of an inhibitory signal that is produced in the numerous lateral roots in cra2 . In addition , this result indicates that the root-dependent regulation of the lateral root number is independent of the shoot-regulation of the nodule number . To identify the gene that is affected in cra2 mutants , Tnt1 Flanking Sequence Tags ( FSTs ) were generated in the different alleles ( cra2-1 to cra2-7 ) to detect Tnt1 insertions affecting a common genomic sequence in several of these mutant lines ( cra2-1 , cra2-4 to cra2-6; Fig . 5A and S5A–B Fig . ) . Interestingly , the three other alleles also showed genetic lesions in the same locus , consisting , respectively , in the presence of other M . truncatula endogenous insertional elements for cra2-3 and cra2-7 and in a point mutation for cra2-2 leading to a frameshift ( S5C-E Fig . ) . Based on the FSTs that were available from the Noble foundation ( http://bioinfo4 . noble . org/mutant/ ) , we could identify three additional lines with a Tnt1 insertion in the same open reading frame ( cra2-8 to cra2-10 ) that showed a “compact root architecture” phenotype ( Fig . 5A and S1A Fig . ) , further confirming that mutation at this locus is causal for the phenotype . The genomic region that was affected in the 10 available cra2 alleles encodes an LRR-RLK belonging to subfamily XI ( Fig . 5B ) , which , in agreement with the results of the grafting experiment , is expressed in the shoots , roots and nodules ( S6 Fig . ) . Interestingly , this LRR-RLK subfamily contains several other receptors that were identified as regulating plant development via local or systemic regulation [10] , [36] . An analysis of CRA2 homology with other LRR-RLKs that have been functionally characterized in legumes showed that CRA2 was not closely related to SUNN/HAR1 or KLV and was most closely related to A . thaliana XIP1 ( Xylem Intermixed in Phloem 1; [37]; Fig . 5B ) . An Arabidopsis xip1 mutant was recently described as showing defects in vasculature patterning specifically in the shoots but not in the roots , and no root developmental phenotype was reported [37] . To determine whether XIP1 and CRA2 may nevertheless have related functions , we analyzed the vascular patterning in cra2 mutant roots and shoots . No significant defect could be detected in the shoot or root cambium structure; in the vascular bundle differentiation , such as the misspecification of xylem and phloem bundles; or in the root or stele diameter ( S7 Fig . ) . These results suggest that XIP1 and CRA2 are not functional homologs . In addition , these results indicate that the cra2 root and nodulation phenotypes ( Fig . 1 , 2 ) are not linked to any detectable defect in vascular bundle patterning . We then analyzed the CRA2 spatial expression pattern under non-symbiotic and symbiotic conditions using either a transcriptional fusion between an ∼2 kb CRA2 promoter region and the GUS reporter ( Fig . 6A–D and I–N ) or in situ hybridization ( Fig . 6 E–H ) . Both approaches revealed an expression that was associated with the root stele and vascular bundles ( Fig . 6A–B and E–F ) . Similarly , CRA2 expression was associated with vascular bundles in the shoots ( Fig . 6G–H ) . This result agrees with the expression pattern of other LRR-RLKs regulating lateral root and nodule numbers in different legumes ( HAR1/NARK/SUNN or KLV; 24 , [38]–[40] ) . CRA2 was additionally expressed in the Cell Proliferation Zone ( CPZ ) of the open RAM ( Fig . 6A and C–D ) . In addition , CRA2 was expressed in the lateral root initiation site as soon as the pericycle divisions could be identified ( Fig . 6I–J ) and later in whole lateral root primordia during the initiation and emergence stages ( Fig . 6K–L ) . Under symbiotic nodulation conditions , CRA2 expression was also detected in nodule primordia ( Fig . 6M ) as well as in mature nodules in relation to peripheral vascular bundles and the apical meristem ( Fig . 6N ) .
CRA2 functions in the regulation of legume root system architecture suggest that this LRR-RLK acts positively in the Autoregulation of Nodulation ( AON ) pathway . In the current model , the systemic SUNN-dependent AON pathway represses the number of nodules that are formed on the roots depending on the shoot metabolic status , e . g . , the amount of carbon skeletons that are provided through photosynthesis and that are required for the subsequent assimilation of fixed nitrogen in the nodules . The SUNN pathway therefore limits the formation of extra nodules depending on environmental cues . When initiated , however , a negative feedback mechanism would be necessary to reset this negative regulation and further permit new symbiotic infection events . The CRA2 LRR-RLK may then participate in the systemic dynamic fine tuning of nodule formation . As both CRA2 and SUNN LRR-RLKs are expressed in the shoot vasculature , it remains to be determined whether and how these proteins act together or independently . As LRR-RLKs have been proposed to form large complexes comprising different members of the family [36] or even other RLKs ( e . g . , CLV1 with ACR4 in the RAM; [16] ) , CRA2 and SUNN may interact within a single complex . The Lotus HAR1-dependent AON pathway was additionally shown to control negatively lateral root formation [20]–[29] . In the sunn Medicago mutant , however , the root length is reduced , but no specific function in the regulation of lateral root number has been identified [23] . CRA2 therefore negatively affects lateral roots similarly to HAR1 in Lotus but dissimilarly to SUNN in M . truncatula . Interestingly , a shorter root length and a decreased number of lateral roots occurred in the Lotus klv mutant , whereas additive effects of klv and har1 were identified for nodulation , indicating their related function in a single AON pathway [40] . In addition , both har1 and klv antagonistic lateral root phenotypes were observed under non-symbiotic conditions [29] , [40] , similar to the phenotype of CRA2 in M . truncatula . This result indicates that these peptide/LRR-RLK regulatory modules acting under non-symbiotic conditions may also control lateral root formation in non-nodulating ( non-legume ) plants . In Arabidopsis , the ACR4 CRINKLY-RLK regulates lateral root initiation [41] , but no LRR-RLK has been yet associated with this developmental process . More generally , despite the close relationships between HAR1 , SUNN and CLV1 , their mutant phenotypes are not conserved between plant species: the sunn and har1 mutants have divergent lateral root phenotypes , whereas no shoot fasciation phenotype was detected in the har1 or sunn mutant in contrast to clv1 in Arabidopsis . Similarly , despite CRA2 and XIP1 having closely related sequences , no vasculature patterning phenotype was identified in cra2 compared to xip1 , and no altered root system architecture phenotype was reported in xip1 . This result suggests that various sets of LRR-RLKs differentially regulate the ability of root systems to form lateral roots depending on species , even inside the legume family . Alternatively and non-exclusively , different patterns of LRR-RLK gene duplication may have occurred in the different plant genomes , generating functionally divergent or redundant pathways . These scenarios may explain the apparent phenotypic diversification that is observed . Overall , this study demonstrates that a single LRR-RLK acts locally in roots and systemically in shoots to control root lateral organ development , thereby coordinating at the whole-plant level the plastic development of the root system depending on the changing environmental conditions . To elucidate the opposite effects of the CRA2 pathway on lateral root and nodule formation , the identification of downstream targets will be essential . Among other candidate pathways , cytokinins were previously reported to antagonistically control lateral root and symbiotic nodule formation [42] . Data on crosstalk between signaling peptides and hormones is just emerging , mainly with auxin and cytokinins [10] . Therefore , a remaining challenge will be to integrate the different peptide/receptor modules that are known to regulate lateral root and nodule formation , including the CRA2 pathway , into the framework of hormonal regulation .
The Medicago truncatula ( Gaertn . ) plants used in this study were of the R108-4 genotype . The Tnt1 insertional mutants were generated and screened at the Noble Foundation ( USA; lines named NFxxx; [30] ) or produced at the “Institut des Sciences du Végétal” ( ISV , CNRS , Gif sur Yvette , France ) and screened at the “Agroécologie” institute ( INRA , Dijon , France; lines with other names than NFxxx ) . The seeds were scarified on sandpaper , sterilized for 20 min in bleach ( 12% [v/v] sodium hypochlorite ) , and thoroughly rinsed in sterile water . The seeds were then stratified at 4°C for one day and then germinated at 25°C in the dark on inverted water agar plates . The seedlings were grown in vitro in a growth chamber at 25°C with a 16 h light period and a 150 µE intensity on N-deprived media ( “i” , [42]; or Fahraeus , [43] ) , on an N-rich medium ( Fahraeus with NH4NO3 10 mM ) , or on a N- and C-rich medium ( “Lateral-Root-Inducing Medium” , LRIM; [42] ) depending on the experiment . Alternatively , the plants were grown in a greenhouse ( 25°C , 16 h 150 µE light period , 60%–70% humidity ) in soil or in pots containing a perlite∶sand mixture ( 3∶1 ) and watered every two days with “i” or “SN/2” media [42] , depending on the experiment . The nodulation experiments used the Sinorhizobium meliloti 1021 strain ( OD600nm = 0 . 05 ) as described in [42] . Plant genomic DNA was extracted from the leaves as described by [44] , and Flanking Sequence Tags ( FSTs ) that were linked to Tnt1 insertions were identified using the transposon display PCR method as described in [45] using the EcoR1 or Ase1 restriction enzymes . The transposon display , genotyping PCRs , and the sequencing of the different mutant alleles were performed using the primers that are listed in S1 Table . The RT-PCRs and real-time RT-PCRs were performed as described in [42] with the primers that are indicated in S1 Table . To generate the MtCRA2 reporter transcriptional fusion , an 1800-bp sequence upstream of the CRA2 start codon was identified , corresponding to the upstream region of the Medtr3g110840 . 1 open reading frame ( M . truncatula genome Mt4 . 0v1 , http://www . jcvi . org/medicago/index . php ) . This region was amplified by PCR using a high-fidelity polymerase ( Phusion , Thermo Scientific ) and primers pCRA2-F and pCRA2-R ( S1 Table ) . The promoter was cloned using Gateway technology initially into the pTOPO-Entry vector ( Invitrogen ) and then into the pkGWFS7 vector ( https://gateway . psb . ugent . be/search ) carrying a Green Fluorescent Protein ( GFP ) - β Glucuronidase ( GUS ) fusion downstream of the cloning recombination site . The graftings were performed as described in the “cuttings and grafts” chapter of the Medicago handbook ( http://www . noble . org/medicagohandbook/ ) . The grafts , which were initially generated in vitro , were transferred after three weeks to pots containing a perlite∶sand mixture ( 3∶1 ) that was imbibed with the “i” solution in a growth chamber ( same conditions as described above ) . After three weeks , the root length and root dry weight ( 60°C for 48 h ) were measured . For root apex excision , the roots were sectioned at one centimeter from the root tips one or three days post germination and placed in the “i” medium in a growth chamber ( same conditions as described above ) . The number of lateral roots was quantified one to seven days post root tip excision using the ImageJ software ( http://imagej . nih . gov/ij/ ) . To generate composite plants , constructs were introduced into the Agrobacterium rhizogenes ARqua1 strain and used for M . truncatula root transformation as described in [46] . Transgenic roots were selected on kanamycin ( 25 mg/L ) for two weeks , and composite plants were then transferred onto growth papers ( Mega International , http://www . mega-international . com/ ) on “i” medium for four to six days and , depending on the experiment , were inoculated by S . meliloti as described in [42] . The roots or stems were cut into 5-mm segments and immediately embedded in 3% agarose ( Euromedex ) . A vibratome ( VT1200S , Leica ) was used to generate 100 µm-thick cross-sections . Toluidine blue and aniline blue ( Sigma ) stainings were performed by incubating sections for 3 to 5 min in 1% toluidine blue or in 0 . 005% aniline blue and subsequently washing the sections twice with distilled water . The sections were viewed under bright-field illumination or under UV excitation ( 365 nm ) , respectively , for toluidine blue or for aniline blue stainings using a Leica DMI 6000B inverted microscope . The phloroglucinol–HCl reagent was prepared by mixing 2 volumes of 2% ( w/v ) phloroglucinol ( Sigma ) in 95% ethanol with one volume of concentrated HCl and observation under bright-field illumination ( DMI 6000B , Leica ) . Callose fluorescence was visualized using a 405 nm excitation , and emission was collected between 480 and 515 nm ( DMI 6000B , Leica ) . The amyloplasts were detected using Lugol ( Sigma ) staining and visualization under bright field illumination . To examine the detailed cellular organization of M . truncatula root apices , the roots were stained using a modified Pseudo Schiff-Propidium Iodide ( PS-PI ) staining protocol as described by [47] . Longitudinal optical sections were obtained using a Leica TCS SP2 confocal laser-scanning microscope using a 488 nm excitation , and emission was collected between 520 and 720 nm . The root meristem size was estimated as the number of cells in the outer cortex from the location of the tip to the first elongating cell using the ImageJ software . The length of the elongated cortical cells in the mature root region was also measured . The GUS activity was revealed as described by ( 48] and observed under bright-field illumination ( DMI 6000B , Leica ) . In addition , using the AOBS reflection mode of a Leica TCS SP8 spectral confocal laser-scanning microscope , the GUS precipitate reflectance was analyzed as described in [47] with a 488 nm excitation and a collection of the reflection signal between 485 and 491 nm on PS-PI counterstained samples ( detected as described above ) . In situ hybridization was performed as described in [48] with the probe as indicated in S1 Table . To measure nitrogenase activity of symbiotic nodules , an Acetylene Reduction Assay ( ARA ) was performed on individual plants with a protocol that was derived from [49] . One month after inoculation with Rhizobium , the plants were placed into 10-ml glass vials that were sealed with rubber septa . Acetylene was injected into each vial , and after 1 h of incubation at room temperature , the produced ethylene was measured using Gas Chromatography ( 7820A , Agilent technologies ) . Phylogenetic analyses were performed using the SeaView package ( v . 4 . 4 . 0; [50] ) . The full-length protein sequences that were retrieved from the NCBI database were aligned using Muscle and optimized with the Gblocks software . Phylogenetic relationships were defined using a maximum likelihood approach . The tree was built with the PhyML software using the LG substitution model and four substitution rate categories . Support for each node was gained by approximate likelihood ratio tests ( aLRT SH-like; [51] ) . The XP_001698687 . 1 RLK from Chlamydomonas patens , which was identified by a BLAST search of NCBI ( E-value = 1e-40 ) , was used as an outgroup to anchor the tree . The statistical analyses were performed with non-parametric tests ( Mann-Whitney when n = 2 independent samples and Kruskal and Wallis when n>2 independent samples ) . | Despite the essential functions of roots in plant access to water and nutrients , root system architecture has not been directly considered for crop breeding improvement , but it is now considered key for a “second green revolution . ” In this study , we aimed to decipher integrated molecular mechanisms coordinating lateral organ development in legume roots: lateral roots and nitrogen-fixing symbiotic nodules . The compact root architecture 2 ( cra2 ) mutant form an increased number of lateral roots and a reduced number of symbiotic nitrogen-fixing nodules . This mutant is affected in a CLAVATA1-like Leucine-Rich Repeat Receptor-Like Kinase ( LRR-RLK ) that has not previously been linked to root development . Grafting experiments showed that CRA2 negatively controls lateral root formation and positively controls nodule development through local and systemic pathways , respectively . Overall , our results can be integrated in the framework of regulatory pathways controlling the symbiotic nodule number , the so-called “Autoregulation of Nodulation” ( AON ) , involving another LRR-RLK that also acts systemically from the shoots , SUNN ( Super Numeric Nodules ) . A coordinated function of the CRA2 and SUNN LRR-RLKs may thereby permit the dynamic fine tuning of the nodule number depending on the environmental conditions . | [
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] | 2014 | Local and Systemic Regulation of Plant Root System Architecture and Symbiotic Nodulation by a Receptor-Like Kinase |
T cells use their T-cell receptors ( TCRs ) to scan other cells for antigenic peptides presented by MHC molecules ( pMHC ) . If a TCR encounters a pMHC , it can trigger a signalling pathway that could lead to the activation of the T cell and the initiation of an immune response . It is currently not clear how the binding of pMHC to the TCR initiates signalling within the T cell . One hypothesis is that conformational changes in the TCR lead to further downstream signalling . Here we investigate four different TCRs in their free state as well as in their pMHC bound state using large scale molecular simulations totalling 26 000 ns . We find that the dynamical features within TCRs differ significantly between unbound TCR and TCR/pMHC simulations . However , apart from expected results such as reduced solvent accessibility and flexibility of the interface residues , these features are not conserved among different TCR types . The presence of a pMHC alone is not sufficient to cause cross-TCR-conserved dynamical features within a TCR . Our results argue against models of TCR triggering involving conserved allosteric conformational changes .
The interaction between T-cell receptors ( TCRs ) on the surface of T-cells and peptides bound by Major Histocompatibility Complexes ( MHCs ) on the surface of antigen presenting cells is one of the most important processes of the adaptive immune system [1] . In the case of MHC class I molecules intracellular proteins are degraded by proteasomes into peptides , the peptides are loaded onto MHCs , and subsequently the peptide/MHC ( pMHC ) structures are presented on the cell surface . The TCRs of T-cells bind to pMHCs with their six hypervariable Complementarity Determining Regions ( CDRs ) and thereby scan the pMHC ( Fig 1A and 1B ) . Based on this interaction further downstream signalling cascades can be activated and an immune response can be elicited against a particular antigenic peptide . The TCR/pMHC interaction is of relatively low affinity ( KD ~0 . 1–500 μM ) and degenerate: One TCR can recognise multiple pMHC and one pMHC can be recognised by multiple TCRs but not every TCR can recognise every pMHC . A long standing question has been how TCR binding to pMHC results in changes in the cytoplasmic domains of the TCR/CD3 signalling subunits ( e . g . phosphorylation ) , a process termed TCR triggering . Several mechanisms of TCR triggering have been proposed [2] , which can be grouped roughly into segregation/redistribution , aggregation , and conformational change models . Conformational change models are of two types [2]: one group , based on the observation that pMHC binding will impose a mechanical pulling force on the TCR [3] , proposes that this mechanical force somehow alters the conformation of TCR relative to the membrane and/or CD3 subunits [4–6] . The second group postulates that pMHC binding is accompanied by an allosteric conformation change transduced through the TCRαβ heterodimer [7] . While there is growing support for mechanical models [8–11] , evidence for ‘allosteric’ models is more equivocal ( reviewed in [2] ) . Rossjohn and colleagues have reported a change in the AB loop of TCR Cα domain accompanying binding to agonist pMHC [7 , 12] , but structural studies have failed to identify a conformation change conserved in all TCRs upon pMHC binding ( reviewed in [2] ) . The experimental studies described above relied on X-ray crystallography or NMR spectroscopy , which cannot measure dynamic changes in the TCR structure at atomistic resolution , leaving open the possibly that pMHC binding results in conserved changes in TCR dynamics . Fortunately , computational methods such as Molecular Dynamics ( MD ) or Monte Carlo simulations can be used to explore this possibility ( reviewed in [13] ) . Recent approaches include TCR/pMHC interface H-bond network analysis [14 , 15] , binding free energy [16] and detachment [17 , 18] simulations , effects on the MHC [19] , peptide [20] , and on the TCR [21] or CDR loop characterisation [22] . In a previous large scale study we analysed the same TCR/MHC in combination with 172 different peptides of known experimental immunogenicity [23] . The aim was to use simulations to seek distinct dynamical TCR behaviours for encounters with immunogenic versus non-immunogenic peptides . While no such differences were identified to be involved in discrimination , this does not rule out a role for conformational dynamics in triggering itself . In the current study we explore this by simulating four different TCRs in their agonist pMHC bound and unbound state and then look for conserved conformational features that distinguish bound from unbound TCRs . Using a total of 26 000 ns of simulation time we show that no such conserved and non-obvious features distinguish bound and unbound TCR .
We extracted the structures of the LC13-TCR/FLRGRAYGL/HLA-B*08:01 ( accession code 1MI5 ) , A6-TCR/LLFGYPVYV/HLA-A*02:01 ( accession code 1AO7 ) , JM22-TCR/GILGFVFTL/HLA-A*02:01 ( accession code 1OGA ) , and 1G4-TCR/SLLMWITQC/HLA-A*02:01 ( accession code 2BNR ) from the Protein Data Bank ( PDB ) [24] . Constant TCR regions were included in all simulations as we have previously shown that the constant regions are important for reliable conclusions from molecular TCR and antibody simulations [25] . The LC13 was chosen as a model system as this system has been the target of extensive MD simulations ( e . g . [23 , 26] ) due to the availability of a large number of experimentally tested mutations . Additionally we chose the A6 , JM22 , and 1G4 systems as they have been investigated computationally before ( e . g . [14 , 15 , 18 , 27] ) and all three TCRs bind to HLA-A*02:01 while the LC13 TCR binds HLA-B*08:01 . This allows us to investigate if there are conserved TCR reaction features across MHC types as well as within HLA-A*02:01 . Eight different structures ( LC13-TCR , A6-TCR , JM22-TCR , and 1G4-TCR with and without pMHC ) were simulated . Each structure was immerged into a dodecahedronic simulation box filled with explicit SPC water allowing for a minimum distance of 1 . 2 nm between protein and box boundary . Na+ and Cl- ions were added to achieve a neutral charge and a salt concentration of 0 . 15 mol/litre . Protonation states were determined automatically by Gromacs [28] . Energy minimisation using the steepest descent method was applied . The systems were warmed up to 310 K using position restraints . Hydrogen atoms were replaced by virtual sites to allow for an integration step of 5 fs for the production runs [29] . Final production runs were carried out using Gromacs 4 [28] and the GROMOS 53a6 force field [30] . Parts of the LC13 simulations were taken from our previous work in [21] and [23] while parts of the A6 , JM22 , and 1G4 simulations were taken from [14] and [15] . Multiple replicas ( identical parameter but different seeds ) per simulation are important for reproducible conclusions as the results of several studies [15–17 , 25 , 31] and a systematic evaluation have shown [32] . Therefore we use 100 LC13 TCR simulation replicas at 100ns each . A boot strapping analysis of these 100 replicas ( S1 Fig ) shows that 10 replicas reduce the variance between replicas by about 70% while more replicas reduce the variance only slowly further ( 25 replicas: 80% and 50 replicas 87% ) . Therefore we simulated the A6 , JM22 , and 1G4 TCR with and without pMHC for 100 ns using 10 replicas totalling 26 μs ( Table 1 ) . Trajectories were manually inspected using VMD [33] and the vmdICE-plugin [34] . Solvent accessible surface area ( SASA ) , root mean square fluctuation ( RMSF ) , radius of gyration ( RG ) , hydrogen bonds ( H-bonds ) , and distances were calculated by the GROMACS [28] modules gmx sasa , gmx rmsf , gmx gyrate , gmx hbond , and gmx distance respectively and imported into pymol/Matlab using gro2mat [35] . The H-bond networks were visualized using pyHVis3D [14] . We used three different types of measurements to quantify the magnitude of difference between descriptors of TCRpMHC and TCR simulations ( modified from our previous study [19] ) : Firstly , the simple difference in the mean values that is referred to as: d=X¯TCRpMHC−X¯TCR Where X-TCRpMHC and X-TCR are the mean values over all frames and replicas of descriptor X ( e . g . SASA or H-bonds of a region ) . The value d helps to quantify the actual magnitude of difference e . g . TCRpMHC simulations have on average 0 . 5 H-bonds less between their TCR chains than TCR simulations . Secondly , we normalise d by the range of the combined distributions excluding the highest and lowest 2 . 5% of the values: d/r=X¯TCRpMHC−X¯TCRrange ( X¯TCRpMHC;2 . 5−97 . 5%∪X¯TCR;2 . 5−97 . 5% ) The value d/r helps to quantify the magnitude of difference related to the width of the combined distributions . Thirdly , we calculate the total variation difference ( tvd ) to quantify the difference in the probability distributions: TVD ( f1 , f2 ) =12∫|f1 ( XTCRpMHC ) −f2 ( XTCR ) |dx Where f1XTCRpMHC is the normalized distribution of all TCRpMHC simulation frames and replicas and f2 ( XTCR ) the normalized distribution of all TCR simulation frames and replicas . The tvd is normed between 0 and 1 where a tvd value of 0 represents perfect overlap of the distributions and a tvd value of 1 represents no overlap . In contrast to d and d/r the tvd does not have a sign i . e . is always positive . We investigated the six CDR loops according to the IMGT [36] definition as extracted from the STCRDAB database [37] as well as the AB loop of the TCR α-chain which was previously hypothesised as influenced by antigen recognition of the LC13 TCR [7] , the loops positioned between the variable and constant TCR domains as the linker of the β-chain ( “CβFG” ) was hypothesised to be important for TCR mechanosensing [10 , 11] , αA and αΒ helices of Cβ as they were reported to be important for CD3 interaction [38] , the F and C-strand of Cα as they were reported to be involved in a possible allosteric TCR signalling mechanism [39] , and Cα DE and Cβ CC’ loops [40] . These regions and their sequence positon in our four different TCRs are summarised in S1 Table . In order to evaluate how likely an observed descriptor difference ( in d , d/r and tvd ) would be seen by chance we performed permutation tests . We merged the n replicas of TCRpMHC simulations with the n replicas of the TCR simulations into one group of size 2n . From this 2n group we picked randomly and with repetition n members for group 1 and n members for group 2 . Then we calculated d , d/r and tvd between group 1 and group 2 . We repeated this procedure 1000 times and obtained a distribution of d , d/r and tvd values . Finally we determine the quartile ( q ) of the observed d , d/r and tvd between TCRpMHC and TCR within these distributions of random boot strapping samples as a indicator of significance . E . g . q = 0 . 98 for d means that 98% of all randomly created group pairs have a smaller d value between them than the d value between TCRpMHC and TCR simulations . An example for a difference in CDR1 loop distance between TCRpMHC and TCR simulations that is likely to be true for the LC13 TCR ( d = 0 . 9 Å and q = 1 . 0 ( i . e . none of the 1000 permutations had a larger difference ) ) and unlikely to be true the JM22 TCR ( d = 0 . 3 Å and q = 0 . 7 ( i . e . about 300 of the 1000 permutations had a larger difference ) is given in S2 Fig .
The distances ( DIST ) between the CDR loops of a TCR can be a descriptor for an opening or closing of the TCR binding interface . Especially the CDR3α and CDR3β loops that are positioned centrally over the scanned peptide ( Fig 1B ) and could be further apart as the pMHC presses between them or could be closer together as a result of binding interface rigidification . Our results show that the first is true for the LC13 and JM22 TCR while the A6 TCR shows a wider distribution and higher distance for TCR simulations ( Fig 2 ) . The 1G4 TCR does not show significant changes . The CDR1 and CDR2 distances also do not show patterns conserved across TCRs ( Table 2 ) . The radius of gyration ( RG ) is a proxy for the compactness of a structure . A high RG indicates a more extended structure while a low RG indicates a more compact structure . We measure the RG of all atoms of the six CDR regions as well as the ABloop , the variable/constant domain linkers and 6 further regions within the TCR constant domains to investigate if pMHC presence has “cramping” effect on any of these regions that could be involved in signalling . Several regions and TCRs show significant differences between their TCRpMHC and TCR states ( Table 2 ) ; however , these are not conserved . The LC13 and JM22 TCR tend to have lower CDR RGs in their TCRpMHC simulations than in their TCR simulations while the opposite tends to be the case for A6 and 1G4 . As an example we show the RG of CDR3α in Fig 3: All differences found are highly significant but they have opposing signs . In contrast the RG of the ABloop and variable/constant linkers are almost unaffected ( Table 2 ) . The solvent accessible surface area ( SASA ) quantifies the extent that a region is exposed to solvent ( here measured using the gmx sasa method of Gromacs ) . When two proteins bind the solvent accessible area of the binding interface will be reduced . This is the case for all six CDRs of all four TCRs upon pMHC binding ( Table 2 ) . The more interesting question is if there is also a change in the SASA if the SASA is measured as if no pMHC would be present for TCRpMHC simulations . i . e . is the protruding of the CDRs altered by pMHC presence ? Here we obtain a picture that is partly similar to the RG-analysis . The A6 and 1G4 TCR which tend to have higher RGs in TCRpMHC simulations tend also to have increased SASAs . However , for the LC13 and JM22 TCR the reduced RG in TCRpMHC simulations seems not to lead to a decreased SASA . Fig 4 shows this effect for CDR3α . The root mean square fluctuation ( RMSF ) is an indication of how stable areas of a structure are . A potential signal transduction could be the increased or decreased flexibility of an area . We have investigated the RMSF of all TCR residues in Fig 5 . Areas of special interest are marked with dashed lines . For these areas permutation tests are given in Table 2 . As expected the RMSFs of the CDRs are lower in TCRpMHC simulations than in TCR simulations due to the restricted degrees of freedom in the binding interface . The changes in CDR RMSF between TCR and TCRpMHC simulations for the LC13 , JM22 and 1G4 TCRs are high and mostly significant while the changes for the A6 TCR are lower and mostly not significant ( compare Fig 5 and Table 2 ) . In contrast by far the largest observed difference in the RMSF is found in the ABloop of the A6 TCR . In TCRpMHC simulations this loop is about 50% more flexible than in TCR simulations . This is not the case for the other three TCRs . Note that the difference in ABloop arrangement between TCR and TCRpMHC was originally described for the LC13 TCR [7] and not for the A6 TCR as observed here . We also investigated the number of H-bonds between the TCR chains as a proxy for spatial re-arrangement between the TCR chains ( Fig 6 ) . For the JM22 TCR there are on average 1 . 37 H-bonds less between the TCR chains for TCRpMHC simulations than for TCR simulations . For the LC13 TCR this number is also slightly reduced by 0 . 51 H-bonds . For the A6 TCR there is a change of 0 . 61 H-bonds in the opposite direction but due to wider variability this number is not significant based on permutation tests ( quartile 0 . 76 ) . Also for the 1G4 TCR the change is insignificant . To investigate the overall H-bond network in TCRpMHC and TCR simulations we used pyHVis3D [14] which creates a three dimensional graphical representation of the H-bond distributions ( Fig 7 ) . This analysis shows different pictures for the four different TCRs . LC13 TCRpMHC simulations show an increased H-bond presence in the area around CDR3α and CDR3β as compared to LC13 TCR simulations which might indicate an interface rigidification . This effect is also present in the JM22 TCR but it is much less pronounced than in the LC13 TCR . In the JM22 TCR the area around the linker between the variable and constant region of the TCRβ chain is mainly affected by pMHC presence causing an increased presence of H-bonds which is not observeable in the LC13 TCR . A6 TCR simulations without pMHC show a higher H-bond frequency in the CDRs of the TCR α-chain while intra TCR chain H-bonds in this area are increased for TCRpMHC simulations . In the constant area of the TCR α-chain H-bonds dominate for TCRpMHC simulations . Similar to JM22 also 1G4 simulations show a higher H-bonds frequency around the linker between the variable and constant region of the TCRβ chain for TCRpMHC simulations . In contrast to all other TCRs the H-bond patterns of the 1G4 TCR around the CDR loops show a very mixed picture . Taken together this shows that there are no conserved differences in the H-bond patterns between TCR and TCRpMHC simulations .
We have presented an MD study of four different TCRs ( LC13 , JM22 , A6 , and 1G4 ) in their pMHC bound and unbound form using a total of 26 000 ns . To our knowledge this is the first study that investigates four different TCRs on such a large scale . The most similar study was published by Hawse et al . [41] who investigated the A6 and DMF5 TCR and found a global rigidification and dampened coupling in the linker between variable and constant TCR domains upon pMHC binding using computational mutagenesis with gradient-based minimization [27] and experimental hydrogen/deuterium exchange . Also Cuendet et al . [18] investigated the detachment of the A6 and B7 TCR from HLA-A2 using about 4 000 ns of steered MD code . This study found interesting binding interface characteristics but did not address the different dynamics within the TCR in the bound and unbound form . In two studies we have previously investigated the LC13 TCR [21 , 23] . In one of these studies [21] we found multiple significant differences between the pMHC bound and unbound LC13 TCR . These included CDR distance distributions , CDR compactness as well as differences in the TCR hydrogen bond network . In the current study we investigated if these results hold true for other TCR/MHC combinations . Surprisingly , we obtained very different results for JM22 TCR/HLA-A*02:01 compared with LC13 TCR/ HLA-B*08:01 . Thus the linker between the C and V regions of the TCR β-chain had a lower RG and RMSF for JM22 TCRpMHC simulations . Furthermore a reduced RG was seen in CDR3α and CDR3β while it was increased in CDR2β . Because LC13 and JM22 bind different MHCs , we investigated two more TCRs ( A6 and 1G4 ) which bind the same MHC as JM22 . Even when binding the same MHC type the dynamics within the TCRs vary significantly in , for example , RGs , SASAs , RMSFs ( Table 2 ) . These differences between TCRs upon pMHC binding are consistent with our experimentally measured finding that the JM22 , A6 and 1G4 TCRs have very different energetic footprints on HLA-A*02:01 [15] . Other experimental studies support the conclusions drawn in our study . With regard to changes at the binding interface , several structural studies have demonstrated local conformational changes upon TCR binding to pMHC [1] , while thermodynamic and kinetic studies of several TCRs , including JM22 [42] , are consistent with a reduction in conformational flexibility upon binding . With respect to conformational changes distal from the interface , while the AB-linker of the LC13 TCR has been described to differ between the X-ray structure of the HLA-B*08:01 bound and unbound structure [7] , for other TCRs this finding could not be replicated . Similarly the B4 . 2 . 3 TCR was reported to be effected in its H3 loop by the binding of H2-Dd presenting a 10-mer HIV-env peptide [39] . But no further support for this being a conserved mechanism could be found in any of the other 10 TCRpMHC complex structures for which a separate TCR structure exists [39] . While our finding argue against pMHC binding inducing conformation changes through allosteric mechanism in the TCR , they remain consistent with models proposing that conformational changes are introduced by mechanical mechanisms [4 , 5 , 8–11] . For example , pulling [8 , 9] and shearing [10] forces have been shown to enhance TCR triggering; with the CβFG loop seemingly be affected by side-wards pulling on the TCR [11] . We conclude that TCR structural dynamics do not differ between TCR/pMHC and TCR simulations in conserved and non-obvious ways . Taken together with previous studies our findings argue against a role for allosteric conformation change models in TCR triggering . Our results are consistent with mechanical models of conformational change , as well as aggregation and kinetic-segregation models . | The interaction between T-cells and other cells is one of the most important interactions in the human immune system . If T-cells are not triggered major parts of the immune system cannot be activated or are not working effectively . Despite many years of research the exact mechanism of how a T-cell is initially triggered is not clear . One hypothesis is that conformational changes within the T-cell receptor ( TCR ) can cause further downstream signalling within the T-cell . In this study we computationally investigate the dynamics of four different TCRs in their free and bound configuration . Our large scale simulations show that all four TCRs react to binding in different ways . In some TCRs mainly the areas close to the binding region are affected while in other TCRs areas further apart from the binding region are also affected . Our results argue against a conserved structural activation mechanism across different types of TCRs . | [
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] | 2019 | MHC binding affects the dynamics of different T-cell receptors in different ways |
Global demands for agricultural and forestry products provide economic incentives for deforestation across the tropics . Much of this deforestation occurs with a lack of information on the spatial distribution of benefits and costs of deforestation . To inform global sustainable land-use policies , we combine geographic information systems ( GIS ) with a meta-analysis of ecosystem services ( ES ) studies to perform a spatially explicit analysis of the trade-offs between agricultural benefits , carbon emissions , and losses of multiple ecosystem services because of tropical deforestation from 2000 to 2012 . Even though the value of ecosystem services presents large inherent uncertainties , we find a pattern supporting the argument that the externalities of destroying tropical forests are greater than the current direct economic benefits derived from agriculture in all cases bar one: when yield and rent potentials of high-value crops could be realized in the future . Our analysis identifies the Atlantic Forest , areas around the Gulf of Guinea , and Thailand as areas where agricultural conversion appears economically efficient , indicating a major impediment to the long-term financial sustainability of Reducing Emissions from Deforestation and forest Degradation ( REDD+ ) schemes in those countries . By contrast , Latin America , insular Southeast Asia , and Madagascar present areas with low agricultural rents ( ARs ) and high values in carbon stocks and ES , suggesting that they are economically viable conservation targets . Our study helps identify optimal areas for conservation and agriculture together with their associated uncertainties , which could enhance the efficiency and sustainability of pantropical land-use policies and help direct future research efforts .
Growing global demands for food and biofuels generate pressures for deforestation across the tropics [1] . Much of this deforestation is carried out without information on the spatial distribution of benefits and costs of deforestation [2] . Studies estimating the trade-offs between the economic value of multiple forest ecosystem services ( ES ) and agricultural conversion have been largely constrained to local and national case studies [3–7] . On the other hand , increasingly detailed , spatially explicit analyses of the global trade-offs between biodiversity and carbon emissions [8–12] and between carbon emissions and agricultural production [13 , 14] have been produced . Analyses that combine both approaches to analyse the economic trade-offs between agriculture and multiple tropical forests’ ES at the global level are , however , lacking . Identifying the spatial distribution of trade-offs between economic net losses and gains resulting from deforestation is important as it can help identify optimal areas for conservation and agriculture , thus informing pantropical land-use policies . Availability of spatial datasets on tropical deforestation [15] , agricultural crop distributions [16] , and potential yields [17] and economic values of ES in tropical forests [18] presents a unique opportunity to comprehensively evaluate the contemporary and future economic trade-offs and inefficiencies between carbon emissions , multiple ES , and agricultural conversion linked to tropical deforestation . Here we present a spatially explicit analysis using deforestation and crop distribution data for the period of 2000–2012 [15] . We compared agricultural benefits to both foregone avoided carbon emissions values and lost ES by developing a spatially explicit meta-analysis of the total economic value of ES in tropical forests ( Materials and methods ) . By overlaying the estimates of carbon emissions and ES values onto spatial analyses of land-use change and agricultural rents ( ARs ) , we quantified the net economic trade-offs and inefficiencies associated with tropical deforestation . We accounted for uncertainty using bootstrapping , Monte Carlo simulation methods , and different scenarios ( Materials and methods ) . Scenario A presents net agricultural rents of national crops ( i . e . , assuming that crops replacing forests can stochastically be those already existing within the country and deducting production costs ) ; scenario C presents net agricultural rents of the economically highest potential crop for that cell ( among the top 10 crops in terms of area and production value in the tropics when considering their potential yields , observed prices , and costs and ignoring know-how and cultural and infrastructure limitations; see Materials and methods ) , deducting production costs; and scenarios B and D are respectively similar to A and C except that production costs are not deducted . Scenarios A and B are designed to represent the contemporary deforestation scenario that makes an attempt to capture a more realistic agricultural expansion ( see Materials and methods for an evaluation of the plausibility of these scenarios ) . Scenarios C and D are designed to emulate the hypothetical conversion of land into the highest-rent crops in the long term as a response to increasing global agricultural demand .
Our meta-analysis of ES identified 3 top models with high support ( S1 Table and S1 Fig in Supporting information ) . The results pointed towards the influence of valuation method and type of ES ( although not statistically significant in the case of type of ES ) on the value of ES . We found a positive relationship of value with temperature and negative with year of publication and bird species richness ( S1 Fig ) . These models presented an improvement of predictive accuracy of 43%–49% with regards to direct benefits transfer at the global and regional levels ( S1 Table ) and predictive versus observed regression slopes of 0 . 879–0 . 884 ( S2 Fig ) . The dataset was also representative of the tropical forest biome ( S2 Table ) . The predictions from the ES meta-analytic model presented , however , a wide range of uncertainty ( Figs 1 and 2; its uncertainty is described in S3 and S4 Figs ) . Although the results are spatially explicit and present large levels of heterogeneity across space ( Fig 2 ) , to help dissect the findings , we present first results aggregated at the global , regional , and national scales before describing specific spatial heterogeneities . Under scenarios A and B in which a forest is replaced with crops already present in the country , the global annual net and gross agricultural benefits were on average I$32 , 000 , 000 , 000 ( 95% uncertainty range [UR] = I$19–I$47 , 000 , 000 , 000/year , Scenario A ) and I$53 , 000 , 000 , 000 ( UR = I$35–I$72 , 000 , 000 , 000/year , Scenario B ) . Under the scenarios in which a forest was replaced by crops with the highest potential rents , the net and gross annual values increased substantially to I$209 , 000 , 000 , 000/year ( UR = I$103–I$339 , 000 , 000 , 000/year , Scenario B ) and I$271 , 000 , 000 , 000 ( UR = I$164–I$403 , 000 , 000 , 000/year , Fig 1 ) . Annual externalities only related to carbon emissions were on average I$24 , 000 , 000 , 000 ( UR = I$22–I$35 , 000 , 000 , 000/year ) at market prices and I$50 , 000 , 000 , 000 ( UR = I$22–I$129 , 000 , 000 , 000/year ) at social prices ( Fig 1 , S5 and S6 Figs , Materials and methods ) . However , when total ES value ( TEV ) ( that includes the value of carbon emissions ) is considered , annual externalities increased to I$107 , 000 , 000 , 000 ( UR = I$85–I$146 , 000 , 000 , 000/year at market prices ) and I$135 , 000 , 000 , 000 ( UR = I$93–I$224 , 000 , 000 , 000/year at social prices ) . Thus , at the aggregate ( global ) level , agricultural expansion under scenarios A and B provided on average a net benefit only when compared with the value of carbon at market prices . In comparison with all other ES valuations , agricultural expansion under scenarios A and B resulted on average in net losses ( although the uncertainty ranges of scenarios A and B overlapped with the lower uncertainty bound of carbon emissions under social prices ) . Under a hypothetical future scenario in which all crops that replaced deforestation presented maximum rents , median annual agricultural rents exceeded on average the value of externalities because of carbon emissions , regardless of pricing , as well as TEV ( Fig 1 ) . The distribution of trade-offs varied substantially between countries ( Fig 3 , S7 , S8 and S9 Figs ) . Countries like Brazil , Indonesia , Peru , and Nicaragua presented high contemporary net losses under scenarios A and B but the capacity to obtain net gains if maximum rent crops were realized in scenarios C and D , pointing towards low production costs relative to further gains from intensive cash crops such as soybean , oil palm , maize , and sugar cane ( Fig 3 , S7 , S8 and S9 Figs ) . In contrast to most countries , several countries presented large differences between scenarios C and D , suggesting high production costs that would hinder intensive cash crops expansion . In this group were countries like Malaysia , Panama , Argentina , Paraguay , and Australia ( Fig 3 , S7 , S8 and S9 Figs ) . When considering the spatial distribution of losses under scenario A , a large heterogeneity was present within and between countries , showing where agricultural conversion was economically efficient and areas where it was inefficient ( Fig 2 , S3 and S4 Figs ) . Net losses of total ES occurred on average in most of South America ( except the Atlantic Forest ) , Madagascar , the Philippines , centre and northern parts of Borneo , Papua , and Indochina , excluding parts of Thailand and Malaysia . Conversely , net gains occurred in western tropical Africa , around the Gulf of Guinea and the eastern part of the Congo Basin , Thailand , parts of Sumatra , the Atlantic Forest , and areas in Bolivia and Peru ( Fig 2 , S3 and S4 Figs show the large heterogeneity of these results across space ) . The areas with large ES losses ( Fig 2 ) were largely in agreement with areas where carbon emissions out-competed agriculture when considered alone under social prices , with the exception of areas such as Southeast Asia and Madagascar , which presented also net losses ( S10 and S11 Figs ) . The maps of net gains from agricultural conversion presented large uncertainty towards higher ES values: the 2 . 5th percentile map resembled the 50th percentile map , but the 97 . 5th percentile map showed a majority of net losses from agricultural conversion under scenarios A and B ( Fig 2 , S3 , S4 , S12 , S13 and S14 Figs ) . By contrast , under scenario C , most agricultural conversion presented net gains under the 2 . 5th and 50th percentile maps ( S15 and S16 Figs ) except areas such as Latin America , Congo Basin , Papua and north of Borneo under the 97 . 5th percentile map ( S17 Fig ) , highlighting the difficulty to meet future agricultural opportunity costs in extensive areas using ES and carbon emissions alone . Our analyses to evaluate the plausibility of the scenarios considered showed that scenarios A and B are plausible given the spatial contagion of crop expansion illustrated by oil palm expansion in Southeast Asia ( S18 Fig; relationship between distance from existing plantation and new oil palm conversion β = –0 . 06 , p-value < 0 . 01 ) and that we could only find 24 new crop–country combinations ( out of the 2 , 903 considered ) with either missing area information or 0 area reported during the period of study ( S3 Table ) .
Our analysis reveals large spatial heterogeneity in net losses or gains from the agricultural conversion of tropical forests across subnational to global scales . Deforestation in Latin America ( except the Atlantic Forest ) was in general identified to generate net losses because of low agricultural rents—i . e . , if carbon emissions and ES values were internalized in the planning of individuals and corporations , these are not viable regions for conversion to agriculture under present conditions—and relatively high carbon emissions and ES values . These results were largely robust to uncertainty under scenario A ( Fig 2 , S3 and S4 Figs ) . By contrast , Southeast Asia ( mostly north of Sumatra , Thailand and the Malayan Peninsula ) were identified to generate net agricultural gains and to be a less preferred target for conservation interventions such as REDD+ investments . However , these results were susceptible to uncertainty ( net losses of ES because of agricultural conservation occur as well for the 97 . 5th percentile map , S4 Fig ) . The identification of these regions agrees with previous studies evaluating the optimal allocation of REDD+ funds [8] . Our results also agree with global trade-off analyses indicating the high agricultural yields in Southeast Asia versus low yields in the Neotropics and Afrotropics [13] , corroborating a crop advantage over carbon in Southeast Asia [14] . In general , our results show good agreement with the identification of areas where agriculture generates net gains in Thailand , the border of India and Nepal , and the Malayan Peninsula [14] . Our analyses , by incorporating the economic dimension beyond yields alone , further suggests that agricultural rents in Southeast Asia are able to surpass the value of carbon emissions , suggesting that high agricultural rents and thus conservation opportunity costs could compromise the viability of REDD+ projects in SE Asia [19] , although , yet again , these results are subject to uncertainty in the upper-level value of ES . Our analyses do not include trade-offs with biodiversity . Factoring biodiversity may mean , however , that conservation funds would need to be invested in Southeast Asia [8 , 9] , which hosts 4 biodiversity hotspots and combines high levels of endemism and threat . This highlights a strong trade-off between agricultural rents and ES with biodiversity in Sumatra and the Malayan Peninsula . This corroborates also the ES–biodiversity negative trade-off found by the ES meta-analytic models: high economic value of ES requires high density of beneficiaries , while high levels of biodiversity require , by contrast , low levels of disturbance and hence less beneficiaries [20] . This trade-off , although pervasive , is not necessarily dominating across all the tropics , as it can still be modulated by agricultural opportunity costs . For instance , because of low agricultural rents , and with high robustness to uncertainty under scenario A , we identified highly biodiverse areas with high potential for species extinctions such as the Philippines , Borneo and Madagascar as net losers from agricultural conversion , showing them as economically viable conservation targets . We identified 2 different dynamic interpretations of the trade-offs between agriculture and forests depending on whether contemporary and hypothetical maximum rent crop conversion were considered . Under contemporary conversion , deforestation produced net negative global externalities from 2000–2012 when the crops present in each country replaced forests , a realistic scenario given that existing local crops reflect cultural and labour constraints ( e . g . , labour constraints make the adoption of oil palm in countries like Brazil unlikely ) , and there is high spatial autocorrelation inherent to agricultural land use [21] ( S17 Fig ) . Our analysis of time series of crop areas further confirms the plausibility of scenarios A and B . These net losses are likely to be even more negative than we report because the analyses assumed that all conversion resulted in immediate and sustained productive agriculture and did not incorporate estimates of degraded land resulting from agricultural abandonment , such as Imperata grasslands in Southeast Asia [22] . On the other hand , crops at the contemporary deforestation frontiers are expected to evolve towards higher-rent crops with better yields and economies of scale . Under these intensification scenarios in which the highest rent crop is adopted , agricultural rents surpass the externalities of carbon emissions and combined ES with very few exceptions . This points towards pantropical runaway costs for conservation [23] that will be very difficult to match under current carbon prices in the long term . It should be noted , however , that the value of ES can also change in space and time [24] and would be expected to increase as tropical forests become more scarce [25] . Similarly , the social price of CO2 increases through time as the impacts of climate change unfold and the gross domestic product ( GDP ) increases [26] . Our current analysis is , however , done using the CO2 prices from the period of study . Future research would thus need to update the changing value in ES , CO2 , and agricultural rents . Our analyses have several limitations . We compounded several datasets and analyses that required uncertainty propagation through modelling . Although we dealt with this problem using bootstrapping and Monte Carlo simulation methods , a research priority would be to reduce data paucity on ES valuation studies ( so that individual models for each ES can be created ) and the location of crops replacing forests , which would substantially reduce the uncertainty attached to our results . Although the cross-validation analysis and comparison with direct benefit transfer suggest that our ES meta-analysis is a step forward compared to direct benefit transfer and assumptions of constant ES values per biome across space , the results related to the ES meta-analysis should be contemplated while bearing in mind the limitations associated to the dataset . Although the uncertainty in the predictions of the ES models was considered , the individual studies from The Economics of Ecosystems and Biodiversity ( TEEB ) dataset that were used to develop the meta-analytic models present themselves inherent inaccuracies and uncertainties that cannot be captured by bootstrapping or cross-validation of the ES models . Estimating the value of ES is very challenging as value depends , in a complex manner on the social , ecological , and economic context of the location ( e . g . , flood protection values will depend on the hydrological characteristics of the catchment and the communities that can be affected by the flood ) . Although we attempted to capture the contextual realities as much as possible statistically , there is no guarantee that all the variables influencing value were included in the meta-analytic models or that the variables we used had sufficient resolution to capture the nuanced socioecological dynamics that shape values in each specific location . As such , we highlight that there is potential for wide error in the values of ES used for analysis and that these errors may have escaped our treatment of uncertainty . In this respect , the trade-off maps between CO2 and agricultural rents present higher confidence than those between TEV and agricultural rents , making it essential that future work strives at generating and synthetizing further ES valuation studies . Because of data paucity , we did not estimate net externalities from agriculture , which is conservative given that agricultural activities tend to generate negative externalities ( e . g . , water pollution , increased flooding , and greenhouse emissions ) that are greater in magnitude than the positive externalities ( landscape aesthetic values , waste sink ) [27] . Our analyses were also static and did not consider market feedbacks through international trade . Market feedbacks can modify agricultural rents in the region where land use occurs and can indirectly modify distant lands through land displacement [28] . For instance , drastic intensification or expansion of oil palm would lead to increases in supply that could cause prices to drop , presumably preventing further oil palm expansion elsewhere [29 , 30] . Further research is needed to understand the role of global market feedbacks on conservation [31] . Another inherent trade-off with the global scale of our analyses is that our results are aggregate in nature and fail to incorporate the necessities of the actual actors behind deforestation . Our maps are thus only intended to support global-scale analyses and should not be used for local decision-making . Our analyses would need to be complemented with on-the-ground , context-dependent studies to evaluate the actual distribution of winners and losers from agricultural conversion . We also note that although economic valuation of ES is fundamental to support land-use planning , valuation of ES is ultimately only 1 decision tool for policy makers . Reliance on economic valuation alone may not lead to the optimal conservation outcomes [32] , and other criteria such as biodiversity and social equity would be needed to support conservation programs . Other limitations because of data paucity were that our estimates of the carbon fraction of biomass were based on a constant conversion factor [33 , 34] . In reality , wood density is variable across tree species . Future work on carbon emissions estimation would benefit from combining datasets of wood density per species [35] and tree species maps that could be generated with future developments of airborne imaging spectroscopy [36] . Another limitation is that we could not find pantropical maps of peatland depths and resorted to using depth measures from peatlands in Indonesia [37] as a surrogate . The discovery of tropical peatlands and estimation of their depth is currently ongoing . For instance , one of the largest peat swamp forests ( 145 , 500 km2 ) was recently discovered in the Cuvette Centrale depression in the central Congo Basin , on the border of the Republic of the Congo with the Democratic Republic of the Congo [38] . Considering the large potential carbon emissions of its conversion would change our results in the areas of the Cuvette Centrale depression ( confluence of the rivers Congo and Ubangi ) corresponding to the Democratic Republic of Congo from small net losses from agricultural conversion ( Fig 2 ) to large net losses . Our results could thus be refined when global maps of peatland depth become available . Our results are helpful in that they help map the uncertainty associated to agriculture–ES trade-offs that can be used to motivate further research in the tropics , areas that receive comparatively much lower research efforts [39] . These results could also help identify the tropical trade-offs between carbon emissions , multiple ES , and agricultural rents . These maps can be used as a starting point to devise spatially efficient conservation and agricultural development policies aimed at internalizing the value of ES . Three main groups of policy interventions ( namely regulations and community-based and economic instruments ) have been implemented with varied outcomes [40] . Among regulations , the “polluter pays” principle , conditioning production subsidies on environmental performance , and interventions directed at the supply chain ( e . g . , removal of farmer credits was effective at slowing down deforestation in Brazil [41] ) could be considered to guarantee provision of multiple ES . Among economic instruments , and besides REDD+ , taxes on agricultural inputs , taxes on consumption , and agri-environmental schemes—which are widely implemented in high-income regions like the European Union—could also be considered in the tropics . Agri-environmental schemes could be considered as flexible interventions to promote activities that enhance provision of ES at the local scale [40] . As a drawback , agri-environmental schemes are costly to implement and to monitor , which may not be viable in low-income settings . In this respect , our results can help identify the largest ES value–agricultural rent differentials as areas where their potential economic viability is highest . Another limitation is that all these interventions could displace production elsewhere through indirect feedbacks and rebound effects . Land-use zoning , certification , and spatial strategic deployment of agricultural innovations could be considered to prevent rebound effects and displacement [42] . Although knowing the spatial distribution of trade-offs and their large associated uncertainty is still a long way from identifying the most effective policies to internalize ES losses into agricultural production , the developed maps can be useful to support evidence-informed spatial policies and to identify areas of high-potential agricultural rents where potential rebound effects could occur . Considering these policies is imperative; the alternative is a spatially inefficient agricultural conversion of tropical forests at the expense of the loss of valuable ES and biodiversity .
We obtained spatial information on the distribution and magnitude of loss of tropical rainforests from 2000 to 2012 [15] . On these deforestation maps , we overlaid data on the distribution and of current and potential yields of major commercial crops , pastures , and livestock density [16 , 17 , 43] and developed meta-analytic models of ESs and estimates of carbon emissions . These meta-analytic models are an update of Carrasco et al . [20] in which we increased the time resolution of the explanatory variables , used information theory and switched to mixed-effects models without variance structures to allow bootstrapping of the models . Potential yields have been determined using a rain-fed land productivity and a water-balance model combined with soil moisture and temperature radiation information integrated in a crop growth model [17] . The combination of these analyses yielded a pantropical spatial map of cells for the distribution of tropical forest loss , the distribution of agricultural rents generated , ESs lost , and carbon emitted . Bootstrapping and Monte Carlo simulations were used to estimate uncertainty in these maps . We restricted our analysis to the 51 countries where deforestation was detected from 2000 to 2005 [44] ( Fig 3 ) . The spatial extent of the analysis was the tropical forest biome in which we used a grid of 0 . 1° resolution , leading to 159 , 458 map cells analysed . These analyses were done using the R environment , and ArcGIS version 10 . 2 . 1 was used to develop all the maps . We estimated the carbon dioxide emitted from deforestation because of losses in carbon aboveground , belowground , and stored in dead organic matter because of conversion from forests to agriculture . Given the large uncertainty on its estimates in the literature [45] , carbon stored in the soil was conservatively assumed to remain the same from the transition from forest into agriculture . We did , however , consider the carbon emitted if peat soil was converted to agriculture . Geographic information systems ( GIS ) maps of biomass above and belowground stored in forests was obtained from Ruesch and Gibbs [46] . Biomass was expressed as carbon tonnes per hectare content using a carbon fraction of biomass [33 , 34] . Tables 2 . 2 and 2 . 3 in IPCC [34] were used to estimate the amount of carbon stored in soil and dead organic matter in tropical forests . Carbon in soil within peat swamps was corrected considering the distribution of peat swamps , and because of data paucity , depth was estimated from peat swamps in Indonesia [37 , 47] . All carbon estimates were expressed as tonnes of carbon dioxide per hectare . We employed 2 carbon price scenarios: ( i ) market prices ( US$13 . 6/tC ) , the average of the market prices per tonne of carbon from August 2011 to October 2016 using the price of California Carbon Allowance Futures [48]; and ( ii ) social prices ( average of US$30/tC ) , based on the White House estimates published in the year 2016 referring to the year 2010 using a discount rate of 3% under 5 different socioeconomic emissions scenarios of the Dynamic Integrated Climate-Economy model ( DICE ) [26] . To estimate the revenues generated by agricultural conversion we selected the top 10 crops in terms of area and value of production in tropical countries [66] . Since some crops appeared in both groups we were left with 18 crops . We added cattle production to the list of crops that were [66]: banana , bean , cassava , cocoa , coconut , coffee , cotton , cowpea , groundnut , maize , millet , oil palm , rice , rubber , sorghum , soybean , sugar cane , and wheat . Additionally , we accounted for the benefits of selling logged timber from deforestation prior to agricultural production . Benefits of sales of timber after land conversion were estimated by multiplying the proportion of growing stock in commercial species by the total growing stock in each region [67] , then multiplying by the national export price per unit of volume [66] ( final values per hectare are shown in S2 Data ) . The available global crop maps corresponded to the period around the year 2000 . Given this limitation we could not link specific crops to deforestation using the maps . Given this uncertainty , we estimated agricultural benefits under 2 broad scenarios . First , under scenarios A and B , we replaced converted forests with the crops already found in the country using Monte Carlo techniques that sampled crops at random from all the deforested cells in the country . Scenarios A and B are meant to represent the status quo by taking into account potentially both smallholder and industrial agricultural activities within each country . These scenarios imply that forest replacement is only possible for crops that have proven to be viable in the country in the past and that agricultural activities are more similar the closer they are in space [21] . They also invoke the requirement of know-how and infrastructure ( e . g . , mills and high levels of labour inputs in the case of oil palm ) for each crop . We nonetheless further evaluated the plausibility of scenarios A and B following 2 approaches . First , we used a spatially explicit dataset of oil palm expansion in Southeast Asia [68 , 69] . If scenarios A and B were plausible , we would expect to see that new oil palm conversion occurred in the vicinity of existing oil palm plantations . We evaluated this with a model that used distance from existing plantations in 2010 as prediction of new oil palm plantations in 2014 using a generalized least-squares model . Second , we evaluated the time series of crop areas of all crops grown in the countries studied from 2000 to 2014 . We evaluated whether crops not present in the countries in previous years started to be grown anew during the time series . Under our assumptions for scenarios A and B , the emergence of new crops would be unlikely over the period of study . A second set of scenarios C and D replaced converted forest with the crop providing the highest potential rent for each cell ( i . e . , industrial and cash crops that involve transport costs to the market ) . This scenario relaxes the assumption that only national existing crops can replace forests and represents a hypothetical future scenario in which specific crop know-how and infrastructures are available in every cell and country . Scenarios A and B therefore present forests replaced by the expansion of national cropping systems with ( A ) and without ( B ) production costs ( labour and fertilizer ) ; scenarios C and D present forests replaced by the crop with the highest potential rent with ( C ) and without ( D ) production costs ( labour , fertilizer , and transport costs ) . We derived transport costs using existing maps of travelling times to the nearest city [54] , driver salaries ( using agricultural wages when available and if not , manufacturing wages ) [65] and fuel prices in each country for the year 2000 [70] ( S2 Data shows the wages and fuel prices used ) . We assumed a standard truck capacity of 18 m3 and an average speed of 45 km/h and calculated the corresponding fuel consumption [71] . We further assumed that the truck returned empty after delivering the produce and only 1 driver was involved . Spatially explicit information on capital costs was however missing and we could not include these in the analysis . Labour costs were estimated by obtaining standard estimates of person-days per hectare and year to produce each crop in the tropics from literature review ( S2 Data shows the estimated person-days and the sources for each crop ) and then multiplying by the agricultural wages in each country whenever available and , if not , multiplying by the manufacturing wages [65] . Person-days estimates were coarse , as it was not possible to identify different estimates per crop and country combinations . These limitations motivated scenarios B and D that did not deduct production costs to estimate the boundaries of the uncertainty caused by production costs . We derived global maps of fertilizer costs using maps of fertilizer usage [72] and later multiplying them by the average price of fertilizer in each country from the years 2000 to 2002 [66] ( S2 Data ) . Conservatively , total and immediate conversion from deforestation into agricultural activities was assumed . The annual net rents from agriculture ( AR ) in each cell i were calculated as: ARi=yuipu−cui where u represents the crop assumed to occupy each deforested cell i under A to D scenarios; yui represents the yield of crop u in cell i; pu is the farm gate price of the crop in each country per year and ton , averaged from 2000 to 2009 if available for specific countries [66] and using neighbouring countries of comparable level of development if no data were available for specific crop–country combinations ( S2 Data ) ; and cui is the production costs of crop u that include transport , fertilizer , and labour costs as described above depending on the scenario considered . To calculate rents from cattle we estimated average national carcass efficiencies [66 , 73] that were later combined with global pasture maps [47] and the number of cattle per unit of area [43] . We used 2016 international dollars to express all economic values . We combined our estimates of carbon emissions and ES losses with agricultural rents , assuming that they represented the marginal cost of deforestation in relation to the marginal benefits of alternative land uses [3] . We assumed that the remaining tropical forest area , after the marginal deforestation , was far from the threshold for which ES can no longer be provided , leading to a spike in value . Under this assumption , the annual economic impact , I , can be approximated by the following equation: I=∑in[ ( TEVi−ARi ) Ai] where TEV is the value of the externalities because of carbon emissions ( annualized using a discount rate of 5% and a 100 years’ time horizon ) and annual ES losses and AR represents net benefits from logging ( annualized using a similar discount rate and time period ) plus annual rents from agriculture of converting the area deforested A in cell i . Our analysis involved the combination of several spatial datasets and analyses with inherent uncertainty on their own . The combination of individual sources of uncertainty may lead to a greater uncertainty in the estimates than individual uncertainties alone . There was thus a need to explicitly model each source of uncertainty and to propagate this uncertainty to estimate what was the combined uncertainty generated by the analyses . As noted , we used several scenarios and sampled at random from agricultural fields to account for uncertainty in production costs and the distribution of crops replacing forests . We further dealt with uncertainty using bootstrapping methods in the case of the meta-analytic models of ES . We then employed uncertainty distributions to take into account uncertainty in the crop prices , the carbon prices , and the deforestation maps . Monte Carlo simulation methods were then used to sample 200 times from each source of uncertainty to generate uncertainty distributions of potential outcomes for each cell of the map . Specifically , we bootstrapped each of the 3 selected ES models to evaluate their predictive uncertainty using the function bootMer from the lme4 package [63] . We carried out 500 bootstraps that involved resampling the dataset , fitting the model again and producing predictions for each type of ES in the map at a 0 . 1° resolution across the global tropical forest biome . As a result , a distribution ( n = 500 ) of ES lost annually in each cell in the map was obtained ( i . e . , 500 different maps of total ES values ) . Among these 500 maps , 1 map was chosen at random each time the model was run . The uncertainty in the deforestation maps was modelled by modifying the original dataset stochastically to reflect 87% and 99 . 7% accuracy in the classification of forest loss and no loss in the tropics [15] ( i . e . , each cell classified as forest lost had a probability of 0 . 87 of remaining as forest loss and 0 . 13 of being changed to not forest loss in each run ) . Maps of forest loss were modified stochastically according to these probabilities every time the model was run . To account for the uncertainty in market prices of carbon , we considered the daily time series of prices from August of 2011 to September of 2016 . The time series was sampled at random every time the model was run , and the sampled value used as the market price of carbon . For the social price of carbon , we considered 1 , 000 observations per each of the 5 modelling scenarios considered and sampled at random among these 5 , 000 values to select the social price of carbon each time the model was run . For agricultural prices , we estimated the standard deviation of the prices for each crop–country combination from 2000 to 2009 . We then constructed normal distributions using the mean and standard deviations of the observed prices . The normal distributions were sampled each time the model was run . The final combination of uncertainty sampling using Monte Carlo methods led to a distribution of model outcomes . 2 . 5th and 97th percentiles of the distributions of outputs generated were estimated and used to generate uncertainty ranges of the results . | Tropical forests are often destroyed to clear land for agriculture or to harvest forestry products , such as timber . However , the benefits derived from agriculture and these products are countered by the costs to the environment and the loss of ecosystem systems ( the benefits that nature provides to humans ) . Little is known about how the economic benefits and costs of deforestation vary on a global scale . Knowing the distribution of benefits and costs would help identify regions where deforestation is most and least beneficial and thus could help select areas to focus conservation efforts . We studied the trade-offs between agricultural benefits , carbon emissions , and losses of multiple ecosystem services ( ES ) in tropical deforested areas around the world . We find large differences between costs and benefits globally . For instance , we identify the Atlantic Forest , areas around the Gulf of Guinea , and Thailand as areas where the benefits from agricultural conversion are greater than environmental costs , which could make it difficult to incentivize and implement biodiversity conservation strategies that are based on payments to farmers . By contrast , Latin America , insular Southeast Asia , and Madagascar represent areas with low agricultural benefits and high environmental costs . This suggests that these regions are economically viable conservation targets . Our study helps identify strategies to enhance the sustainability of land-use policies in the tropics . | [
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] | 2017 | Global economic trade-offs between wild nature and tropical agriculture |
In contrast with common human infections for which vaccine efficacy can be evaluated directly in field studies , alternative strategies are needed to evaluate efficacy for slowly developing or sporadic diseases like tularemia . For diseases such as these caused by intracellular bacteria , serological measures of antibodies are generally not predictive . Here , we used vaccines varying in efficacy to explore development of clinically useful correlates of protection for intracellular bacteria , using Francisella tularensis as an experimental model . F . tularensis is an intracellular bacterium classified as Category A bioterrorism agent which causes tularemia . The primary vaccine candidate in the U . S . , called Live Vaccine Strain ( LVS ) , has been the subject of ongoing clinical studies; however , safety and efficacy are not well established , and LVS is not licensed by the U . S . FDA . Using a mouse model , we compared the in vivo efficacy of a panel of qualitatively different Francisella vaccine candidates , the in vitro functional activity of immune lymphocytes derived from vaccinated mice , and relative gene expression in immune lymphocytes . Integrated analyses showed that the hierarchy of protection in vivo engendered by qualitatively different vaccines was reflected by the degree of lymphocytes' in vitro activity in controlling the intramacrophage growth of Francisella . Thus , this assay may be a functional correlate . Further , the strength of protection was significantly related to the degree of up-regulation of expression of a panel of genes in cells recovered from the assay . These included IFN-γ , IL-6 , IL-12Rβ2 , T-bet , SOCS-1 , and IL-18bp . Taken together , the results indicate that an in vitro assay that detects control of bacterial growth , and/or a selected panel of mediators , may ultimately be developed to predict the outcome of vaccine efficacy and to complement clinical trials . The overall approach may be applicable to intracellular pathogens in general .
Most vaccines against infectious diseases in clinical use today act by stimulating the production of antibodies , which block virus entry , neutralize toxins , or otherwise limit infection through a variety of mechanisms . Measurements of serum antibodies have therefore been applied to predict successful vaccine-induced protection against diseases such as rabies , tetanus , and diphtheria [1] . In contrast , cell-based immune responses provided by T lymphocytes may be more important for control of intracellular pathogens . To date , however , no predictive correlates have been established for any intracellular pathogen . Understanding T cell effector functions that control intracellular infections , and developing clinically useful predictive correlates , would greatly facilitate evaluation of new vaccines for intracellular pathogens of major public health importance such as Mycobacteria , Chlamydia , Salmonella , and Leishmania . To address these questions , we have exploited experimental infection models that use the Live Vaccine Strain ( LVS ) of Francisella tularensis , a Gram-negative intracellular bacterium that causes tularemia . Although the incidence of tularemia in the U . S . is low , F . tularensis is a bioterrorism concern due to its high infectivity and mortality rates following pulmonary infection [2] . Antibiotics are effective , but difficulties with diagnosis and with prompt treatment make developing vaccines a priority [3] , [4] . However , the sporadic nature of disease likely means that vaccine field trials for efficacy are impractical . The use of live attenuated Type B Francisella strains as vaccines in the former U . S . S . R . during and after World War II had clear impact on the epidemiology of disease [5] . Successful vaccination of humans using attenuated bacteria has been mimicked experimentally; rabbits , guinea pigs , rats , and mice are all either natural hosts or are susceptible to Francisella , and provide reasonable animal models for vaccination studies [6] . LVS , an attenuated strain derived from F . tularensis subsp . holarctica ( Type B ) [7] , is the only vaccine against tularemia currently undergoing clinical development in the U . S . [2] , [4] . Human challenge studies , as well as use among laboratory workers , suggest that LVS vaccination provides at least partial protection against some forms of the disease , but specific efficacy levels have not been firmly established [2]–[5] , [8]–[10] . In contrast , observations suggested minimal protection of people following vaccination with killed Francisella despite stimulating production of abundant serum antibodies [3] , [8] , [11]–[12] , similar to experimental studies using mice [13]–[16] , especially following aerosol exposure to the most virulent strains . Examination of broth cultures of all strains of F . tularensis , including LVS , on blood agar plates reveals a variety of colony morphologies . These opacity variants suggest bacterial phase variation , a phenotype that may confound evaluation and use of LVS . Lots of LVS that include a high proportion of phase variants have been associated with reduced immunogenicity in humans [4] , [17] , as suggested earlier in animal studies [7] . Stable opacity variants of LVS , denoted LVS-G and LVS-R , have been isolated in vitro [18] . These isolates express alternative chemotypes of Francisella LPS , and appear to be analogous to clinical lots with reduced immunogenicity in humans . However , LVS-G and LVS-R have not been tested as vaccines in any experimental models , including mice , to date . Murine infections with LVS provide a well-established model of infection and immunity against Francisella , and indeed for intracellular bacteria generally [19]–[21] . Similar to many intracellular bacteria , LVS infects and replicates primarily in macrophages [22] , but exhibits convenient route-dependent virulence in mice [19] . Thus , LVS administered to mice subcutaneously or intradermally ( ID ) has a high LD50 of about 106 and establishes a vaccinating infection , eliciting strong cellular as well as humoral immune responses . However , doses of 101 or more of LVS administered to mice intraperitoneally ( IP ) or intravenously are lethal . BALB/c or C57BL/6 mice vaccinated ID with 104 LVS survive lethal challenge with LVS of up to 106 IP , and are at least partially protected against parenteral challenge with fully virulent Type A F . tularensis SchuS4 [3] , [23]–[24] . Also similar to many intracellular pathogens , in vivo studies clearly demonstrate that this protection is dependent on T lymphocytes , and involves production of Interferon gamma ( IFN-γ ) , Tumor Necrosis Factor alpha ( TNF-α ) , and nitric oxide ( NO ) . To further uncover T cell effector mechanisms , we have previously developed an in vitro tissue culture system to mimic in vivo immune responses [25]–[26] , in which LVS-immune lymphocytes are co-cultured with LVS-infected bone marrow derived macrophages and intramacrophage bacterial replication is measured . LVS-immune splenocytes , liver leukocytes , and lung leukocytes all control intramacrophage LVS replication in vitro , but naive cells do not [27] . In this assay , appropriate T cell subpopulations but not B cells or myeloid cells have activity , and the model appears to faithfully reflect known in vivo T cell effector mechanisms [25]–[26] . Here , we take advantage of a panel of Francisella vaccine candidates , including LVS , LVS-G , LVS-R , and heat-killed LVS , that induced quantitatively different levels of protection in mice against Francisella challenge . These vaccines were chosen to approximate Francisella vaccines studied in humans; thus , LVS has been associated with reasonable efficacy , while lots of LVS with higher proportions of opacity variants exhibit reduced immunogenicity ( modeled here by the stable variants LVS-G and LVS-R ) , and killed Francisella provided poor protection in humans . Using this panel , we searched for immune responses that predict successful protection . We found that the relative activity of Francisella-immune lymphocytes in vitro in the co-culture assay , as well as the relative expression of a group of immunologically-related genes in cells recovered from this assay , correlated with the degree of protection observed in vivo . This approach to identifying correlates , which couples a functional in vitro assay that detects reduction in intracellular bacterial loads with expression of relevant mediators , may be generally applicable to vaccine-induced protection against intracellular pathogens .
These studies carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All experiments performed using LVS only were conducted under protocols approved by the Animal Care and Use Committee ( ACUC ) of CBER . Experiments including F . tularensis SchuS4 challenge were performed at the Rocky Mountain Laboratories ( RML ) under protocols approved by the RML ACUC . Both sets of protocols stressed practices and procedures designed to strictly minimize any suffering . Six to twelve week old wild type male C57BL/6J mice were purchased from Jackson Laboratories ( Bar Harbor , ME ) . All mice were housed in sterile microisolator cages in a barrier environment at CBER/FDA , fed autoclaved food and water ad libitum , and routinely tested for common murine pathogens by a diagnostic service provided by the Division of Veterinary Services , CBER . Within an experiment , all mice were age matched . F . tularensis strain SchuS4 , provided by Jeannine Peterson , ( Centers for Disease Control , Fort Collins , CO ) ; F . tularensis LVS ( American Type Culture Collection 29684 ) ; F . tularensis LVS-G and LVS-R , originally obtained from Francis Nano ( University of Victoria , Victoria , British Columbia , CA ) ; and Listeria monocytogenes strain EGD ( ATCC 15313 ) were all grown to mid-log phase in modified Mueller-Hinton ( MH ) broth ( Difco Laboratories , Detroit , MI ) , as previously described [28] , harvested , and frozen in 1 ml aliquots in broth alone at −70°C . For each bacterial stock , separate experiments determined numbers of live colony forming units ( CFU ) , confirmed typical colony morphologies , and confirmed expected LD50s and times to death using adult male BALB/cByJ mice ( which are the most sensitive strain for quality control testing ) . Bacteria were periodically thawed for use , and viability was quantified by plating serial dilution on modified MH agar plates . Aliquots of F . tularensis LVS were heat killed at 56°C for 30 minutes immediately prior to use , and complete killing confirmed by plate count . Groups of mice were immunized by intradermal ( ID ) injection with 1×104 CFU LVS , 1×104 LVS-G , 1×104 LVS-R , or an amount equivalent to 1×108 heat-killed LVS; doses of each were optimized in initial experiments for maximal protection against lethal IP LVS challenge . All vaccines were diluted in 0 . 1 ml phosphate-buffered saline ( PBS ) ( BioWhittaker , Walkersville , MD ) containing <0 . 01 ng of endotoxin/ml . Actual doses of inoculated bacteria were retrospectively determined by plate count; control groups received 0 . 1 ml PBS ID . As indicated , four – twelve weeks after vaccination , mice were challenged with 103 – 106 LVS intraperitoneally ( IP ) , or 50 CFU SchuS4 subcutaneously ( SC ) , and monitored for survival . Animals were euthanized when clearly moribund . Single-cell suspensions of splenocytes were generated for in vitro culture , flow cytometry , and qRT-PCR analysis by standard techniques , and had no detectable CFU at the time of harvest . Viability was assessed by exclusion of trypan blue and flow cytometry ( see below ) . Co-cultures were performed in 24 well tissue culture plates as described previously [25]–[27] , [29]–[31] . Briefly , bone marrow macrophages ( BMMØ ) were cultured in complete DMEM ( DMEM supplemented with 10% heat-inactivated FCS [HyClone , Logan , UT] , 10% L-929-conditioned medium , 0 . 2 mM L-glutamine , 10 mM HEPES buffer , and 0 . 1 mM nonessential amino acids ) in 24 well plates . Confluent adherent macrophage monolayers were infected for 2 hours with F . tularensis LVS at a multiplicity of infection ( MOI ) of 1∶20 ( bacterium-to-BMMØ ) , washed , treated for 60 min with 50 µg/ml gentamicin , and washed extensively with antibiotic-free medium . Single-cell suspensions of splenic lymphocytes derived from vaccinated mice ( 5×106/well , or as indicated ) were added to confluent LVS-infected macrophages ( ∼1×107/well ) [25]–[26] . At the indicated time points , non-adherent cells were harvested , centrifuged and assessed for changes in cell surface phenotype by flow cytometry or gene expression by qRT-PCR as described below . Supernatants from harvested cells was collected and stored at −70°C until analyzed for nitric oxide and cytokines as described below . Intracellular bacterial loads in adherent macrophages were determined as previously described . Additional macrophages were collected following incubation in 0 . 05% trypsin/EDTA for 5 minutes , followed by neutralization with complete media containing serum . Cells to be assessed for changes in gene expression by qRT-PCR were pelleted by centrifugation for 10 minutes at 1000 rpm , immediately immersed in RNAlater ( Ambion , Austin , TX ) and stored at −70°C until further characterization . Total RNA was extracted from samples using RNeasy mini kits ( Qiagen , Valencia , CA ) , according to the manufacturer's directions . RNA quality and concentration were assessed by Bioanalyzer , including calculation of the RNA Integrity Number ( RIN ) via a software algorithm that estimates RNA sample integrity from elements in the bioanalyzer electrophoretic trace , and then assigns a score to RNA quality between 0 and 10 ( Agilent Technologies , Santa Clara , CA ) . One microgram of RNA was used to synthesize cDNA using the commercially available kit RetroScript Reverse Transcription for RT-PCR ( Ambion , Applied Biosystems Foster City , CA ) , following the manufacturer's instructions . Semi-quantitative real-time PCR amplification was completed with an ABI Prism 7000 sequence detection system ( Applied Biosystems , Carlsbad , CA ) . For initial screening of genes' expression , cDNA prepared from non-adherent cells was diluted and used to amplify a panel of genes of immunological interest ( e . g . , Th1-Th2-Th3 RT2 Profiler PCR Array System , SABiosciences , Frederick , MD ) , following the manufacturer's instructions . To validate the initial array qRT-PCR results , a second series of independent amplifications for selected genes were performed . Independent primers and probes were purchased from Applied Biosystems . cDNA was initially diluted to 100 µl and then two µl of each cDNA was further diluted to a volume of 25 µl PCR mix ( Applied Biosystems ) containing 0 . 1 µM and 0 . 2 µM of each primer and probes , respectively . Serial dilutions of each individual gene were used to generate a Glyceraldehyde phosphate dehydrogenase ( GAPDH ) standard curve . For PCR amplifications , the initial denaturation at 95°C for 10 minutes were followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute . The level of mRNA of each gene relative to the GAPDH mRNA concentrations was calculated by plotting the crossing point ( Ct ) of each amplification in relationship to the GAPDH standard curve . Delta Ct ( ΔCt ) , and the ratio between ΔCt of vaccines' samples and control samples ( ΔΔCt ) , were then calculated . Single cell suspensions prepared from spleens and splenocytes recovered from co-culture after the indicated time of culture were stained for a panel of murine cell surface markers and subjected to multiparameter analyses using a Becton-Dickinson LSR II flow cytometer ( San Jose , CA ) and FlowJo ( Tree Star , Inc ) software essentially as previously described [29]–[30] . Briefly , cells were washed and resuspended in flow cytometry buffer ( PBS/2% serum ) , and non-specific binding of antibodies was inhibited by blocking Fc receptors with anti-CD16 ( Fc Block; BD Pharmingen ) . To discriminate live from dead cells , a staining step for dead cells was performed using a commercially available kit and following manufacturers' instruction ( Live/Dead Staining Kit , Invitrogen ) . The cells were then washed and stained for cell surface markers . Antibody concentrations were optimized separately for use in seven- to nine-color staining protocols as required , using appropriate fluorochrome-labeled isotype matched control antibodies . The following antibodies were used: anti-B220 ( clone RA3-6B2 ) , anti-CD19 ( clone 1D3 ) , anti-TCRβ ( clone H57-597 ) , anti-CD4 ( clone RM4-5 ) , anti-CD8β ( H35-17 . 2 ) , anti-NK1 . 1 ( clone PK136 ) , anti-CD11b ( clone M1/70 ) , anti-Gr-1 ( clone RB6-8C5 ) , and anti-CD11c ( cloneHL3 ) , each labeled with a variety of fluorochromes as needed ( above antibodies were purchased from BD Pharmingen ) . Kaplan Meier curves were plotted to compare time to death following lethal LVS challenge between different vaccine groups , and log-rank ( Mantel-Cox ) analyses calculated to compare survival of different groups ( using Prism , GraphPad Software , La Jolla , CA ) . Linear regression models were fit to compare the effects of splenocytes on controlling bacterial growth from different vaccine groups while adjusting for splenocyte concentration . Univariate and multivariate logistic regressions were used to correlate protection against lethal LVS challenge with either fold change in gene expression at two different time points ( ∼6 weeks after vaccination and ∼12 weeks after vaccination ) , or with all data combined across both time point using standardized scores of gene expression . The results of these analyses were quite similar , and thus the results using all data combined are shown here . Standardized scores were used to protect against the possibility that the relative magnitude of gene expression for any given gene might be relatively different at the early time point compared to the late time point . Standardized scores were obtained by subtracting the average log expression level and then dividing by the standard deviation of the log expression level in the same experiment . The Akaike information Criterion was used to compare different logistic regression models . Pearson's correlation coefficients of standardized scores of expression level for pairs of genes were reported . All p-values were two-sided , and p-values<0 . 05 were considered to be statistically significant . Data analyses were conducted using R ( R Foundation for Statistical Computing , Vienna , Austria ) . Initial studies optimized conditions for these experiments; further characterization and modifications of previously published in vitro co-culture methodologies [25]–[26] were required to be most appropriate for the present purposes . Detailed information regarding data supporting the resulting modifications is provided in Supporting Information ( see Supporting Information , Text S1 , Figures S1–S3 , and Table S1 ) .
To determine whether the in vitro co-culture system may serve as a functional correlate of protection , we first identified a panel of vaccine candidates that provided different degrees of protection against lethal Francisella challenge in vivo . C57BL/6J mice were vaccinated ID with 104 LVS , with 104 of the opacity variants LVS-G or LVS-R , or with 108 -heat killed ( HK- ) LVS , and then challenged one month after vaccination with increasing lethal doses of LVS IP ( Figure 1 ) . All mice vaccinated with wild type LVS survived challenge with up to 5×105 LVS IP , and 75% survived challenge with the highest dose tested , 106 LVS IP . In contrast , mice vaccinated with LVS-G exhibited 90% survival following challenge with 5×105 CFU and 30% following challenge with 1×106 CFU . Mice vaccinated with LVS-R exhibited only 20% survival following challenge with 5×105 CFU , and none survived challenge with 106 CFU . Finally , vaccination with HK-LVS failed to protect against challenge with 5×104 CFU or higher . In later experiments , a single challenge dose of 5×105 - 106 LVS IP was chosen as appropriate for discriminating between the degree of protection provided by all vaccines in the panel . These data indicated that this panel of candidate vaccines exhibited a hierarchy of relative protection against in vivo lethal LVS challenge , such that LVS>LVS-G>LVS-R>HK-LVS>PBS ( naive control ) . We next examined the protective capacity of these vaccines against lethal parenteral challenge with a selected dose of fully virulent Type A F . tularensis ( SchuS4 ) . Similar to the outcome using LVS challenge , and consistent with previous reports [32] , approximately 30% of mice vaccinated with LVS survived this dose of SchuS4 challenge and time to death of those that died was greatly extended , while vaccination with LVS-G protected only 10% of mice against lethal SchuS4 challenge ( Figure 2 ) . In contrast to challenge with LVS , vaccination with LVS-R did not provide detectable protection against this dose of SchuS4 challenge , and vaccination with HK-LVS only slightly extended time to death . Nonetheless , for this group of vaccines , a similar hierarchy of protective efficacy found following challenge with wild type LVS was also found using challenge with fully virulent Francisella . In parallel with in vivo vaccination and challenge studies , the activities of splenocytes obtained from mice vaccinated with LVS , LVS-G , LVS-R , HK-LVS , or PBS ( control ) were compared . To determine the relative effectiveness of each type of primed cells , decreasing numbers of splenocytes were added to a constant number of LVS-infected macrophages . As seen in Figure 3A , on a per-cell basis , cells obtained from LVS-infected mice were most effective in controlling the intramacrophage growth of LVS; those from LVS-G vaccinated mice were less effective , and those from LVS-R-vaccinated mice the least effective . The relationship between relative control by cells from the different groups was then investigated . A linear regression with indicators of different vaccine groups and the cell concentration as covariates was used to compare log CFU of recovered bacteria in different vaccine groups , adjusting for the levels of cell concentrations ( Figure S4 ) . The result of regression analysis demonstrated that , for any fixed cell concentration , cells from LVS-G-vaccinated mice were significantly less effective in controlling bacteria growth , a difference of about 0 . 95 log , compared to those from LVS-vaccinated mice ( p<0 . 001 ) . Similarly , cells from LVS-R-vaccinated mice were about 1 . 57 log less effective than cells from LVS-vaccinated mice ( p<0 . 001 ) . Finally , cells from LVS-R-vaccinated mice were 0 . 62 logs less effective than from those from LVS-G-vaccinated mice ( p<0 . 001 ) . As seen previously [25]–[27] , [29]–[31] , the results suggested that co-cultures containing cells from naive mice exhibited minimal and inconsistent reductions in bacterial numbers compared to those with LVS-infected macrophages alone ( e . g . , Figure 3A , p = 0 . 02; Figure 3B , p = 0 . 35 ) ; further , there was no significant relationship between the concentration of naive cells and bacterial numbers . Finally , cells obtained from mice vaccinated with HK-LVS did not significantly inhibit bacterial growth control compared to either naive cells or cultures with LVS-infected macrophages only , even at the highest cell numbers tested ( Figure 3B ) . Thus , the hierarchy of in vitro activities of cells from vaccinated mice was again LVS>LVS-G>LVS-R>HK-LVS . Further , because only LVS-immune T cells are active in this setting , these data estimate the relative frequencies of memory T cells . Supernatants and cells were also recovered on day 2 from each type of co-culture . Supernatants were then analyzed as above for cytokine production by ELISA and NO production by Griess reaction; cells were characterized by flow cytometry; and mRNA prepared from recovered cells was analyzed for relative gene expression . The amounts of TNF-α , IFN-γ , and NO produced were consistent with previously published studies using LVS vaccination alone [25]–[27] , [29]–[31] . Here , relative cytokine and NO production exhibited the same pattern as that observed in the survival studies and in in vitro control of intramacrophage LVS replication , such that LVS>LVS-G>LVS-R>HK-LVS and naive groups ( data not shown ) . Flow cytometry analyses of recovered cells confirmed the relative enrichment of T cells ( similar to that illustrated in Table S1 ) , and did not reveal any obvious differences between vaccine groups ( data not shown ) . Collectively , these data indicated that the hierarchy of protective capacity engendered by in vivo vaccination with this panel of vaccines was faithfully reflected by the relative ability of each type of Francisella-immune splenocytes to persist in culture , produce relevant cytokines and nitric oxide , and ultimately to effect control of intramacrophage bacterial growth . Because these in vitro co-culture conditions reliably detected differences in vaccine quality , non-adherent immune splenocytes from all groups were recovered on day two from each co-culture , and then analyzed in detail for relative gene expression . For these experiments , groups of mice were vaccinated with LVS , LVS-G , LVS-R and HK-LVS . At either 6 weeks or 12 weeks after vaccination , some mice were challenged in vivo , and other mice were sacrificed at the same time to prepare splenocytes , perform in vitro co-cultures , and recover non-adherent cells for mRNA analyses . Similar to initial studies using cells from naive or LVS-vaccinated mice only ( see Supporting Information text ) , the relative mRNA expression of genes of immunologic interest in splenocytes from mice vaccinated with LVS , LVS-G , LVS-R , or HK-LVS was compared to that of splenocytes from naive mice by RT-PCR , using commercially available arrays that included immunologically-related genes ( e . g . , Profiler PCR Th1-Th2-Th3 array ) . Data generated from initial experiments using the complete vaccine panel revealed that some genes , such as GF1 and CCR4 , which in initial studies were up-regulated in LVS-primed cells compared to naive cells , were either inconsistent or up-regulated to a similar degree for all vaccines and did not exhibit a differential pattern ( Table S2 , “SYBR” ) . In contrast , other genes appeared to exhibit a range of expression that reflected the relative effectiveness of vaccination . For example , IFN-γ appeared to be highly expressed in LVS-primed cells as well as in cells from LVS-G-vaccinated mice , only moderately expressed in cells from LVS-R-vaccinated mice , and expressed very little in cells from HK-LVS-vaccinated ( all compared to naive mice ) . Similar to IFN-γ , the relative expression of several other genes , such as IL-6 , TNF-α , IL-18bp , and GM-CSF , reflected the relative level of both in vivo protection and in vitro bacterial growth control activities of the different vaccines . The initial comparisons focused attention on a group of 15 genes with apparently differential expression patterns , either in terms of relative up-regulation or relative down-regulation . Seven other genes were also considered of ongoing interest , either because they were not included on the commercial panel used and had a known biological relationship to other genes that were correlated with protection ( e . g . , IL-22 ) , and/or because results were ambiguous ( e . g . , IL-17A and IL-13 ) . A set of 22 genes were therefore selected for more detailed studies , and separate primer-probe sets prepared to perform qRT-PCR analyses . This approach was applied to again analyze mRNA from previous experiments , as well as mRNA from additional independent experiments . Collectively , these included two independent experiments using splenocytes from mice vaccinated six weeks earlier ( Table S2 , experiments 4 and 6 ) and two independent experiments using mice vaccinated twelve weeks earlier ( Table S2 , experiments 5 and 7 ) . For analysis of qRT-PCR data from selected genes , Ct results were normalized to a standard curve of GAPDH before calculation of ΔCt and fold changes ( ΔΔCt ) between samples from naive and vaccinated mice ( ΔΔCt ) . When compared for up-regulated genes , the fold change for most of these genes in cells from LVS-vaccinated mice was similar to those in cells from LVS-G-vaccinated mice; both were greater than those in cells from LVS-R vaccinated mice , which in turn were greater than those in cells from HK-LVS vaccinated mice or naive mice ( Figure 4 , panels A–C ) . In contrast , when the relative fold change ( ΔΔCt ) of putatively down-regulated genes was compared across different experiments , the amounts of differences were relatively modest ( Figure 4 , panel D ) , similar to observations made using Profiler PCR arrays . Taken together , using a different detection system ( including probes instead of SYBR Green ) and a different normalization approach , we again found clear relationships between the relative expression levels of most members of this panel of up-regulated genes and the hierarchy of vaccine protection . Further , the latter method allowed more accurate quantification of relative gene expression . To examine whether gene expression patterns observed are specific for F . tularensis LVS vaccination and related to Francisella vaccine efficacy , C57BL/6J mice were vaccinated ID with LVS , LVS-R , HK-LVS , or 5×103 Listeria monocytogenes . Two weeks later , mice were either challenged with lethal dose of 106 LVS IP , or used to prepare spleen cells that were co-cultured with LVS-infected macrophages , recovered after two days , and analyzed for relative gene expression . For the LVS related vaccines , the same patterns of survival and in vitro growth control were found ( data not shown ) . In contrast , Listeria-vaccinated splenocytes did not significantly reduce the intramacrophage growth of LVS compared to control co-cultures ( data not shown; and see [25] ) , and Listeria-vaccinated splenocytes exhibited an absence of up- or down-regulation . The fold change ( ΔΔCt ) values using Listeria-vaccinated splenocytes were mostly similar to those observed in cells from mice vaccinated with HK-LVS . For example , IFN-γ up-regulation in cells from LVS-vaccinated mice was about 22 fold compared to naive mice; 7 . 2 in cells from LVS-R-vaccinated mice; 0 . 8 in cells from HK-LVS vaccinated mice; and 1 . 7 in cells from Listeria-vaccinated mice . Thus , the working panel of genes specifically reflected activities of Francisella-immune cells . The resulting data derived from both the Profiler arrays and from the selected genes assessed by qRT-PCR were used to examine statistical correlations between survival and in vitro gene expression in response to the different vaccines ( see Table S2 , all experiments ) . For each gene , the proportion of surviving mice was plotted against a standardized score of gene expression . Examples of these relationships are illustrated in Figure 5 , in which analyses of IFN-γ , TNF-α , IL-6 , and T-bet relative mRNA expression by qRT-PCR are shown; p values for the relationship between survival and all genes analyzed by Profiler array or qRT-PCR are provided as Supporting Information ( Tables S3 and S4 , respectively ) . Of the 22 selected genes , 20 were significantly related to survival according to Profiler array data , validating the initial selection , but only 16 , were significantly related to survival using qRT-PCR data . The logistic regressions analyses using data from the Profiler arrays ( Table S2 , SYBR ) indicated that about 20 other genes , in addition to the 22 genes already selected , were also significantly related to survival . These included genes that were up-regulated ( e . g . , SOCS3 and Tmed ) and some that were down-regulated ( e . g . , Jak1 and CD27 ) . However , the fold change of each of these genes even for LVS-immune cells compared to naive cells was relatively small , <2 for up-regulated genes and >0 . 5 for down-regulated genes . Moreover , the range of differences for these genes across all vaccines was judged too small to be reliably useful in this context , and these candidates have not yet been pursued further . Taken together , the genes that exhibited consistent changes of useful magnitude across all experiments , as well as significant pairwise correlations of the relative degree of expression between all possible pairs of genes ( Table S5 ) , included IFN-γ , IL-6 , IL-12rβ2 , T-bet ( Tbx21 ) , Socs1 , and IL18bp ( Table 1 , Group 1; Figure 4 , panel A ) . Genes with notable , but less universal , associations were GM-CSF , IL-27 , TNF-α , IL-27 , and Irf1 ( Table 1 , Group 2; Figure 4 , panel B; remaining up-regulated genes are illustrated in Figure 4 , panel C , and down-regulated genes in Figure 4 , panel D ) . Finally , to assess whether fold changes in mRNA expression for different genes would work in concert and better predict survival than single genes , multivariate logistic regression analyses were performed . Although specific pairs of genes displayed correlative degrees of expression , multivariate analyses of gene pairs ( Table S6 ) did not provide stronger associations with survival than any one gene alone , based on the Akaike information criterion value . Likewise , groups of three genes did not provide any statistically significant improvement in survival prediction than did the models with one or two genes ( data not shown ) .
Currently there are no validated options for predicting protection against intracellular pathogens . Human clinical field trials for many intracellular pathogens will be difficult , either because of the long time to develop disease ( i . e . , tuberculosis ) , or the sporadic nature of disease in nature ( i . e . , tularemia ) . A recent FDA regulation provides an option for evaluating vaccine efficacy using animal studies under special , well-defined circumstances that may most likely be applicable to biodefense pathogens [33]; but a rational means of bridging efficacy between animals and humans and extrapolating vaccine dose will be critical . Many such issues could potentially be addressed by derivation of correlates of protection that can be measured in several species . The word “correlate” has been ascribed a wide variety of definitions [1] , [34]; here , we use the term to refer to a measurement that detects relevant biological functions critical for , and statistically related , to protection against an infectious disease [35] . Historically , efforts to identify and measure relevant serum antibodies have failed to successfully predict vaccine-induced protection , particularly for replicating , live attenuated vaccine . Another approach is to relate the quantity of an immune parameter to the degree of protection . Production of IFN-γ ex vivo has been extensively explored , particularly in studies of M . tuberculosis . However , there are many human clinical and experimental examples where the relative levels of IFN-γ measured do not reflect the degree of successful vaccination [36]–[41] . The collective evidence instead indicates that it is likely that local availability of IFN-γ is necessary , but not sufficient , for protection . More recently , “multi-functional T cells” that exhibit the ability to simultaneously produce IFN-γ , TNF-α , and IL-2 , have been proposed as vaccine correlates [42]–[43] . While promising in the context of mouse models of Leishmania infections [44] and some studies of tuberculosis vaccines [45]–[47] , in other cases no obvious correlation has been detected between MFCs and protection [48]–[51] . Efforts to develop genomic and metabolomic biomarker signatures for tuberculosis infection and vaccination , particularly in the context of HIV/AIDS , are underway [52] . Although still complex , the approach illustrated here of coupling a panel of qualitatively different vaccines , combined with in vitro re-stimulation via co-cultures and semi-quantitative mRNA analyses , clearly identified mediators that correlate strongly with the relative degree of vaccine-induced protection against lethal challenge in vivo . The Francisella infection and immunity model offered the advantage of having a panel of different vaccine candidates , coupled with the ability to more precisely define the strength of protection in vivo by using a range of lethal challenge doses . Vaccination of humans with LVS engenders production of Francisella-specific serum antibodies , as well as memory T cells in peripheral blood that produce IFN-γ , IL-17A , and IL-22 following antigen stimulation [53]–[56] , but these are currently of unknown contribution to protection of people . To date , there are limited studies regarding correlates of immunity to Francisella . Vaccination mice with static Francisella vaccine candidates , including outer membrane protein preparations or ethanol-inactivated LVS formulated with Freund's adjuvant , provided partial protection against respiratory challenge with 40 CFU of fully virulent type A F . tularensis , accompanied by production of serum antibodies and large levels of TNF-α and IL-2 in sera of vaccinated mice after challenge [57] . In studies using a mouse model to compare intradermal vaccination of mice with LVS to vaccination with genetically attenuated mutants of SchuS4 , protection against challenge with fully virulent F . tularensis was not correlated with levels of serum IFN-γ or IgM/IgG antibodies [58]; only pulmonary IL-17 quantities after secondary challenge appeared to track with protection [59] . Here , LVS-G and LVS-R , spontaneous variants that express alternate LPS chemotypes , proved to provide intermediate levels of in vivo protection ( Figure 1 ) . Because antibodies to LPS likely play a minor role in protection against lethal Francisella challenge even in mice [60]–[61] , the reduced protection is unlikely to be explained completely by reduced serological responses . More likely , reduced protection is explained either by reduced persistence and total antigen exposure in these serum-sensitive variants [18]; or , changes in LPS expression are a visible marker for simultaneous changes in expression of other bacterial genes that are important in protection . Of note , it is likely the mechanisms of protection provided by the live attenuated strains LVS , LVS-G , and LVS-R are similar , but the strengths of protection quantitatively different; we consider this an important feature that is critical to permitting strong interpretations across different vaccines . Using this panel , a hierarchy of strength of protection was evident using Francisella LVS challenge of mice ( Figure 1 ) . Although it was not feasible to perform larger experiments using graded doses of challenge with fully virulent F . tularensis SchuS4 , the proportion of vaccinated survivors and differences in times to death following challenge with one selected dose supported a similar hierarchy ( Figure 2 ) . Tangentially , these comparisons also suggest that lethal parenteral challenge of vaccinated mice with LVS could serve as an informative screen for vaccine efficacy prior to testing by challenge with fully virulent Type A F . tularensis . Using carefully selected conditions , we then compared the relative activity of splenocytes from differentially vaccinated mice in an in vitro tissue culture system that measures reduction of intramacrophage bacterial numbers by immune T cells , and found that the relative strength of in vivo protection was clearly reflected by the relative activity of immune splenocytes in vitro ( Figure 3 ) . The results therefore support the utility of the co-culture assay as both a relevant functional assay in its own right . Further , despite multiple attempts to identify strong T cell antigens involved in murine responses to Francisella and develop associated reagents [62]–[64] , tools such as tetramers remain lacking . Studies in mice and humans suggest that host responses do not involve a classical “immunodominant” protein but are directed to a large collection of protein antigens [65]–[67]; thus although tetramer analyses may no doubt eventually prove helpful , such approaches may detect only a small fraction of the total anti-Francisella T cell response . Because only LVS-immune are active in specifically controlling intramacrophage growth of the homologous bacteria [25]–[26] , the results presented here validate that the in vitro co-culture assay is a new , and currently the only available , approach to establish the relative frequency of Francisella-specific memory T cells in a mixed population ( Figure 3 , Figure S4 ) . The in vitro co-culture assay was previously developed using both Francisella LVS and M . tuberculosis as a research model to explore mechanisms of interactions between infected host macrophages and immune T lymphocytes . In many respects , this system faithfully reflects known in vivo T cell effector mechanisms , including both IFN-γ-dependent and non-IFN-dependent mechanisms [25]–[27] , [29]–[31] . However , it should be noted that studies demonstrated that T cells from LVS-vaccinated IL-12 knockout mice are quite active in co-cultures , despite the fact that IL-12 knockout mice do not clear a vaccinating LVS infection [31] . Elsewhere , this co-culture approach was recently applied by our colleagues to murine studies of a panel of vaccine candidates for M . tuberculosis [68]–[69] . In those studies , the in vitro assay successfully discriminated between vaccines with high or moderate activity , as defined by in vivo protection . Although we find the in vitro co-culture approach promising , as well as potentially feasible in the near term , cell-based assays are difficult to implement for human clinical trials . We therefore pursued an additional strategy , by searching for genes whose differential expression was related to the hierarchy of vaccine-induced in vivo protection and in vitro cellular activity . We focused on screening immunologically-related genes . This approach is obviously biased toward analyzing known entities instead of discovering new ones , but it offered the potential advantage of identifying relevant mediators . Remarkably , cells recovered from in vitro culture differentially expressed mediators at the mRNA level ( Figure 4; Figure 5; Table S2 ) and a number of candidates emerged ( Figure 4; Table 1 ) . Of note , among these mediators , were IFN-γ and TNF-α , as might be expected , and thus validating the overall approach . It should be noted that we observed considerable variability in mRNA levels between experiments . For example , expression of IFN-γ was always highly up-regulated in cells obtained from mice vaccinated with LVS and LVS-G , but the fold change compared to naive cells varied between about 13 and 360 ( Table 1; Table S2 ) . We suspect that biological , in addition to technical , reasons contribute to the observed variability . To increase confidence in predictors , quantifying a panel of genes is therefore likely to be preferable over assessing a single gene mediator , even an important one such as IFN-γ . This point may be especially germane to clinical settings that lack the advantages offered by using genetically identical inbred mice . Despite the quantitative variability , from a group of about 84 genes , 16 proved to be robust enough to yield significant correlations between the magnitude of mRNA expression and survival , as well as exhibit relatively large differences in the degree of expression ( Figure 5; Table 1; Figure S3; Tables S3 – S6 ) . These likely include those whose gene products contribute directly to mechanisms , and those that are co-regulated and only epiphenomena . We are most interested in those that are mechanistically relevant , and thus likely to serve as definitive predictors across variables such as time after vaccination , route , dose , tissues sampled , and especially different animal species . Notably , the most useful of the expression differences involved up-regulated genes , which is appealing in potentially reflecting a requirement for production of a mediator to provide a particular function during challenge . It is striking that several of the leading candidates are plausibly related to Th1 cell biology , including T-bet and IFN-γ . Although TNF-α also exhibited significant differential regulation and is clearly relevant , production of TNF-α is tightly regulated to avoid toxicity , and thus ex vivo measurements may not be among the most useful . IL-12rβ2 is only found as part of the complete receptor for IL-12 p70 , which is not expressed on resting T cells but induced by T cell activation and contributes directly to Th1 lineage commitment [70] . For example , in naive transgenic CD4+ T cells , IFN-γ stimulation up-regulates expression of T-bet in a STAT-1 dependent manner and promotes IL-12Rβ2 chain expression [71] . Notably , STAT-1 was also among our candidate genes with significant associations , albeit one that exhibited considerable variability and thus was not included in our two highest priority groups ( Table 1 ) . In contrast to IFN-γ , T-bet , and IL-12rβ2 , other high priority candidates such as IL-6 , SOCS-1 , and IL-18bp were more surprising . IL-6 has a wide variety of sources and functions , but in adaptive immunity is most commonly associated with promoting B cell activation and IgA production and infrequently with resistance to intracellular pathogens [72] . In the Francisella infection model , our preliminary results indicate that IL-6 knockout mice are severely impaired in their ability to survive primary LVS vaccination ( Kurtz and Elkins , manuscript in preparation ) , as are T-bet knockout mice and IL-12rβ2 knockout mice ( Melillo and Elkins , manuscript in preparation ) . The specific contribution of SOCS-1 , an important member of a large family of “suppressor of cytokine signaling” mediators that regulate T cell differentiation as well as T cell effector functions , awaits further study . Perhaps the most unexpected candidate is IL-18 binding protein ( IL-18bp ) ; although induced by IFN-γ , its production is usually associated with cells other than leukocytes [73] , and to our knowledge has no reported direct link to Th1 T cell effector functions . Taken together , the results presented here are in important step toward the identification of T cell functions and products required for survival of lethal exposure of intracellular bacteria . We propose that the candidates described as Group 1 and Group 2 ( Table 1 ) receive high priority for detailed direct exploration , initially in animal studies , of biological relevance and mechanistic contribution . Knowledge obtained by in vitro and pre clinical studies will be the key to facilitating design of assays with formats amenable to clinical studies , such as ex vivo re-stimulation of human peripheral blood leukocytes . It is likely that defining groups of mediators will be preferable in order to overcome issues related to variability in measurements , and ensure predictive confidence for human clinical trials . The larger goal will therefore be to establish protective levels of each individual mediator , and thus select combinations that reliably predict successful vaccination against intracellular pathogens . | Diseases such as tuberculosis ( caused by Mycobacterium tuberculosis ) or tularemia ( caused by Francisella tularensis ) result from infections by microbes that live within cells of a person's body . New vaccines are being developed against such intracellular pathogens , but some will be difficult to test , because disease takes a long time to develop ( e . g . , tuberculosis ) or because outbreaks are unpredictable ( e . g . , tularemia ) . Usually such infections are controlled by activities of T cells . However , there are no accepted measures of T cell function that reliably predict vaccine-induced protection . We studied two new ways to do so . We used a group of vaccine candidates against tularemia that stimulated good , fair , or poor protection of mice against Francisella challenge . We then measured whether Francisella–immune cells from vaccinated mice controlled the growth of bacteria inside cells , and/or whether the expression of immune genes in Francisella–immune cells was increased . We found that the degree of protection was matched by the degree of the cells' function in controlling intramacrophage bacterial growth . Further , the degree was predicted by relative amounts of gene expression for several immune mediators . Thus the two new options explored here may help predict protection , without waiting for the onset of disease . | [
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] | 2012 | Development of Functional and Molecular Correlates of Vaccine-Induced Protection for a Model Intracellular Pathogen, F. tularensis LVS |
Herpes viruses persist in the infected host and are transmitted between hosts in the presence of a fully functional humoral immune response , suggesting that they can evade neutralization by antiviral antibodies . Human cytomegalovirus ( HCMV ) encodes a number of polymorphic highly glycosylated virion glycoproteins ( g ) , including the essential envelope glycoprotein , gN . We have tested the hypothesis that glycosylation of gN contributes to resistance of the virus to neutralizing antibodies . Recombinant viruses carrying deletions in serine/threonine rich sequences within the glycosylated surface domain of gN were constructed in the genetic background of HCMV strain AD169 . The deletions had no influence on the formation of the gM/gN complex and in vitro replication of the respective viruses compared to the parent virus . The gN-truncated viruses were significantly more susceptible to neutralization by a gN-specific monoclonal antibody and in addition by a number of gB- and gH-specific monoclonal antibodies . Sera from individuals previously infected with HCMV also more efficiently neutralized gN-truncated viruses . Immunization of mice with viruses that expressed the truncated forms of gN resulted in significantly higher serum neutralizing antibody titers against the homologous strain that was accompanied by increased antibody titers against known neutralizing epitopes on gB and gH . Importantly , neutralization activity of sera from animals immunized with gN-truncated virus did not exhibit enhanced neutralizing activity against the parental wild type virus carrying the fully glycosylated wild type gN . Our results indicate that the extensive glycosylation of gN could represent a potentially important mechanism by which HCMV neutralization by a number of different antibody reactivities can be inhibited .
Cytomegaloviruses ( CMV ) have co-evolved with their respective hosts . During this long and continuing co-evolution these viruses have adapted to the host defense systems and vice versa to allow the life-long persistence of these viruses . As a result , infections in immunocompetent hosts are generally asymptomatic and a life-long persistent/latent infection is readily established . Development of symptoms or disease is prevented by a multilayered , in large parts redundant , innate as well as adaptive immune response [1] . Persistence and transmission between hosts eventually requires the evasion of immune control . Multiple mechanisms that permit evasion of immune control by the innate and adaptive cellular immune responses have been extensively documented [1]–[3] . In contrast , very little is known about mechanisms by which CMV can evade humoral immune responses that presumably consist of antiviral antibodies that potentially neutralize free virus or destroy infected cells via antibody mediated cytotoxicity . Since viral transmission between hosts in a community setting is thought to occur via cell free virus in most cases that have been studied , evading virus neutralizing antibodies is essential for successful spread and persistence of CMVs in the population . On the population level , the extensive strain polymorphism that has been documented in different human and animal CMVs could serve as an immune evasion strategy [4]–[6] . CMV strains exhibiting antigenic and genetic variability are capable of super-infecting immune hosts and can be readily transmitted between immune individuals [7] , [8] . Transmission of super-infecting strains has been well documented during pregnancy or following organ transplantation [9] , [10] . Strain-specific virus neutralization is potentially a contributing factor to this phenomenon and strain-specific neutralization has been observed in a number of studies [11] , [12] . Thus , virus strain-polymorphism can be considered as a mechanism that permits successful maintenance of CMV within the host population . To evade neutralizing antibodies on the level of an individual viral strain within a single host requires an evasion strategy other than virus strain-polymorphism . The development of virus mutants during virus persistence in an individual host that resist virus neutralizing antibodies is , based on existing data , only a theoretical possibility . Herpes viruses in general are believed to be genetically stable secondary to the proofreading activity of their DNA polymerase . Moreover , the neutralization of CMV in vivo almost certainly involves antibodies directed at a multitude of different viral antigens . Thus , multiple mutations would be required for evasion of virus neutralizing activities that are likely to be present in a single individual host . Induction of neutralizing and non-neutralizing antibodies which compete for binding to the same antigenic determinant has been described as one possible mechanism for evasion of neutralization but this has been shown only for a single antigenic site on glycoprotein ( g ) B [13] , [14] . Another possibility for viral evasion of neutralizing antibodies is the addition of carbohydrate to virion envelope glycoproteins that can alter antibody binding , a mechanism that has been extensively documented for viruses such as HIV and influenza , among others [15] . Human CMV ( HCMV ) is a structurally complex virus which contains a large number of envelope glycoproteins , several of which are predicted to be extensively glycosylated . One such extensively glycosylated envelope proteins is gN , a type I glycoprotein that is particularly interesting for several reasons . It is component of the gM/gN complex which is among the few envelope proteins that are conserved between the herpes viruses indicating an important function of this glycoprotein complex in the biology of herpes viruses [16] . For HCMV , gN is essential for virus replication whereas for alpha-herpes viruses it has been classified as non-essential [17]–[20] . In contrast to alpha-herpesviruses , the gM/gN complex is the most abundant protein complex in the HCMV virion envelope [21] . gN is an extremely polymorphic protein at the amino acid ( aa ) level . So far four major genotypes have been identified [22] . Differences between the gN genotypes are exclusively located within the surface domain of the protein , reaching amino acid differences of up to 50% between genotypes [22] . The protein is extensively modified by O-linked sugars contributing over 40 kDa of mass to the 15 kDa polypeptide backbone [23] . Despite the enormous amino acid variation in the surface domain of gN , the total number of serine ( ser ) and threonine ( thr ) residues remains constant at approximately 50 . The conservation of the number of potential glycosylation sites in the face of significant primary sequence variation of non-ser/thr residues in the surface domain of this molecule suggests a strong selective pressure to maintain this precise level of glycan density . In contrast to gN from HCMV , which has a primary sequence of 138 aas , the gN proteins from alpha- and gamma-herpes viruses without exception are smaller molecules of approximately 100 aa that are not predicted to be extensively glycosylated . Experimental data have confirmed the prediction in those cases where the proteins have been studied [18] , [24]–[26] . Even within the cytomegalovirus family , gN homologous proteins of the most species are predicted to represent small proteins with limited modifications [27] , [28] . The extensive glycosylation of CMV gNs seems to be restricted to viruses derived from the great apes and humans since gN from chimpanzee CMV is predicted to contain a comparable number of O-glycosylation sites to HCMV while gN from rhesus and cynomolgus CMV are short , largely unmodified proteins [29]–[31] . The function ( s ) of the carbohydrate moieties of gN is largely unknown . The gCII complex , which has been shown to consist of gM and gN , has previously been proposed to be involved in the initial interaction process between the target cells and the virus since it was reported to bind to heparin [32] . Cell-surface heparan sulfate proteoglycans are thought to represent the initial molecules used by the virus to adhere to target cells [33] . With respect to the humoral immune response , gN has been identified as a target of neutralizing antibodies [34] . In fact , in human serum the capacity to neutralize infectious virus in vitro is comparable between anti-gN and anti-gB antibodies [34] . Moreover , exchanging the gN-genotypes in a single , genetically homogeneous HCMV strain resulted in strain-specific neutralization by human convalescent sera further emphasizing the importance of gN for the humoral immune response [12] . We hypothesized that the extensive glycosylation of HCMV gN could provide the virus with a mechanism to evade neutralization by antibodies . To test this hypothesis we generated gN-recombinant viruses with reduced carbohydrate modification . Our results indicated that under-glycosylation of gN increased the susceptibility to the neutralizing activity of antibodies directed at gN . Unexpectedly , we also demonstrated that recombinant viruses with under-glycosylated gN were more susceptible to antibodies directed against a number of different virion envelope proteins of HCMV that have been shown to be major targets of the neutralizing antibody response . Together these findings suggest that one function of the extensive glycosylation of gN could be to limit the activity of virus neutralizing antibodies directed at different envelope glycoproteins , a function similar to that of carbohydrates that serve as a glycan shield to limit antibody neutralization of RNA viruses .
More than 50% of the amino acids within the surface domain of gN are ser or thr residues that can serve as substrate for the addition of O-linked sugars . The finding that the viral protein migrates in SDS-PAGE as a 50–60 kDa diffuse species while the theoretical molecular mass of gN is 15 kDa indicating that a significant number of the potential glycosylation sites are utilized [23] . The consequences of this extensive glycosylation to the function of gN and ultimately in the biology of HCMV are unknown . Because site specific mutagenesis of ser and thr residues ( total of 36 in HCMV strain AD169 ) individually and in combination in the surface domain of gN would represent an experiment of considerable complexity , we estimated the impact of reduced glycosylation of gN to the formation of the essential gM/gN complex using mutants constructed by deletion of stretches of ser or thr rich areas of the surface domain of gN . Expression plasmids were constructed that upon transfection into mammalian cells would give rise to truncated gN proteins that could be studied following transient expression . Deletion of aa 24–40 were made to yield plasmid gN-41sig , aa 24–60 and aa 24–89 to yield gN-61sig and gN-90sig , respectively ( Figure 1 ) . All plasmids were constructed as to maintain the authentic gN signal sequence which is predicted to be located between aa 1–21 . To facilitate protein detection , all proteins were expressed with a myc-epitope at the carboxyl terminus . The plasmids were individually co-transfected with a full length wild type gM encoding plasmid into Cos7 cells and complex formation was analyzed using the gN-specific monoclonal antibody ( mab ) 14-16A . This antibody has previously been shown to be specific for gN that is complexed with gM [23] . This mab does not react with gN that is not complexed with gM , thus providing an assay for the maintenance of sufficient structure of gN to allow complex formation with gM [23] . Reactivity with mab 14-16A was seen in cells transfected with gM combined with gN and gN-41sig as well as the localization of the protein in the TGN ( trans-Golgi network ) , findings that indicated that a complex was formed between these two proteins and that the trafficking of the complex within the cell was similar to the wild type gM/gN complex ( Figure 2 ) . No reactivity with mab 14-16A was detected following co-transfection of gM plus gN-61sig , perhaps secondary to a loss of the epitope recognized by mab 14-16A . However , when the cells were stained with an anti-myc antibody we observed reactivity that co-localized the myc-tagged gN with markers for the TGN , indicating formation and correct transport of the gM/gN-61sig complex . Isolated expression of gN results in compact intracellular aggregation of the protein in structures containing endoplasmic reticulum markers and defective transport to the TGN [23] . A similar intracellular traffic defect was observed for the gN-truncated proteins and gN-41sig is shown as example ( Figure 2 ) . Transfection using gM combined with the gN-90sig plasmid did not result in a protein that could be detected by immunofluorescence with either antibody suggesting that this deletion resulted in loss of protein structure required for complex formation with gM ( data not shown ) . Together these data demonstrated that deletion of stretches of ser/thr rich sequences of gN could be accomplished without loss of structure required for complex formation with gM and trafficking of this complex to the TGN in transfected cells . To generate recombinant viruses expressing truncated gN proteins , we followed our previous strategy , i . e . construction of HCMV bacterial artificial chromosomes ( BAC ) carrying the truncated versions of the gN-gene in place of the full length gene , followed by reconstitution of infectious virus in human cells [19] . All viruses were constructed in the genetic background of HCMV strain AD169 [35] . Replicating virus was recovered for gN versions starting at aa 41 and aa 61 giving rise to RVgN-41sig and RVgN-61sig , respectively . In several attempts no replicating virus could be recovered from BACs carrying the gN-90sig mutation , indicating that this large deletion in gN is lethal , a finding that confirmed our previous results of the essential role of gN for replication of HCMV [36] . RVgN-41sig and RVgN-61sig recombinant viruses replicated with similar efficiency when compared to the parental virus RVAD169 , a finding that was consistent with the capacity of these two gN mutants to form a complex with gM and traffic normally in transfected cells . ( Figure 3A ) . In accordance with the similar efficiency of replication of the gN-truncated viruses , we observed no delay in expression of immediate early proteins as determined by indirect fluorescence arguing that the early infection events are similar for the three viruses ( data not shown ) . To determine if gM/gN complex formation occurred in cells infected with the respective viruses , we carried out indirect immunofluorescence analysis 5 days after infection ( Figure 3B ) . In cells infected with the wild type RVAD169 virus , the gN signal colocalized with gB in a region in close proximity to the nucleus which has been termed the assembly compartment ( AC ) [37] . In cells infected with RVgN-41sig a similar staining pattern was observed , indicating that the truncated form of gN did not influence the trafficking of the gM/gN complex to the AC . As was demonstrated in transfected cells , expression of the gN-61sig protein could not be detected using the mab 14-16A . However , co-localization of gM and gB in cells infected with RVgN-61sig could be detected and because gM complex formation with gN has been shown to be required for transport of gM from the ER , this finding indicated that the gM/gN-61sig complex had been transported properly to the AC [23] . ( Figure 3B ) . Co-localization of gN-61sig and gB was also observed when gN-61sig was detected via the myc epitope present on gN-61sig ( Figure 3B ) . To analyze incorporation of the different gM/gN complexes into extracellular virus particles , we gradient purified the respective viruses using ultracentrifugation through glycerol-tartrate gradients and analyzed the viral lysates for the presence of the gM/gN complex by western blot . When virion lysates were analyzed under reducing conditions we did not detect differences in the ratio of the major capsid protein ( MCP ) and gB between the viruses ( Figure 3C ) . The gM/gN complex was analyzed under non-reducing conditions since gN migrates as a smear under reducing conditions preventing an accurate estimation of the amount of protein [23] . To detect gN complexes present in all three recombinant viruses we used a gM/gN-specific polyclonal human serum , that was affinity purified from a HCMV hyperimmune globulin preparation [34] . It was shown to be monospecific for gM/gN [34] . The amount of protein that was applied to the analysis was adjusted to give a comparable gB-specific signal for all three viruses ( Figure 3C ) . The results demonstrated that the RVAD169 and RVgN-41sig contained similar amounts of the respective gM/gN complex . For RVgN-61sig , the amount of gM/gN complex was more difficult to estimate due to the diffuse migration of the complex but appeared similar to the other two viruses . The explanation for the diffuse migration of the gM/gN-61sig complex is unknown but a plausible explanation is increased structural heterogeneity of the remaining carbohydrate modifications secondary to loss of a significant number of potential O-linked glycosylation sites . The presence of a similar gM/gN to gB ratio was also confirmed by western blot analyses for RVAD169 and RVgN-41sig using mab 14-16A ( data not shown ) . To obtain more quantitative data on the different proteins in the respective virion particles we performed an ELISA using lysates from gradient purified virions as coating antigen . The ratio of MCP to the envelope proteins gB and gH was comparable for the three different recombinant viruses ( Figure 3D ) . Note that for this analysis an anti-gH mab was used that is dependent on the native conformation of the antigen , indicating that the lysis procedure left the proteins largely intact [38] . Virions from RVgN-61sig gave a reduced signal with mab 14-16A compared to RVAD169 and RVgN-41sig , confirming the results of the indirect fluorescence analysis . Together these data argue that deletion of stretches of ser/thr rich sequences within the surface of domain did not alter the function of the gM/gN complex required for the production of replication competent viruses . Furthermore , the stoichiometry of the three major glycoprotein components of the virion envelope appeared to be unaltered in virions produced by these recombinant viruses suggesting that the deletion of these sequences in the surface domain of gN did not alter the incorporation of the gM/gN complex ( or gB , gH ) into the envelope of the virus . The gM/gN complex of HCMV was originally designated gCII complex and it was reported that components of the gCII complex have heparin-binding capacity [32] . Heparin binding is most likely secondary to the carbohydrate modifications of gN since gM is minimally glycosylated and largely buried in the viral envelope . We therefore tested heparin for blocking infection of the gN-truncated viruses . In accordance with previous reports , we observed almost complete inhibition of infection in the presence of 2 µg/ml heparin [39] . Importantly , there was no difference between the three viruses in terms of their capacity to be inhibited by the addition of heparin ( Figure 3D ) . In summary , when combined these data indicated that the behavior of gN-truncated viruses in these in vitro assays were phenotypically very similar if not identical to the parental RVAD169 . Several possible functions have been suggested as explanations for the extensive carbohydrate modifications of gN including a potential role in cell binding , possibly as a result of its interactions with cell surface glycosaminoglycans and/or serving to limit accessibility of anti-gN antibodies that could neutralize infectious virus . We examined this latter possibility by using the gN truncation mutant viruses in antibody mediated virus neutralization assays . To specifically compare the impact of the loss of carbohydrate modifications and the loss of amino acid sequence on susceptibility of the mutant viruses to neutralization by antibodies , we included antibodies reactive with gN as well as other envelope glycoproteins , gB and gH in these assays . With the exception of 14-16A , an IgM antibody that neutralizes HCMV only in the presence of complement , all antibodies neutralize HCMV in the absence of complement . The gN-specific mab 14-16A showed increased capacity to neutralize RVgN-41sig . RVgN-61sig was not neutralized , which was consistent with the findings that gN-61sig was not recognized by this antibody ( Figure 4 ) . These results argued that the loss of carbohydrate and not the antibody recognition site in the RVgN-41sig virus encoded gN was responsible for increased susceptibility to neutralization by this antibody . Moreover , it could also be argued that that carbohydrate modifications on wild type gN functioned to limit the virus neutralizing function of mabs directed against gN . Unexpectedly and perhaps more importantly , both of the gN-truncated viruses were significantly more susceptible to neutralization by other , non-gN specific neutralizing mabs utilized in these assays . The effect was most pronounced for the gH-specific murine mab 14-4b ( gH1 ) and the human anti-gB mab ITC88 ( gB-AD2 ) , where differences in 50% neutralization titer of approximately 10-fold were detected . The human anti-gH mab MSL-109 ( gH2 ) and the human anti-gB mabs SM5-1 ( gB-AD4 ) , 1G2 ( gB-AD5 ) and C23 ( gB-AD2 ) showed less drastic differences in virus neutralizing activity between the parental and the gN-truncated viruses ( Figure 4 ) . Note that the gH-specific mabs and two of the gB-specific mabs ( SM5-1 , 1G2 ) depend on native antigen conformation for binding . When non-neutralizing mabs against gB or gH were tested , we found no increase in neutralization activities of these antibodies . The anti-gB mab 27–156 ( gB-AD1 ) is shown as an example . Because the biochemical composition of the envelope as measured by the amounts of three major glycoproteins in the virion envelope was unchanged in the mutant viruses that lacked wild type levels of glycosylation on gN , these results suggested that changes in carbohydrate content of a single envelope glycoprotein were responsible for the increased susceptibility of mutant virions to virus neutralizing antibodies directed at unrelated envelope proteins . These findings raised the possibility that the extensive carbohydrate modifications of gN could be functioning in a similar fashion as the glycan shield that has been proposed for other viruses , including HIV [40] , [41] . Sera from HCMV infected individuals contain virus neutralizing antibodies directed against a number of different envelope glycoproteins . The majority of antibodies has been suggested to be directed against gB , gH and gN when such sera are analyzed with laboratory strains of HCMV such as strain AD169 , which was used in this study [34] , [42] , [43] . To determine whether the effect observed when mabs were used to neutralize the gN-truncated viruses would be reflected in differences in virus neutralization by polyvalent human sera , we carried out neutralization assays with randomly selected sera from HCMV seropositive donors . A total of 11 specimens were tested and representative results are shown in Figure 5 . As could be expected , the polyvalent sera showed less marked differences in neutralization titer between parental virus and the gN-truncated versions than was observed when assays were carried out with single antigen/epitope specific mabs . Two sera showed a significant difference ( represented by serum 57 and 97 ) , and the remaining sera differences between 1 , 3 and 2 , 2 fold , which , however , did not reach statistical significance ( represented by serum ER ) . In addition , a commercial immunoglobulin preparation ( Sandoglobin ) , presumably derived from a large number of donors , also showed a higher neutralization titer against the viruses expressing the truncated forms of gN , although this difference did not reach significance . We have previously shown that neutralizing anti-gB antibodies do not prevent attachment of fully glycosylated virions to target cells making inhibition of attachment an unlikely mechanism responsible for the increased neutralizing sensitivity of the gN-truncated viruses to anti-gB antibodies [44] . However , the possibility existed that the removal of carbohydrates results in an altered steric orientation of the antibodies bound to the virion surface and thereby provide a new functional property that could alter virion attachment . Therefore , we tested attachment of the different viruses to fibroblast target cells in the presence of antibody . Virus/antibody mixtures were added to target cells at 4°C and the number of HCMV DNA copies attached to the cells was determined by quantitative real time PCR . Attachment of virions to fibroblasts was similar for RVAD169 and the gN-truncated viruses and was not influenced by addition of antibody ( Figure 6A ) . The slight increase of bound virus in the presence of the gB-AD2 specific antibody C23 as compared to control was repeatedly observed and might reflect deposition of antibody/virus aggregates on the surface of cells . Neutralization of HCMV via AD2 specific antibodies requires both arms of the IgG Fab fragment and thus crosslinking of different viruses by C23 is a possibility [45] . We next determined the activity of the mabs towards virus that was adsorbed to cells by pre-adsorbing virus to cells for 1 h at 4°C before the gH-specific mab 14-4b was added . As shown in Figure 6B mab 14-4b was capable of neutralizing HCMV at a postadsorption step . In contrast to our findings from assays of antibody inhibition of attachment , the gN-truncated viruses were more susceptible to neutralization than the parental wild type virus . The higher antibody concentration that was required to completely neutralize adsorbed virus was presumably secondary to the requirement of blocking fusion of an already attached virion . Finally , to determine whether antibodies that neutralized gN-truncated viruses more efficiently than the parent virus had altered kinetics of virion binding , we performed experiments that allowed an estimate of the rate of antibody-mediated virus neutralization . Virus and antibody were mixed at 4°C and either used immediately to infect cells or warmed to 37°C for 15 min or 30 min before adding to target cells and the percent neutralization was determined 24 h later . As can be seen in Figure 6C , the anti-gH antibody showed increased neutralization capacity towards the gN-truncated viruses at every time point , reflecting the observations that were made in our standard neutralization assays . Neutralization by the gB-AD2 specific human mab C23 was less affected by truncation of gN but clearly detectable ( Figure 6C ) . Again the finding was consistent with the results from the standard neutralization assay as shown in Figure 4 and because the kinetics were similar , argued that the increased susceptibility to virus neutralizing antibody that was seen with the gN truncated viruses was not secondary to alterations in the kinetics of antibody binding to the virion . In summary , these results indicated that the presence of antibody did not influence the attachment of the gN-truncated viruses to target cells . Moreover , increased neutralization of the gN-truncated viruses was observed very early after combining antibody and virions and even after virions had attached to fibroblasts , suggesting an improved accessibility of target epitopes for the gB- and gH-specific mabs . The data presented thus far indicated that viruses with truncated gNs differed in the accessibility of epitopes on envelope proteins . Whether these differences would also alter the antigenicity of the viruses was tested in the next series of experiments . Groups of 3 mice each were immunized with equal amounts of gradient purified RVAD169 , RVgN-41sig and RVgN-61sig , respectively . The resulting sera from each group were pooled and tested in an ELISA for production of HCMV specific antibodies using RVAD169 as antigen . The individual pools had comparable ELISA titers against whole HCMV antigen as well as against gB alone ( Figure 7A ) . Control mice that had been injected with PBS did not develop anti-HCMV antibodies ( Figure 7A ) . We then tested the serum pools for neutralization of RVAD169 and the gN-truncated viruses ( Figure 7B ) . The individual viruses were neutralized by the different serum pools with similar titers . However , the 50% neutralization titers of the serum pools for RVAD169 , RVgN-41sig and RVgN-61sig were different . Whereas , the three serum pools showed 50% neutralization at a dilution of approximately 1∶800 when RVAD169 was used , the titer increased to approximately 1∶3200 when RVgN-61sig was used as target ( Figure 7B ) . 50% neutralization titers for RVgN-41sig by the three serum pools was observed in the range of 1∶1200 ( Figure 7B ) . The increased susceptibility of RVgN-61sig to virus neutralizing antibodies raised the question whether this phenomenon was based on an overall increase in neutralizing antibody titer or a specific increase in a selected set of antibodies . To test this , we performed ELISA assays using antigens known for binding of neutralizing antibodies . These included three well characterized antigenic domains on gB , namely AD1 , AD2 and AD4 [44] , [46] , [47] . In addition , the AD86 epitope on gH as well as an antigenic region on the tegument protein pp150 was analyzed [48] , [49] . For these analyses , the epitope detected by the anti-gH antibody 14-14b could not be assayed secondary to its conformational dependence . The serum pool derived from RVAD169 immunized mice showed comparable reactivity against epitopes located on gB , gH and pp150 ( Figure 8 ) . Sera from mice immunized with the gN-truncated viruses showed a different pattern of reactivity . Whereas reactivity was comparable between all three serum pools for the AD1 epitope on gB and the AD86 epitope on gH , sera from the gN-truncated immunized animals showed drastically enhanced antibody titers against gB AD2 , gB AD4 and pp150 . Testing the sera individually gave similar reaction pattern indicating that pooling the sera did not bias the result ( data not shown ) . These data suggested that the increase in neutralization capacity in sera from gN-truncated viruses could be based on a selective increase of antibodies binding to neutralization relevant epitopes on different envelope glycoproteins that were less accessible in the wild type AD169 virus .
The remarkable variation in the primary sequence of gN that has been documented in different viral isolates and the extensive oligosaccharide modifications of the gN of HCMV are two characteristics of the HCMV gN that demonstrate its uniqueness among the gNs of human herpes viruses . The observation that the predicted number of potential sites for O-linked carbohydrate modifications in gN remains constant regardless of the variability of the primary sequence argues for a critical role of this modification for the biology of HCMV . Our results are consistent with the possibility that the carbohydrate modifications of this envelope protein limits the activity of virus neutralizing antibodies and suggests at least one functional role for this post-translational modification of this structural protein . The results of our studies indicated that deletion of ser/thr rich primary sequence of the gN ectodomain and the associated carbohydrate modifications associated with these ser/thr residues had no measurable impact on the formation of the gM/gN complex , localization of the complex in sites of virus assembly , and virus replication . Furthermore , the early events of infection such as adsorption , penetration and expression of immediate-early proteins were similar for both gN-truncated viruses and the wild type parent virus . Likewise , for all viruses in this study attachment was inhibited to a similar extent by the addition of heparin , a finding that argued that either the carbohydrate modifications on gN were unimportant for cell surface proteoglycan binding or that residual carbohydrates on the truncated gNs viruses were sufficient for binding to these cellular molecules . Consistent with the former possibility is the finding that other envelope glycoproteins such as gB can also efficiently bind the heparan sulfate [39] . Overall , our results strongly argued that the deletions introduced into the surface domain of gN did not alter the structure of the molecule so as to limit its functional interactions with gM and its essential role in virus replication . In contrast to these results , deletion of oligosaccharides from gN had a significant impact on the susceptibility to virus neutralization by antibodies suggesting that the oligosaccharides played a role in limiting the activity of virus neutralizing antibodies . The effect was seen for antibodies whose activity depended on binding in cis , i . e . gN-specific antibodies . Importantly , deletion of 17 amino acids from the ser/thr sequence in the amino-terminus of gN resulted in a replication competent virus that had increased susceptibility to the neutralizing activity of a mab that reacted with gN from both the wild type and gN truncated virus . Because this antibody recognizes a non-conformation dependent binding site , based on its reactivity with denatured protein , the increased neutralizing activity of this mab for the RVgN-41sig virus is unlikely to be secondary to a conformational change in gN . Together these finding argued that alteration in the carbohydrate modifications of gN in the virus expressing the truncated form of gN likely accounted for the increased virus neutralizing activity of the anti-gN mab when assayed with the RVgN-41sig virus as compared to wild type virus . Of perhaps greater interest was the finding that deletion of ser/thr rich sequences in the surface domain of gN resulted in viruses that were more susceptible to virus neutralizing activities of antibodies whose binding was in trans to gB and gH . This finding was of significance for several reasons . Perhaps the most obvious is that it argued strongly that the mutations introduced into gN that altered the carbohydrate content of gN also had an effect on the recognition of two other abundant envelope glycoproteins by neutralizing antibodies . The mutations in gN did not alter the biochemical content of the envelope as measured by the amount of gN/gM , gB , and gH in the envelope nor the conformation of gB or gH as revealed by their continued recognition by conformation dependent mabs . However , these mutations did alter the susceptibility of these mutant viruses to the neutralizing activity of these mabs . Removal of 17 or 37 residues from the extraviral part of gN resulted in a phenotype that was similar with regard to susceptibility to neutralization . This result indicated that the glycan modification in toto and not site specific carbohydrate modification may be required for the inhibition of virus neutralizing antibody activity that is observed in vitro . Whether similar requirements are operative in vivo is unknown but the maintenance of the number of O-linked glycosylation sites in the coding sequence of gN from a large number of clinical isolates would argue that for optimal evasion of antiviral antibodies , usage of all of the potential carbohydrate modification sites would be required . Our findings argue for a role for the carbohydrate modifications present on gN in antibody recognition of envelope glycoproteins by virus neutralizing antibodies . The structural relationships between the gM/gN complex , gB , and gH/gL in the AD169 laboratory strain of HCMV are unknown but the complexity of protein composition of the virion envelope would suggest that the structure of the envelope would be complex . Because of this complexity we cannot definitively exclude the possibility that a deletion such as the 17 aa deletion in the amino terminus of the gN41-sig mutant could result in major structural changes in the envelope of the virion once the gM/gN41-sig complex was incorporated . Such structural changes resulting from this mutation could be proposed but not tested with available methodologies . Probing the envelope of the mutant viruses with mabs does argue that the overall structure of the envelope is likely intact and that similar stoichiometric relationships between the three major glycoproteins are maintained , findings that are also consistent with the similar in vitro replication of mutant and wild type viruses . However , we cannot formally exclude the possibility that the deletion of residues in the surface domain of gN lead to structural rearrangements within the envelope of infectious virions that increased the neutralizing activity of antibodies directed against three different envelope glycoproteins . Glycan shields that protect viruses from antibody-mediated neutralization are a well described phenomenon and have been extensively investigated in a number of RNA viruses . The most well studied examples are HIV and influenza virus [40] , [41] . For these viruses it is sufficient for the shield to work in cis meaning that the protein that carries the sugar is protected by carbohydrate modification , which could be expected since the viruses carry a single multifunctional protein that works in attachment , receptor binding and fusion . Thus , blocking antibody access of a limited set of protein domains that are crucial for the functional activity of neutralizing antibody is sufficient . The envelopes of herpes viruses are considerably more complex carrying several neutralization-relevant targets that exhibit redundant functions in the early events of virus infection . In case of HCMV these include at least gB and different gH complexes [50] , [51] . Thus , evasion from neutralizing antibodies would require a potentially very different strategy and one that is likely much more complex because simply protecting a single protein would likely not be sufficient . The induction of neutralizing and non-neutralizing antibodies which compete for binding to the same antigenic domain is a mechanism that has been described for the gB neutralizing site AD1 [13] . Whether such a mechanism is operative for additional antibody binding sites on the HCMV envelope is unknown . More recent data show that there are domains on gB which are bound exclusively by neutralizing antibodies indicating that competition of neutralizing and non-neutralizing antibodies is probably not a general evasion mechanism [44] . Data from the present study raises the possibility that carbohydrate shielding of several glycoproteins by the heavily glycosylaetd gN envelope protein could be a second major mechanism that limits the neutralization of virus infectivity by antibodies . Deletion of a fraction of the predicted oligosaccharide addition sites from gN resulted in increased neutralization activity of a number of mabs that have been shown to be directed at different envelope proteins , although the effect was not equivalent for all antibodies . Whereas one anti-gH antibody and an anti-gB AD2 antibody required 5–10 times higher concentration to achieve 50% neutralization titers towards the gN-truncated viruses , antibodies against other epitopes on gB or gH were less affected by the gN truncations . This finding argues the functional efficiency of the proposed glycan shielding may be heterogeneous depending on the binding activity and target epitope of different mabs . However , with the possible exception of the anti-gB antibody C23 , we did not see similar neutralization capacity of any antibody for the parent and the gN-truncated viruses . We can only speculate on the mechanism ( s ) that are involved in the glycan mediated shielding of HCMV from the activity of virus neutralizing antibodies . A significant change in the protein composition of the envelope secondary to the gN-truncation could facilitate binding of neutralizing antibodies by drastically altering the structure of the virion envelope . However , this seems unlikely since we did not detect changes in the gB/gN ratio in purified virions nor significant changes in virion binding or internalization . In addition , the observed epitope-specific effect on virus neutralizing antibody activity together with the unchanged stoichiometry of the envelope glycoproteins gB , gH , and gM/gN in recombinant viruses with deletions in the ser/thr rich sequences of gN would argue against a global change in the viral envelope . In support of these arguments , deletion of individual envelope proteins in HSV-1 did not change the composition of others [52] . Thus , it is unlikely that deletion of 17 aa , as in the case of RVgN-41sig , resulted in significant changes in glycoprotein composition or structure of the viral envelope . An obvious explanation could be the shielding of a fraction of epitopes on gB and gH by the glycans of gN thereby impeding antibody access to selected epitopes . Whether this mechanism is possible given the differences in protein size between gB ( approx . 700 aa surface domain ) , gH ( approx . 700 aa surface domain ) and gN ( approx . 100 aa surface domain ) remains to be determined experimentally and will require structural information about the epitopes recognized by neutralizing antibodies that are exposed on the virion surface . Alternatively , removal of sugars from gN could alter the architecture of the outer surface of the envelope without affecting copy numbers of the individual protein components of the envelope . Mass spectrometry of HCMV has identified gM as the most abundant glycoprotein in the viral envelope . It can be assumed that gN is also highly abundant since it is covalently linked to gM via a disulfide bond [21] . The linkage is between cysteine at position 90 in gN and cysteine at position 44 in gM , thus it is not affected by the truncations that were introduced in gN [36] and the western blot analyses of the gN-truncated virions under non-reducing conditions supported this assumption . By occupying space , the bulky carbohydrate head of fully modified gN could result in closer packing of the surface domains of gB and gH , thereby impeding access of some gB- and gH-specific antibodies . If sugar is removed , it may result in a decreased density of the spacing of the respective proteins and permit easier access of antibodies to epitopes on gB and gH . Alternatively , structural changes may be induced resulting in altered binding avidity of immunoglobulin which could affect neutralization sensitivity [53] . The fact that we observed increased susceptibility for only a subset of neutralization relevant epitopes would be compatible with any of these mechanisms . It is interesting to note , that HCMV carries a number of additional glycoproteins which have been shown to contain significant carbohydrate modifications , such as gB and gO [54]–[56] . If they were to have similar effects , the overall protection from the activity of virus neutralizing antibodies would be very significant . For gB of murid herpes virus 4 , a significant effect of glycosylation on the evasion from virus neutralizing antibodies has been demonstrated [57] . There is indirect evidence that gO of HCMV may also protect the virus from neutralizing antibodies . Jiang et al . [58] have reported that the cell-to-cell spread of a recombinant virus lacking gO is more sensitive to neutralization by polyclonal sera than the parental virus . Thus , the protection from antibody-mediated neutralization by glycan modification on envelope glycoproteins could represent a more general phenomenon for herpes viruses than has been previously considered . The immunization experiments indicated that gN-truncated viruses induced an antibody response that was not different from animals immunized with the parent virus in terms of the overall ELISA titer and gB ELISA titer . However , when the sera were tested in virus neutralization assays , a clear difference was seen . The RVgN-41sig virus and the RVgN-61sig virus were neutralized more efficiently by any serum pool , a result that was most apparent when the RVgN-61sig virus was used in these assays . These findings again emphasize that the gN-truncated viruses were more readily neutralized when compared to the wild type parental virus . Interestingly , on the epitope level , the gN-truncated viruses induced an antibody response that was different from the response against the parent virus . Epitopes which represented minor targets and induced only low levels of antibody in the wild type RVAD169 virus immunized animals became markedly more immunogenic in the gN-truncated virus immunized animals . This was most pronounced for the AD2 and AD4 epitopes on gB but also seen for the epitopes on gH and pp150 . Interestingly , this was again an epitope specific effect because this response was not as apparent for the AD1 epitope on gB , possibly because of the dominance of this epitope in the antibody response to HCMV in mice and humans . Thus , the trans-effect that was seen in neutralization of the gN-truncated viruses with the different mabs was also reflected after immunization with viruses deficient in the glycan modifications of gN . The underlying mechanisms for the induction of a different set of antibodies by the fully glycosylated virus and the gN-truncated viruses are unclear at present but may involve activation of a different set of naïve B cells to produce antibodies after antigenic stimulation , a mechanism that has recently been suggested for the induction of neutralizing HIV-specific antibodies [59] . Defining the underlying mechanism for the enhanced immunogenicity of selected epitopes on the HCMV envelope proteins is a relevant question for the development of immunogenic components for vaccine development and is actively under investigation in our laboratories . Finally , a recent study of bovine herpes virus 4 that detailed the effect of O-glycosylation of gp180 on antibody evasion also found increased sensitivity of gp180-deficient virions to antibody-mediated neutralization but no difference between the immunogenicity of viruses with or without expression of gp180 [60] . It was surprising that we did not observe a difference in antibody binding ELISA assays when we used whole virus or purified gB as antigenic substrate . The most likely explanation is that the overwhelming composition of the antibody response against either antigen was directed at antibody binding sites irrelevant to the activity of virus neutralizing antibodies . Our analysis of the human antibody repertoire against gB has revealed that >95% of antibodies directed against gB are non-neutralizing [44] . The situation may be similar after immunization of mice with HCMV particles . Increased antibody titers against neutralization relevant epitopes , however , did not translate into increased neutralization capacity of the respective serum pool against the virus carrying full length gN suggesting that cryptic epitopes were not exposed in viruses lacking the full complement of carbohydrate modifications on gN . Thus , the resistance of viruses containing full length fully glyosylated gN can be most readily explained by the shielding by carbohydrates of the respective epitopes recognized by virus neutralizing antibodies . Lastly , what could be the relevance of our findings for the activities of virus neutralizing antibodies in vivo ? The results from the immunization experiments in mice suggest that individual differences in the antibody repertoire induced in infected individuals in an outbred population such as humans has only limited consequences for the neutralization of the virus in vivo since neutralization relevant epitopes might be protected by the glycan shield of the virus .
This study was performed in strict accordance with German law ( Tierschutzgesetz ) . The protocol was approved by the Committee on the Ethics of Animal Experiments at the Bavarian Government ( Regierung von Mittelfranken , permit 54-2531 . 31-8/04 ) . All efforts were made to minimize animal suffering . The human kidney cell line 293T and human lung fibroblasts ( MRC5 ) were maintained in Dulbecco modified Eagle medium supplemented with 10% fetal calf serum ( FCS ) , glutamine ( 100 mg/L ) and gentamycin ( 350 mg/L ) . Human foreskin fibroblasts ( HFF ) were kept in minimum essential medium supplemented with 5% FCS , glutamine and gentamycine . All viruses used were propagated on MRC5 cells and viral titers were determined in HFF using an indirect immunofluorescent assay with the mab p63-27 , directed against the HCMV immediate-early 1 ( IE1 ) protein [61] . Virions were isolated by glycerol-tartrate gradient centrifugation as described [62] . For growth curves , HFF , plated in 24-well dishes , were infected at a multiplicity of infection ( m . o . i . ) of approximately 0 . 1 . After adsorption of virus ( 2 hours ) , the inoculum was removed and replaced by fresh medium . Supernatants were harvested at the indicated time points and stored at −80°C until use . Virus titers were determined by an indirect immunfluorescence assay using a mab against the IE-1 protein of HCMV as described [61] . The mabs used in this study have been described . Murine mab: gM-specific IMP91-3/1 [23] , gB-specific 27–287 [63] , gN-specific 14-16A [23] , gH-specific 14-4b [38]; Human mab: gB-specific C23 ( kindly provided by Teijin Pharma Limited , Japan ) [64] , ITC88 [14] , SM5-1 and 1G2 [44] . For specificity of the mabs see Figure S1 . Secondary antibodies were purchased from Dianova or DAKO . Sera from HCMV- positive and negative individuals were randomly selected from our diagnostic department . Serial mab or serum dilutions were incubated with virus preparations for 1 h at 37°C . Viral titers were adjusted to give 100 to 150 infected cells counted on a fluorescence microscope using a 200× magnification , equivalent to 2000 infected cells/15000 cells . The virus antibody mixture was added to fibroblasts which were seeded at 1 , 5×104 per well in 96-well plates the day before . The medium was replaced 4 h later and the number of infected cells was counted 16 h later using indirect immunofluorescence with mab p63-27 . Percent neutralization was calculated as reciprocal of infectivity with maximum infectivity being determined by incubation of virus without antibody . The number of infected cells without addition of antibody also served as reference for the determination of infectious units ( IU ) . For the kinetic experiments , all reagent ( virus , antibody , tissue culture medium ) were cooled to 4°C before mixing . Mixtures were transferred to 37°C for the indicated time periods and added to fibroblasts at 37°C . The medium was replaced 4 h later and the number of infected cells determined as described above . Balb/c mice were obtained from Charles River Laboratories , Inc . . Groups of 3 mice each were immunized with the respective virus . 200 µl containing 5 µg of gradient purified virus and 100 µl aluminum hydroxide adjuvant were administered intraperitoneally . Booster immunizations were given at weeks 5 and 8 . Animals were sacrificed and sera of mice immunized with the same virus were pooled . Plasmids expressing truncated forms of gN were constructed on the basis of pcDNA3 . 1myc/his ( Invitrogen ) . First , the gN coding sequences for residues 1–23 , which includes the predicted signal sequence , was inserted using the EcoRI and BamHI restriction sites of pcDNAmyc/his . In a second cloning step , gN sequences coding for aa 41–138 , 61–138 and 90–138 were inserted into the gN-signal sequence containing plasmid via the BamHI and HindIII sites . All DNA fragments were generated by PCR using appropriate primers and integrity of the gN-expressing sequences of the resulting plasmids ( gN-41sig , gN-61sig , gN-90sig ) was confirmed by nucleotide sequencing . Mutagenesis of HB5 [35] was performed using linear DNA fragments for homologous recombination . To generate BACmids containing the respective UL73 mutant sequences we used the plasmid pCPoΔUL73 [19] . This plasmid is a pCPo15 [65] derivative that contains the entire orf UL72 ( nt 104560–105730 , nomenclature according to Genbank Accession number X17403 ) at the 5′end and the entire orf UL74 ( nt106095–107587 ) at the 3′end of the kanamycin resistance gene in plasmid pCP-o-15-Link2 . Fragments containing the respective UL73 sequences were PCR-amplified from the plasmids described above and the amplimers were inserted into the 5′-flanking region of the kanamycin resistance gene in pCPoΔUL73 . From these plasmids PCR fragments were generated encompassing the respective UL73-Kan-UL74 segment . Primers that were used included UL72up5 ( nt 105682–105696 ) and UL73rec3 ( nt 106271–106251 ) . Recombination in pHB5 was done as described previously [19] . In brief , the DNA fragment was electroporated into E . coli DH10B carrying the BAC pHB5 and the plasmid pBAD for recE/T mediated recombination [66] . Bacterial colonies were selected on agar plates containing kanamycin ( 30 µg/ml ) and chloramphenicol ( 30 µg/ml ) . To confirm the integrity of the recombined BAC , digestion of DNA with the appropriate restriction enzymes was carried out and analyzed via agarose gel electrophoresis in comparison to the parental pHB5 . To confirm recombination at the predicted site Southern Blot analysis , PCR analysis as well as DNA sequence analysis of the UL72–UL75 region was performed . To remove the kanamycin resistance gene after successful recombination , plasmid pBT340 encoding the flp-recombinase was used as described [65] . To reconstitute infectious virus , MRC-5 cells ( 300 . 000 cells per well ) were seeded into 6-well dishes . 48 h later 5 µg of BAC DNA together with 1 µg of pcDNApp71tag DNA ( kindly provided by B . Plachter , University of Mainz , Mainz , Germany ) were transfected with Superfect reagent ( Qiagen ) according to the manufacturer's instructions . 24 hours later culture medium was replaced by fresh medium and cells were cultivated for 7 days . Cells were then transferred to 25 cm2 flasks and cultured until a cytopathic effect was observed . The recombinant viruses were designated: RVAD169 ( reconstituted from HB5 ) , RVgN-41sig and RVgN-61sig , respectively . Glycerol-tartrate gradient purified virus was subjected to urea-polyacrylamide-gelelectrophoresis as described previously [23] . Transfer of samples to nitrocellulose membranes was carried out by standard procedure . For visualization of antigens , gB- , gN- and gM-specific mabs were applied and detected with peroxidase-conjugated anti-mouse-IgG and anti-mouse-IgM , respectively , and the ECL detection system ( Pharmacia Biotech ) . The recombinant antigens used for the ELISA have been described [67] . Briefly , the following antigens were used: soluble gB ( kindly provided by Sanofi-Pasteur , France ) ; gB-AD1 , containing aa 484–650 of gB; gB-AD2 , containing aa 68–80 of gB; gB-AD4 containing aa 121–132 and 344–438; gH-AD86 containing aa 1–142 of gH strain AD169 and pp150 containing aa 555–705 of the tegument protein pp150 strain AD169 . Proteins were diluted between 25 ng and 200 ng ( depending on antigen ) in 0 . 5 M sodium carbonate buffer , pH 9 . 6 , or in 6 M urea ( AD1 ) and 50 µl was used to coat microtiter plates overnight at 4°C . For the ELISA using viral lysates , wells were coated with virus lysates of 500 ng/well in 50 µl in 0 . 5 M sodium carbonate buffer , pH 9 . 6 . Original virus lysates were prepared at 100 ng/µl in PBS/1% NP40 . All subsequent steps were carried out at room temperature . Reaction wells were rinsed with PBS supplemented with 0 . 1% Tween 20 and blocked for 2 h with PBS containing 2% FCS . Plates were again rinsed with PBS supplemented with 0 . 1% Tween 20 and incubated with mabs or serum ( 50 µl/well ) for 2 h . Unbound antibody was removed by washing and peroxidase-conjugated anti-human or anti-mouse IgG ( Dako , Germany ) was added at an appropriate dilution for 1 h . The plate was washed and 100 µl TMB ( tetramethylbenzidine ) peroxidase substrate , diluted 1∶1 in peroxidase substrate solution B ( KPL , USA ) , added for 5 min . The reaction was stopped by the addition of 100 µl 1 M H3PO4 and the OD450 was determined using an Emax microplate reader ( Eurofins MWG Operon , Germany ) . Dilution of all antibodies was done in PBS with 2% FCS . In all assays involving bacterially derived fusion proteins , the respective prokaryotic fusion partner was assayed in parallel and the optical density was subtracted from values obtained with the fusion protein . Fibroblasts were seeded at 3×104 cells per well in 96-well plates . Virus was preincubated with individual mabs for 1 h at 37°C at concentrations ensuring complete neutralization . Cells and the virus/mab mixture were cooled to 4°C and the virus/mab mixture was added to the cells at a multiplicity of infection ( m . o . i . ) of 0 . 5 . Following incubation for 1 h at 4°C , cells were washed three times with ice-cold PBS and cell lysates were prepared by freezing/thawing . DNA was extracted from the lysates using a MagNA Pure LC ( Roche , Germany ) instrument and quantitative real-time PCR was performed on an ABI PRISM 7500 . To control for recovery of cells , copy numbers of albumine DNA was determined in parallel to HCMV and HCMV copies were calculated per 1000 copies albumine . Primers: CMV 5′:GAGCAGACTCTCAGAGGATCGG; CMV 3′: AAGCGGCCTCTGATAACCAAG; Albumine 5′: GTGAACAGGCGACCATGCT; Albumine 3′: GCATGGAAGGTGAATGTTTCAG . Cos7 cells grown on glass coverslips in 24-well plates were transfected with 0 . 8 µg of plasmid DNA using Lipofectamin ( Invitrogen ) . Fibroblasts , also grown on glass coverslips in 24-well plates , were infected with the respective viruses at a m . o . i . of 0 . 4 . At the indicated times , the coverslips were washed and fixed in 3 . 0% paraformaldehyde in PBS . The fixed cells were permeabilized with PBS , 0 . 1% Triton X-100 for 4 min and then blocked using PBS 1% BSA for 15 min at room temperature . Primary antibodies were then added for 30 min at 37°C . Following washing , antibody binding was detected with the appropriate secondary antibody conjugated with either FITC or TRITC ( Dianova ) . Images were collected using a Zeiss Axioplan 2 fluorescence microscope fitted with a Visitron Systems CCD camera ( Puchheim , Germany ) . Images were processed using MetaView software and Adobe Photoshop . | Herpes viruses are transmitted between individuals in cell free form and successful spread benefits from mechanisms that limit the loss of infectivity by the activity of virus neutralizing antibodies . Human cytomegalovirus ( HCMV ) is an important pathogen and understanding how the virus can evade antiviral antibodies may be clinically relevant . HCMV particles contain a number of highly polymorphic , extensively glycosylated envelope proteins , one of which is glycoprotein N ( gN ) . This protein is essential for replication of HCMV . We have hypothesized that the extensive glycosylation of gN may serve as a tool to evade neutralization by antiviral antibodies . Recombinant viruses were generated expressing gN proteins with reduced glycan modification . The loss of glycan modification had no detectable influence on the in vitro replication of the respective viruses . However , the recombinant viruses containing under-glycosylated forms of gN were significantly more susceptible to neutralization by a diverse array of antibody reactivities . Immunization of mice with viruses carrying fewer glycan modification induced significantly higher antibody titers against the homologous virus; however , the neutralization titers against the fully glycosylated virions , were not enhanced . Our results indicate that glycosylation of gN of HCMV represents a potentially important mechanism for evasion of antibody-mediated neutralization by a number of different antibody specificities . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"immunology",
"biology",
"immunomodulation",
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] | 2012 | Glycoprotein N of Human Cytomegalovirus Protects the Virus from Neutralizing Antibodies |
Type 3 effector proteins secreted via the bacterial type 3 secretion system ( T3SS ) are not only virulence factors of pathogenic bacteria , but also influence symbiotic interactions between nitrogen-fixing nodule bacteria ( rhizobia ) and leguminous host plants . In this study , we characterized NopM ( nodulation outer protein M ) of Rhizobium sp . strain NGR234 , which shows sequence similarities with novel E3 ubiquitin ligase ( NEL ) domain effectors from the human pathogens Shigella flexneri and Salomonella enterica . NopM expressed in Escherichia coli , but not the non-functional mutant protein NopM-C338A , showed E3 ubiquitin ligase activity in vitro . In vivo , NopM , but not inactive NopM-C338A , promoted nodulation of the host plant Lablab purpureus by NGR234 . When NopM was expressed in yeast , it inhibited mating pheromone signaling , a mitogen-activated protein ( MAP ) kinase pathway . When expressed in the plant Nicotiana benthamiana , NopM inhibited one part of the plant's defense response , as shown by a reduced production of reactive oxygen species ( ROS ) in response to the flagellin peptide flg22 , whereas it stimulated another part , namely the induction of defense genes . In summary , our data indicate the potential for NopM as a functional NEL domain E3 ubiquitin ligase . Our findings that NopM dampened the flg22-induced ROS burst in N . benthamiana but promoted defense gene induction are consistent with the concept that pattern-triggered immunity is split in two separate signaling branches , one leading to ROS production and the other to defense gene induction .
Type 3 effector proteins of pathogenic Gram-negative bacteria are transported into eukaryotic host cells through the bacterial type 3 secretion system ( T3SS ) , which forms a needle-like pilus [1]–[3] . Various effectors from phytopathogenic bacteria act as virulence factors by suppressing activation of plant defense genes , i . e . they inhibit innate immunity triggered by highly conserved ubiquitous microbial elicitors ( microbe-associated molecular patterns – MAMPs ) such as flagellin , also called pattern-triggered immunity . On the other hand , plants can also possess resistance ( R ) proteins that mediate defense ( effector-triggered immunity ) by directly or indirectly recognizing specific type 3 effectors ( avirulence factors ) . Hence , type 3 effectors of pathogenic bacteria can positively or negatively affect pathogenicity [1]–[3] . Interestingly , certain rhizobia also use type 3 effectors during symbiosis with host legumes [4] , [5] . Rhizobia are nitrogen-fixing bacteria which establish a specific mutualistic endosymbiosis with legumes and certain species of the genus Parasponia . As a result of rhizobial infection , roots of host plants develop nodules , in which the bacteria differentiate into bacteroids . For the host's benefit , atmospheric nitrogen is then reduced to ammonia by the bacterial nitrogenase enzyme . During nodule formation , various signal molecules are exchanged between the two partners [6] , [7] . Flavonoids released by host plants into the rhizosphere interact with rhizobial transcriptional regulators ( NodD proteins ) . As a result , symbiotic genes involved in synthesis of bacterial nodulation signals ( Nod factors ) are activated . In certain rhizobial strains , such as Rhizobium sp . strain NGR234 [8] , NodD-flavonoid interactions also result in stimulated expression of ttsI . This gene encodes a transcriptional activator , which controls expression of genes that have a conserved cis-element in their promoters , named tts-box . In NGR234 and a number of other strains , genes encoding a bacterial type 3 secretion system ( T3SS ) and corresponding type 3 effectors are regulated by TtsI [9] , [10] . Mutant analysis revealed that type 3 effectors of NGR234 can play a role during symbiosis . Depending on the host plant , positive , negative or no effects on symbiosis have been reported [11]–[16] . One approach to study the function of bacterial effectors is to express them singly in eukaryotic cells . The type 3 effector proteins NopL and NopT ( nodulation outer proteins L and T ) of strain NGR234 have been characterized in this way . When expressed in tobacco and Lotus japonicus , NopL suppressed expression of defense genes [17] . NopL was multiply phosphorylated within eukaryotic cells and interfered with mitogen-activated protein ( MAP ) kinase signaling in yeast and tobacco cells [16] . Indeed , nodules of certain bean cultivars colonized by NGR234 mutated in nopL rapidly developed necrotic areas , indicating a lack of suppression of defense [16] . The protease NopT , another type 3 effector of NGR234 belonging to the YopT-AvrPphB effector family , influenced nodulation of host plants either positively or negatively [14] , [15] . Accordingly , when transiently expressed in tobacco plants , proteolytically active NopT elicited a rapid hypersensitive reaction , suggesting that NopT action induced an R-protein mediated defense response in this non-host plant [14] . Similarly , resistance ( R ) genes ( Rj2 and Rfg1 ) of certain soybean cultivars are involved in host-specific nodulation and prevented establishment of symbiosis with specific strains in a T3SS-dependent manner [18] . The leucine-rich repeat ( LRR ) protein NopM ( nodulation outer protein M ) was first identified in Sinorhizobium fredii HH103 using a proteomic approach , in which secreted proteins from a T3SS-deficient mutant were compared to proteins from wild-type bacteria [19] . Homologous sequences exist in various rhizobial strains , namely Rhizobium sp . strain NGR234 ( nopM formerly y4fR ) , Bradyrhizobium japonicum USDA110 ( blr1904 and blr1676 ) and B . elkanii USDA 61 ( nopM ) . During the course of the present study , T3SS-dependent secretion of NopM has been reported for strain NGR234 and a nopM deletion mutant induced fewer nodules on the host Lablab purpureus compared to the parent strain [15] . The nopM promoter activity depended on TtsI , which is predicted to bind to the conserved tts box ( TB1 ) in the promoter region of nopM [10] . Based on sequence comparisons , rhizobial NopM proteins are predicted type 3 effectors belonging to the IpaH effector family with representatives in Shigella flexneri ( such as IpaH9 . 8 and IpaH1 . 4 ) and Salomonella enterica ( SspH1 , SspH2 , SlrP ) [20]–[24] . The NopM sequence is also related to the YopM effector of Yersinia pestis [25] . Sequence similarities , albeit less related , also exist for non-characterized effectors from other bacteria , including the phytopathogens Pseudomonas syringae and Ralstonia solanacearum ( e . g . HpX29 of R . solanacearum strain RS1000 ) [26] . IpaH family effectors are E3 ubiquitin ligases with a NEL ( novel E3 ligase ) domain . Enzymatic activity has been demonstrated for effectors from S . flexneri ( such as IpaH9 . 8 and IpaH1 . 4 ) and S . enterica ( SspH1 , SspH2 , SlrP ) [20]–[24] . E3 ubiquitin ligases mediate transfer of ubiquitin from an E2 ubiquitin conjugating enzyme to a given target protein in eukaryotic cells , which is thereby marked for degradation . Ubiquitin-mediated proteasome-dependent protein degradation is conserved in eukaryotic cells . Ubiquitination itself requires three enzymatic components . First , an ubiquitin-activating enzyme ( E1 ) forms a thioester bond between a catalytic cysteine and the carboxy terminal glycine residue of ubiquitin . The ubiquitin is then transferred to an ubiquitin-conjugating enzyme ( E2 ) . Finally , an E3 ubiquitin ligase facilitates the covalent conjugation of ubiquitin from an ubiquitin-loaded E2 to one or more lysine residues in a given protein substrate [27] . Bacterial E3 ubiquitin ligases delivered into host cells mimic the activities of host E3 ubiquitin ligases and ubiquitinate specific target proteins . For example , IpaH9 . 8 of S . enterica blocks the innate immune system of human cells by interfering with the nuclear factor κB ( NF-κB ) pathway . IpaH9 . 8 interacts with NEMO ( NFκB essential modifier or IKKγ; an essential component of the multi-protein IKK ( IκB kinase ) complex ) and the ubiquitin-binding adaptor protein ABIN-1 . As a result , NEMO is polyubiquitinated and NF-κB activation is suppressed [28] . The SlrP effector of S . enterica targets thioredoxin and ERdj3 , an endoplasmic reticulum luminal chaperone [29] . In this study , we characterize NopM of Rhizobium sp . strain NGR234 . We demonstrate that NopM possesses E3 ubiquitin ligase activity . Our mutant analysis reveals that NopM acts as an E3 ubiquitin ligase during symbiosis with the host L . purpureus . NopM activity also inhibits mating pheromone signaling when expressed in yeast , and MAMP-triggered generation of reactive oxygen species ( ROS ) when expressed in Nicotiana benthamiana plants , while stimulating expression of MAMP-induced defense genes at the same time . We discuss our results in the light of the role of NopM in symbiosis .
The coding region of nopM was cloned into pET28b resulting in plasmid pET-nopM . A second plasmid , pET-nopM ( C338A ) , was constructed in which the cysteine residue 338 of NopM was replaced by alanine . Residue C338 in the C-terminal NEL domain is predicted to be an essential catalytic residue required for the ubiquitin transfer [20]–[22] . Escherichia coli BL21 ( DE3 ) harboring the constructed plasmids were induced with IPTG and extracted proteins were purified using nickel–nitrilotriacetic acid affinity chromatography . When analyzed by SDS-PAGE , a strong band with an apparent molecular mass of about 65 kD was detected , which corresponded to His-tagged NopM and NopM-C338A , respectively ( calculated molecular weight of NopM ≈60 . 5 kD ) . This band was not seen , when proteins from E . coli BL21 ( DE3 ) harboring the empty vector pET28b were purified in a similar way . Immunoblot analysis revealed that corresponding anti-NopM antibodies recognized His-tagged NopM and NopM-C338A proteins ( Figure 1A ) . His-tagged NopM and NopM-C338A purified from E . coli cells were then tested using an in vitro E3 ubiquitin ligase assay using HA-tagged ubiquitin as a substrate . After incubation , reaction mixtures were separated by SDS-PAGE and corresponding immunoblots were performed with anti-HA or anti-NopM antibodies . As shown in Figure 1B , when ubiquitination reactions were performed with His-NopM , anti-HA antibodies recognized a ladder of ubiquitinated proteins in the range of 24 to >200 kD . The size of proteins detected by the anti-HA antibodies ( 27 kD , 45 kD and 63 kD bands ) were multiples of the size of HA-ubiquitin ( 9 kD ) , indicating formation of polyubiquitination chains . In contrast , reactions with NopM-C338A did not result in a ladder of polyubiquitinated proteins . Anti-NopM antibodies recognized a 65-kD protein band corresponding to His-tagged NopM and NopM-C338A , respectively . No additional bands of higher molecular weight were observed , indicating that NopM itself was not autoubiquitinated ( Figure 1B ) . Taken together , these findings show that NopM is an E3 ubiquitin ligase and that the mutant protein NopM-C338A lacks this enzyme activity . Two mutant derivatives of Rhizobium sp . NGR234 were constructed to examine the function of NopM during symbiosis . A nopM knock-out mutant , called NGRΩnopM , was generated , which contained an Ω spectinomycin interposon close to the ATG start codon . A point mutant , called NGRnopM ( C338A ) , was constructed by using a corresponding DNA sequence encoding NopM-C338A ( Figure 2A ) . Proteins from apigenin-induced culture supernatants were concentrated and used for immunoblots with the anti-NopM antibodies . NopM and NopM-C338A ( ca . 60-kD bands ) were detected in culture supernatants from the parent strain NGR234 and the NGRnopM ( C338A ) mutant , respectively . As expected , no bands were seen for the knock-out mutant NGRΩnopM . Strain NGRΩrhcN , a mutant lacking a functional T3SS served as a negative control [11] . NGRΩnopM carrying the plasmid pFAJ-nopM ( containing nopM including its promoter sequence ) secreted NopM , indicating complementation by this plasmid ( Figure 2B ) . Nodulation phenotypes of the examined strains differed when the legume L . purpureus was inoculated . Figure 2C shows the results for a representative nodulation experiment . The parent strain NGR234 induced about 5–6 nodules per plant under the tested growth conditions . In contrast , NGRΩnopM induced significantly fewer nodules ( 1–2 nodules per plant ) , indicating that NopM was required for optimal nodulation of this host plant . Plants inoculated with NGRnopM ( C338A ) showed a similar reduction in nodulation , indicating that the C338 residue is essential for the nodule-promoting effect of NopM . The symbiotic phenotype of NGRΩnopM and NGRnopM ( C338A ) on L . purpureus could be complemented when plasmid pFAJ-nopM was introduced into these mutants . The nodule number was significantly increased and reached values comparable to those of the parent strain NGR234 ( Figure 2C ) . Nodulation tests were also performed with Phaseolus vulgaris ( cv . Yudou No 1 ) . Optimal nodulation of this plant with NGR234 required NopT , another type 3 effector of NGR234 [14] . Nodulation data with either NGRΩnopM or NGRnopM ( C338A ) were similar to those obtained from the parent strain NGR234 , however . Similarly , nodulation tests with the constructed mutants showed no obvious differences for Flemingia congesta ( data not shown ) , although nodulation of this host plant is improved by a functional T3SS [12] . Taken together , the mutant analysis revealed that the symbiotic phenotype of the constructed nopM mutants depended on the tested host legume and that the positive effect of NopM on L . purpureus nodulation likely depended on its E3 ubiquitin ligase activity . When expressed in yeast , IpaH9 . 8 of S . flexneri blocked mating pheromone ( α-factor ) response signaling , a specific MAP kinase pathway [20] . We used the same type of assay to study the effect of NopM when expressed in yeast . The α-factor is perceived by a G protein-coupled receptor and activation of the signal cascade results in arrest of the cell cycle and transcription of mating genes . Accordingly , application of α-factor to the center of an agar plate of strain W303-1A ( MATa ) results in a typical halo of growth inhibition [30] . The coding sequence of nopM and the point-mutated sequence encoding NopM-C338A were cloned into the expression vector pESC-leu , which has a galactose-inducible promoter ( GAL1 ) . W303-1A cells carrying the resulting plasmids ( pESC-nopM and pESC-nopM ( C338A ) , respectively ) expressed NopM and NopM-C338A on galactose plates: An immunoblot with anti-NopM antibodies exhibited a band corresponding to the predicted size of NopM ( 60 . 5 kD ) , which was absent in cells transformed with the empty vector pESC-leu ( Figure 3A ) . Upon exposure to α-factor , yeast cells expressing nopM under the GAL1 promoter failed to form a halo , indicating that NopM interfered with the mating pheromone signaling pathway . Using the same assay , NopM-C338A did not inhibit mating pheromone response signaling ( Figure 3B ) . A similar growth inhibition assay was performed with yeast strain SY2227 , which expresses the G protein β-subunit STE4 when the fungus is grown on galactose-containing media . Overproduction of STE4 activates the mating pheromone signaling pathway and therefore causes cell growth arrest in the absence of α-factor [31] . Figure 3C shows the growth phenotype of this strain transformed with pESC-nopM , pESC-nopM ( C338A ) or the empty vector pESC-leu . Yeast transformed with pESC-nopM showed normal growth on SD/galactose plates . In contrast , cells transformed with pESC-nopM ( C338A ) or pESC-leu poorly grew on SD/galactose ( Figure 3C ) . Hence , NopM , but not NopM-C338A , inhibited STE4-induced mating pheromone signaling . To investigate effects of NopM within plant cells , NopM and NopM-C338A were transiently expressed in N . benthamiana . DNA encoding NopM or NopM-C338A was cloned into the binary vector pCAMBIA-T , which contains a 35S cauliflower mosaic virus 35S promoter . Agrobacterium tumefaciens cells carrying these vectors were then used for infiltration of N . benthamiana leaves . Immunoblot analysis with anti-NopM antibodies revealed the presence of NopM and NopM-C338A proteins in transformed tissue ( Figure S1 in Text S1 , panel A ) . Leaves expressing NopM ( 2 days after infiltration ) showed no hypersensitive reaction ( Figure 4A ) . Trypan blue based cell death staining of leaves ( 5 days after infiltration ) showed that neither NopM nor NopM-C338A caused visible changes as compared to leaf tissue transformed with the empty vector ( Figure S1 in Text S1 , panel B ) . The P . syringae pv . tomato DC3000 effector HopQ1 , which is known to induce a hypersensitive reaction in N . benthamiana [32] , was used as a positive control . As expected , the HopQ1 expressing tissue was necrotic and strongly stained by trypan blue . Transient generation of reactive oxygen species ( ROS ) induced by MAMPs is a rapid signaling response , which depends on Rboh enzymes ( respiratory burst oxidase homologs ) and is activated by calcium-dependent protein kinases ( CDPKs ) [33] , [34] . When challenged with flg22 , a conserved , 22-amino acid motif of the bacterial MAMP flagellin [1] , N . benthamiana leaf disks respond with a ROS burst , which can be measured with luminol and horseradish peroxidase [35] . An example for such an experiment is shown in Figure 4B . Interestingly , the flg22-induced ROS burst was nearly completely abolished in leaf disks expressing NopM ( statistical analysis of all data from 4 independent time series; significant differences as compared to controls transformed with the empty vector; one-way ANOVA , P = 0 . 002 ) . In disks expressing NopM-C338A , however , the ROS burst in response to flg22 was similar to empty vector controls ( p = 0 . 24 ) . Accordingly , differences between disks expressing NopM and NopM-C338A were significant ( one-way ANOVA , P = 0 . 008 ) , suggesting that ROS suppression depends on the ubiquitin E3 ligase activity of NopM ( Figure 4B ) . Transcript levels of the flg22-responsive defense genes NbAcre31 ( encoding a putative calcium-binding protein ) and NbCyp71d20 ( encoding a putative cytochrome P450 ) are upregulated in response to flg22 [36] . Quantitative reverse transcription ( qRT ) -PCR was used to examine the effect of NopM on expression of these genes ( Figure 4C and D ) . In the absence of flg22 , NopM expression resulted in slightly elevated transcript levels of NbAcre31 ( one-way ANOVA , p = 0 . 03 ) , but not of NbCyp71d20 ( p = 0 . 08 ) . In contrast , cells expressing NopM-C338A neither showed increased transcript levels of NbAcre31 nor of NbCyp71d20 . As expected , leaf tissue challenged with flg22 showed stimulated expression , particularly for NbCyp71d20 . When compared to empty vector controls , effects of the flg22 treatment on NbCyp71d20 activation were significantly stronger in either NopM or NopM-C338A expressing tissues ( Figure 4D ) . Thus , NopM promoted flg22-induced NbCyp71d20 expression independently of its E3 ubiquitin ligase activity . The flg22-induced expression of NbAcre31 and NbCyp71d20 depends on the MAP kinase SIPK ( salicylic acid-induced protein kinase ) [37] . The amounts of active MAP kinases in N . benthamiana were visualized on immunoblots with anti-p42/44-phospho-ERK antibodies , which recognize activated SIPK and WIPK ( wound-induced protein kinase ) . Expression of NopM in N . benthamiana did not result in MAP kinase activation . A treatment of leaves with flg22 for 15 min caused MAP kinase activation in control plants transformed with the empty vector as well as in NopM or NopM-C338A expressing plants , indicating that NopM did not block flg22-induced MAP kinase signaling ( Figure S1 in Text S1 , panel C ) . In the presence of NopM and flg22 , activation of SIPK might be slightly stronger , but a more quantitative approach would be required to reveal small changes in MAP kinase activation .
We show in this study that the LRR protein NopM of the rhizobial symbiont NGR234 is an E3 ubiquitin ligase belonging to the IpaH effector family . Effectors of this family are also known as NEL ( novel E3 ligase ) domain effectors . These enzymes are structurally unrelated to other bacterial E3 ubiquitin ligases , which have a HECT or RING/U-box domain [38] . The NEL domain in the C-terminal region of NopM was functional in NopM , whereas the NopM-C338 mutant protein was inactive . The C338 residue of NopM likely acts as a nucleophile , forming a thioester bond with ubiquitin . Enzymatic activities of NEL domain effectors have been only reported for the human pathogens S . flexneri and S . enterica [20]–[24] . Thus , NopM represents a first studied example for a NEL domain effector delivered into plant cells . Inoculation tests with the constructed NGR234 mutants , NGRΩnopM and NGRnopM ( C338A ) , showed reduced nodulation on L . purpureus , indicating the importance of the C338 residue during establishment of symbiosis . We suggest that NopM functions as an E3 ubiquitin ligase during the infection process and that ubiquitination of one or more host proteins helps to promote nodulation on L . purpureus . In some other host plants , however , effects of NopM on nodulation were either not observed or even negative ( [15] and this study ) . It is tempting to speculate that NopM function reflects an evolutionary adaptation to protein substrates of specific hosts . Indeed , NGR234 has been originally isolated from L . purpureus [39] whose nodulation is promoted by NopM . Negative effects of NopM on nodulation in other legumes are potentially related to specific R-proteins of the host plant as shown for T3SS-dependent nodulation of certain soybean cultivars [18] . Similar to many Gram-negative pathogenic bacteria , rhizobial T3SSs are believed to deliver type 3 effector proteins into host cells . Translocation of rhizobial type 3 effectors into legume cells has been questioned [40] . However , recent evidence has provided strong support , based on transfer of adenylate cyclase fused to rhizobial effectors [41] , [42] . Our findings indicate that NopM , but not NopM-C338A , promoted nodule formation in L . purpureus . In fact , delivery into host cells is a prerequisite for bacterial E3 ubiquitin ligases , as they function in combination with ubiquitin and E1/E2 enzymes , which are present only in the eukaryotic cell . Yeast is a model to investigate effects of type 3 effector proteins in eukaryotic cells [43] . The IpaH9 . 8 effector of S . flexneri blocked the mating pheromone response signaling in yeast and ubiquitinated Ste7 , the mitogen-activated protein kinase kinase of this pathway [20] , [21] . Our data point to a similar activity of NopM in yeast and these findings prompted us to investigate whether NopM can interfere with MAP kinase signaling in plants . Interestingly , N . benthamiana plants expressing NopM did not show suppression of flg22-induced MAP kinase signaling . Instead , the flg22-associated ROS burst was nearly completely abolished in plants expressing NopM . These findings are consistent with the concept that early flagellin signaling is split in two separate signaling branches , one leading to MAP kinase activation and the other to calcium-dependent protein kinase ( CDPK ) mediated ROS production [34] , [37] . Suppression of flg22-triggered ROS in N . benthamiana depended on the C338 residue of NopM , suggesting ubiquitination of a MAMP signaling component . The E3 ubiquitin ligase activity of NopM is reminiscent of the virulence function of AvrPtoB , a type 3 effector of P . syringae with a functional E3 ubiquitin ligase RING/U-box E3 domain [44] . In Arabidopsis thaliana , AvrPtoB targets proteins such as the flagellin receptor complex FLS2-BAK1 [45] and the chitin receptor kinase CERK1 [46] . In contrast to ROS suppression , expression of either NopM or NopM-C338A in N . benthamiana promoted flg22-triggered accumulation of NbCyp71d20 transcripts . Hence , ubiquitination activity of NopM was not essential to induce this effect . We suggest that an interaction between NopM and a N . benthamiana protein is sufficient to cause a partial deregulation of immune signaling in flg22-challenged tissue . Taken together , we provide genetic and biochemical evidence that NopM is a type 3 effector with a functional NEL domain . Inoculation tests with the constructed point mutant NGRnopM ( C338A ) suggest that the E3 ubiquitin ligase activity of NopM is required for optimal nodulation of the host plant L . purpureus . When expressed in N . benthamiana , NopM suppresses the flg22-elicited ROS burst , suggesting that NopM blocks ROS-associated defense responses . Future work is required to test whether NopM can also suppress ROS formation in legume roots . Indeed , ROS generation could be detrimental during the rhizobial infection process [5] , [47] and it is tempting to speculate that NopM keeps ROS generation in L . pupurpureus infection threads below a harmful threshold level .
Bacterial strains and plasmids used for this study are listed in Table S1 of Text S1 . Plasmids were constructed according to standard methods and corresponding PCR primers are listed in Table S2 of Text S1 . The sequence encoding NopM of Rhizobium ( Sinorhizobium fredii ) sp . NGR234 ( accession number AAB91674 ) was cloned into the pET28b vector , resulting in plasmid pET-nopM . PCR-based site-directed mutagenesis was used to mutate the cysteine 338 ( TGT codon ) of NopM into alanine ( GCT codon ) and the resulting plasmid was named pET-nopM ( C338A ) . The plasmids were then transformed into E . coli BL21 ( DE3 ) cells . The His-tagged NopM and NopM-C338A proteins were purified from isopropyl-β-D-thiogalactopyranoside ( IPTG ) induced cultures by nickel affinity chromatography with Ni-NTA resin beads ( Qiagen , Hilden , Germany ) . For immunization of a New Zealand rabbit , Ni-NTA purified His-tagged NopM was separated by SDS-PAGE and gel bands containing NopM were cut from the gel . Purified ubiquitin-activating enzyme from human ( E1 ) , UbcH5B ( E2 ) , and HA-ubiquitin were purchased from Boston Biochem ( Cambridge , MA , USA ) . His-tagged NopM and NopM-C338A from E . coli cultures grown at 27°C for 12 h were purified by nickel affinity chromatography according to the manufacturer's recommendations under non-denaturing conditions ( Qiagen , Germany ) . Ubiquitination assays were performed in a 40-µl volume containing the reaction buffer ( 25 mM Tris HCl ( pH 7 . 5 ) , 50 mM NaCl , 5 mM ATP , 10 mM MgCl2 , 0 . 1 mM DTT ) , 2 µg HA-ubiquitin , 0 . 5 µg of E1 , and 2 µg of UbcH5B in the presence or absence of 1 µg of His-tagged NopM , or NopM-C338A , respectively . Reactions were incubated at 37°C for 1 h and stopped by addition of an equal volume of Laemmli sample buffer ( 62 . 5 mM Tris HCl ( pH 6 . 8 ) , 10% ( v/v ) glycerol , 2% ( w/v ) SDS , 0 . 005% ( w/v ) bromophenol blue ) containing 100 mM DTT . Reaction mixtures were separated by SDS-PAGE , transferred onto a nitrocellulose membrane , and probed with specific antibodies ( anti-NopM antibodies at 1∶10 000 dilution; anti-HA antibodies from ( Abcam , England ) at 1∶ 4 000 dilution ) . Immunoblots were developed with enhanced chemiluminescence reagents ( GE Healthcare ) . For construction of the mutant NGRΩnopM , a 2 . 5-kp fragment containing nopM was cloned into pBluescript II KS ( + ) , generating pSK-nopM2500 . PCR-based site-directed mutagenesis was used to generate a BamHI restriction site close to the ATG codon of nopM . A spectinomycin-resistant ( Spr ) Ω interposon was excised from pHP45 [48] with BamHI and ligated into the BamHI site , generating pSK-nopMΩ . The construct was then cloned into the suicide vector pJQ200SK [49] . The resulting plasmid ( pJQ-nopMΩ ) was mobilized from E . coli DH5α into Rhizobium sp . NGR234 by triparental mating using the pRK2013 helper plasmid [50] . Gene replacement was forced by selecting for the resistance of the Ω interposon marker ( Spr ) and for growth on 5% ( w/v ) sucrose . The obtained mutant NGRΩnopM was confirmed by Southern blot analysis using the DIG DNA labeling and detection kit as specified by the supplier ( Roche , Basel , Switzerland ) . For construction of NGRnopM ( C338A ) , plasmid pSK-nopM2500 was mutated by a PCR-based site-directed mutagenesis approach , thereby creating the restriction site Aor51HI . The insert of this plasmid ( named pSK-nopM ( C338A ) ) with DNA encoding NopM-C338A was then cloned into the suicide vector pJQ200SK , resulting in plasmid pJQ-nopM ( C337A ) . After conjugation , Rhizobium sp . NGR234 bacteria were first cultivated on agar plates containing gentamycin and rifampin and then on plates containing rifampin and 5% ( w/v ) sucrose . Genomic DNA from candidate colonies served as a template for a PCR ( primers 11 and 12; Table S2 in Text S1 ) . The amplicon from the mutant NGRnopM ( C338A ) was completely cleaved by Aor51HI into two smaller fragments . For complementation analysis of the constructed mutants , a 2081-bp fragment containing the coding region and promoter sequence of nopM was cloned into pFAJ1702 [51] . The obtained plasmid ( pFAJ-nopM ) was then mobilized into Rhizobium sp . NGR234 and selection was performed on agar plates containing tetracycline . Secreted proteins from culture supernatants from Rhizobium sp . strains NGR234 ( parent strain ) , NGRΩnopM ( this study ) , NGRnopM ( C338A ) ( this study ) , NGRΩnopM carrying pFAJ-nopM ( this study ) and NGRΩrhcN [11] were isolated according to a previously described procedure [12] , [52] . Briefly , cultures ( RMS medium ) were induced with 1 µM apigenin and cultivated at 27°C on a rotary shaker for 40 h . Proteins from culture supernatants were precipitated by addition of TCA ( 10% , w/v ) and incubation over night at 4°C . After centrifugation ( 10 000× g , 4°C , 30 min ) , precipitates were washed twice with 5 ml of cold 80% acetone and resuspended in 100 µl of rehydration buffer ( 8 M urea , 2% w/v CHAPS , 0 . 01% w/v bromophenol blue ) . Secreted proteins ( corresponding to 100 ml of cell culture ) were subjected to immunoblot analysis with antiserum against NopM ( 1∶5 000 dilution ) followed by staining with chemiluminescence reagents ( Thermo Scientific , Waltham , MA USA ) . Nodulation tests were performed in plastic jars using the host plants Lablab purpureus cv . Chaojibiandou , Phaseolus vulgaris cv . Yudou No 1 , and Flemingia congesta . Seeds were surface sterilized and germinated on agar plates , and plantlets were transferred to 300-mL plastic jar units linked with a cotton wick ( a mixture of vermiculite and expanded clay in the upper vessel; nitrogen-free nutrient solution in the lower vessel ) . Plants ( 1 plant per jar ) were inoculated with 109 bacteria ( strain NGR234 and mutant derivatives; see Table S1 in Text S1 ) . Plants were cultivated at 26±2°C in a temperature-controlled greenhouse . The nodulation test results were statistically analyzed with the Kruskal-Wallis rank sum test , which is suitable for unequal replications . A P-value of ≤0 . 01 was considered as significant . All data are presented as means ± SE ( standard error ) . Standard media and techniques were used for transformation , maintenance , and growth of Saccharomyces cerevisiae [53] . Strains ( haploid strain W303-1A ( MATa ) strain SY2227 ) and constructed plasmids encoding NopM or NopM-C338A are listed in Table S1 of Text S1 . For immunoblot analysis , yeast cells were cultured at 30°C in liquid SD/-Leu medium ( Clontech ) supplemented with 2% galactose . Membranes were incubated with anti-NopM antibodies at a 1∶5 000 dilution and blots were developed with 3 , 3′-diamino-benzidine ( Boster , Wuhan , China ) . The halo assay with the mating pheromone was performed by placing a filter disk impregnated with 8 µg of the mating pheromone α-factor ( Sigma-Aldrich; dissolved in 8 µl H2O ) to the center of each agar plate . The plates were sealed , incubated at 27°C for 1 week and then photographed . Plasmids ( pCAMBIA-nopM , pCAMBIA-nopM ( C338A ) , the empty vector pCAMBIA-T and pGWB417-HopQ1-myc; see Table S1 in Text S1 ) were transformed into chemically competent Agrobacterium tumefaciens strain GV3101 by heat shock . Leaves from 4-week old Nicotiana benthamiana plants were infiltrated with bacteria ( OD600 = 0 . 5 ) re-suspended in infiltration buffer ( 10 mM MgCl2 , 10 mM MES pH 5 . 6 ) . Expression of NopM was detected by immune blot analysis with anti-NopM antibodies at a 1∶1000 dilution . Blots were developed with CDPstar reagents ( New England Biolabs ) . Staining of N . benthamiana leaves was performed with trypan blue as described previously [54] . Leaf discs ( 0 . 38 cm2 ) were floated on water overnight and ROS released by the leaf tissue were measured using a chemiluminescent assay [35] . The water was replaced with 500 µl of an aqueous solution containing 20 µM luminol ( Sigma-Aldrich ) and 1 µg of horseradish peroxidase ( Fluka , Buchs , Switzerland ) . ROS was elicited with 1 µM flg22 peptide ( QRLSTGSRINSAKDDAAGLQIA ) in all experiments . Mock treatments without flg22 were performed with the BSA/NaCl solution ( 1% w/v BSA , 1% w/v mM NaCl ) used to solubilize flg22 . Luminescence was measured over a time period of 30 min using a luminometer ( MicroLumat LB96P; EG&G Berthold ) . Data from 12 leaf disks derived from 4 independent infiltrations were statistically analyzed by one-way ANOVA considering P≤0 . 05 as significantly different . Two days post A . tumefaciens transformation , N . benthamiana leaves transiently expressing NopM , NopM-C338A and empty vector controls were infiltrated with 1 µM flg22 peptide or mock-treated with BSA/NaCl for 15 min . Leaf discs ( 50 mg ) were then frozen in liquid nitrogen and proteins were extracted in 100 µl extraction buffer ( 50 mM Tris-HCl pH 7 . 5 , 150 nM NaCl , protease inhibitor cocktail from Sigma-Aldrich ) for 30 min at 4°C . Subsequently 100 µl Lämmli loading buffer ( 2× ) was added to each sample . Samples were subjected to immunoblot analysis using the anti-p42/44-phospho-ERK antibody ( Sigma-Aldrich ) . Blots were developed using CDP-star technology ( NEB ) . One day post infiltration , N . benthamiana leaf discs expressing NopM , NopM-C338A or the empty vector ( EV ) control were collected , and then floated overnight in water . Leaf discs were subsequently treated with 1 µM flg22 or mock-treated with BSA/NaCl solution for 30 min and then frozen in liquid nitrogen . Total RNA was extracted using the NucleoSpin RNA Plant extraction kit ( Machery-Nagel ) . The absence of genomic DNA was checked by PCR amplification of the housekeeping NbEF1α gene by using 1 µg of RNA ( NbEF1α amplification crosses an exon/intron boundary ) . For analysis of gene expression , first-strand cDNA was synthesized from 1 µg of RNA using AMV reverse transcriptase ( Promega ) and an oligo ( dT ) primer ( Microsynth ) , according to the manufacturer's instructions . For quantitative PCR , 5 µl of a 1/100 µl dilution of cDNA were combined with SYBR master mix . PCRs were performed in triplicates with the 7500 Real Time PCR system ( Applied Biosystems ) . Data were collected and analyzed with the respective ABI analyzing program . The NbEF1α RNA was analyzed as an internal control and used to normalize the values for transcript abundance . All samples were related to the empty vector ( EV ) control . Primers for the genes NbCyp71D20 , NbAcre31 and NbEF1α are listed in Table S2 of Text S1 . Data derived from three biological repeats were statistically analyzed by ANOVA ( one-way ANOVA ) considering P≤0 . 05 as significantly different . NopM of Rhizobium ( Sinorhizobium fredii ) strain NGR234: AAB91674 | Many Gram-negative bacterial pathogens possess type 3 secretion systems , which deliver effector proteins into eukaryotic host cells through needle-like structures . Effectors manipulate the host cell and many of them suppress host defense responses . Interestingly , certain symbiotic strains of rhizobia also possess such secretion systems . Rhizobia infect legume roots and induce root nodules , where the bacteria convert atmospheric nitrogen into ammonia . Here , we characterize the effector NopM of Rhizobium sp . strain NGR234 . We demonstrate that NopM possesses E3 ubiquitin ligase activity , indicating that NopM can “tag" proteins with ubiquitin , and thus target them for proteasome-dependent degradation . Using a mutant approach , we demonstrate that enzymatically active NopM promotes establishment of symbiosis with Lablab purpureus , the host plant from which NGR234 was originally isolated . We further examine effects of NopM when directly expressed in eukaryotic cells and show that NopM interferes with specific signaling pathways . NopM expressed in the model plant Nicotiana benthamiana dampened generation of reactive oxygen species ( ROS ) , which are formed in response to the bacterial flagellin peptide flg22 . We suggest that NopM promotes nodule initiation by reducing the levels of harmful ROS during the infection process . | [
"Abstract",
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] | [
"biology"
] | 2012 | Functional Analysis of NopM, a Novel E3 Ubiquitin Ligase (NEL) Domain Effector of Rhizobium sp. Strain NGR234 |
Mimivirus and Megavirus are the best characterized representatives of an expanding new family of giant viruses infecting Acanthamoeba . Their most distinctive features , megabase-sized genomes carried in particles of size comparable to that of small bacteria , fill the gap between the viral and cellular worlds . These giant viruses are also uniquely equipped with genes coding for central components of the translation apparatus . The presence of those genes , thought to be hallmarks of cellular organisms , revived fundamental interrogations on the evolutionary origin of these viruses and the link they might have with the emergence of eukaryotes . In this work , we focused on the Mimivirus-encoded translation termination factor gene , the detailed primary structure of which was elucidated using computational and experimental approaches . We demonstrated that the translation of this protein proceeds through two internal stop codons via two distinct recoding events: a frameshift and a readthrough , the combined occurrence of which is unique to these viruses . Unexpectedly , the viral gene carries an autoregulatory mechanism exclusively encountered in bacterial termination factors , though the viral sequence is related to the eukaryotic/archaeal class-I release factors . This finding is a hint that the virally-encoded translation functions may not be strictly redundant with the one provided by the host . Lastly , the perplexing occurrence of a bacterial-like regulatory mechanism in a eukaryotic/archaeal homologous gene is yet another oddity brought about by the study of giant viruses .
The first giant virus , Mimivirus , was discovered ten years ago [1] . This double stranded DNA virus infecting amoebae of the Acanthamoeba genus exhibits a record-breaking particle more than 700 nm in diameter and a 1 . 2 Mb genome , larger than several cellular genomes [2] . Remarkably this virus possesses 1018 genes [3] , i . e . twice the number of genes found in the bacteria Buchnera aphidicola [4] , the archaea Nanoarchaeum equitans [5] or the eukaryotic endosymbiotically derived Hemiselmis andersenii nucleomorph [6] . Importantly several genes of this giant virus encode functions previously thought to be hallmarks of the cellular world , the most striking being central components of the translation machinery . For instance the Mimivirus genome encodes 4 different aminoacyl-tRNA synthetases that specifically attach amino acids to their cognate tRNAs . Transcriptome analyses showed that these genes are expressed in a regulated manner during the viral replication cycle [7] , thus making them unlikely to be pseudogenes . Moreover functional and structural studies of the Mimivirus Methionyl- and Tyrosyl-tRNA synthetases proved that they are genuine functional enzymes [8] . However , the loop involved in the recognition of the tRNA anticodon by the Tyrosyl-tRNA synthetase is shorter in Mimivirus , suggesting that only two bases are recognized rather than three in the cellular enzymes [8] . So not only do giant viruses' genomes encode unexpected genes , but these genes are clearly different from their known cellular counterparts , ruling out a simple horizontal gene transfer ( HGT ) . Collectively these elements fuelled the debate on the origin of giant viruses , on their living or nonliving condition [9] , [10] , and whether they belong to a 4th domain of life as some authors even claimed [11] , [12] . Two main scenarios can explain the presence of cell-specific genes in a virus . On the one hand this can be due to massive horizontal gene transfers between the host ( or its intracellular parasites ) and the virus [13] . On the other hand this could be the result of the reductive evolution of an ancient more complex cellular ancestor [14] . Our recent discovery of Megavirus , a new giant virus relative of Mimivirus shed some light on these fundamental issues . Megavirus has a larger capsid , longer genome and wider gene content than Mimivirus or any other characterized virus to date [15] . Importantly , all the Mimivirus genes involved in translation have an ortholog in Megavirus . Furthermore three additional aminoacyl-tRNA synthetases were found in this new giant virus . It then becomes very unlikely that the translation-related genes found in the Mimivirus and Megavirus genomes were acquired by HGT . A more parsimonious scenario is simply that these genes were already present in the common ancestor of Mimivirus and Megavirus , leading to the hypothesis that this ancestor was endowed with an even more complete translation apparatus , inherited from an ancestral cellular organism [12] , [16] . We reasoned that further studying other giant virus-encoded translation components might provide additional insights on the nature of this ancestor . Translation of messenger RNAs into proteins is a complex and multistep process . It involves three major stages: initiation , elongation and termination . It is noteworthy that Mimivirus and Megavirus encode 5 orthologous genes , in addition to the aminoacyl-tRNA synthetases , that are involved in these three phases [2] , [15] . This suggests that a tight control of the translation process is required for the optimal progress of the virus replication cycle , and that the virally-encoded factors function in a way that cannot be assumed by their cellular counterparts . Each of the above steps is essential for optimal protein synthesis . Accurate termination for instance allows correct decoding of the mRNA , as well as promotes the proper dissociation and recycling of the ribosomes . Two functional classes of release factors ( RFs ) mediate translation termination ( summarized in Table S1 ) . The class-I RF recognizes the stop codon located in the ribosomal A-site and then releases the polypeptide chain , assisted by the class-II GTPase RF . There are two class-I RFs in bacteria , RF1 which recognizes UAA/UAG stop codons , and RF2 which recognizes UAA/UGA stop codons . In eukaryotes and archaea , there is a single omnipotent class-I RF called eRF1 and aRF1 respectively , capable of recognizing all three stop codons . Whereas eRF1 and aRF1 share conserved sequence motifs and are functionally and structurally related , they are highly divergent in sequence and structure from the bacterial RF1/RF2 [17] , [18] , with the exception of a uniquely conserved GGQ motif . The class-II GTPase RFs , called RF3 in bacteria and eRF3 in eukaryotes are also unrelated , and do not exhibit sequence similarity apart from their GTPase domain [19] . In addition , eRF3 is an essential gene in eukaryotes while RF3 is lacking in some bacterial lineages [20] . They also function differently: whereas eRF3 and eRF1 physically interact to release the peptide [21] , RF3 interacts with the ribosome to remove RF1/RF2 from the A site [22] . Finally , although the eukaryotic eRF1 and the archaeal aRF1 class-I RFs are closely related , no obvious eRF3 class-II ortholog could be found in archaeal genomes . This has been puzzling for a long time until the discovery that the omnipotent archaeal elongation factor 1 α ( aEF1α ) is able to bind aRF1 and functions as a class-II RF [23] , [24] . In summary , although the function of RFs is as universal as the stop codons , the proteins involved in the termination of translation are clearly different between bacteria on the one hand , and eukaryotes and archaea on the other . Translation termination is globally highly accurate but occasionally leads to unfaithful decoding of the gene sequence . Mis-terminations of polypeptide , the so-called translational recoding events , are of two types: the “stop codon readthroughs” and the frameshifts [25] . Readthroughs are caused by the binding of an aminoacyl-tRNA in lieu of a release factor when the ribosome encounters the stop codon . This leads to translation proceeding in the same frame upstream and downstream of the stop codon . A near-cognate tRNA such as the glutamine tRNA ( close to the UAG stop codon ) or the tryptophan tRNA ( close to the UGA stop codon ) can be incorporated [26] . Alternatively a cognate but non-standard tRNA can be involved , for instance the tRNA suppressors [27] and the selenocysteine tRNAs [28] . The other type of error , translational frameshift , is caused by a leap of one or two nucleotides leading to the pursuit of translation , albeit in a different reading frame . The occurrence of such mis-terminations can be programmed to act as a powerful regulatory mechanism . One of the most elegant genetic switches involves a programmed translational frameshift in the bacterial RF2 class-I RF [29] . In 70% of surveyed bacteria , RF2 appears to be composed of two partially overlapping open reading frames ( ORFs ) [30] . The first ORF terminates by a UGA stop codon , immediately followed by a second ORF ( in a +1 frame ) encoding the rest of the protein . When functional RF2 is plentiful , a high proportion of ribosomes terminates at UGA to synthesize a short non-functional N-terminal RF2 peptide . Since full-length RF2 is then in limited amount , the normal processing of the UGA stop codon ( peptide release ) is stalled , enhancing the probability of a frame shift , and thereby favoring the translation of a complete functional RF2 protein . This negative feedback loop , exclusively found in the bacterial RF , can thus buffer RF2 concentration and enable subtle controlling of translation termination [29] , [31] . In this study , we started from the discovery that the class-I translation RFs homologs present in the Mimivirus ( R726 gene ) and Megavirus ( mg280 gene ) giant viruses had been wrongly annotated . We then established the correct structure of these genes by predicting a unique combination of two recoding events: a readthrough and a frameshift , shared by both viral genes . Further computational analyses as well as several lines of experimental evidences validated the new gene structure and the recoding events , which can thus act as autoregulatory elements . Unexpectedly , these viral class-I RF homologs uniquely combine regulatory features specific to the bacterial domain with a clear sequence resemblance with class-I RFs of the eukaryotic and archaeal types . Once again this raises the question of the origin and evolution of the translation components found in giant viruses .
Mimivirus R726 gene is annotated as a class-I peptide chain release factor . According to previously published transcriptomic data [7] its 5′UTR is 640 nt long , which makes it the longest 5′UTR among the 979 Mimivirus protein-coding genes . R726 5′UTR length is 20 . 5 standard deviations above the average of 12 . 5 nt . This anomalous 5′UTR length prompted us to reexamine the initial annotation of R726 . Predictions of unusually large 5′ UTR ( see Figure 1A ) most often arise from mistakes in the definition of the transcript boundaries , in this case however , several elements argue against such an explanation . First , known transcriptional regulatory elements flank the predicted transcript while none were found inside it ( Figure 1B ) . Furthermore , our 454 RNA-seq data ( from [7] ) covered the entire R726 transcript and thus supported the annotation ( see Figure 1C ) . The incorporation of 5′ and 3′ specific tags at the extremities of the cDNAs allowed us to precisely map transcriptional start sites ( TSS ) and transcriptional end sites ( TES ) ( see [7] for details ) . Figure 1C again shows that R726 TSS and TES coincide with the annotated transcript boundaries . Finally , an independent and strand-specific dataset from total RNA sequenced on the SOLiD plateform ( from [3] ) confirmed the transcript boundaries as well ( Figure 1D ) . Altogether these results indicate that the R726 transcript annotation is correct . A second possibility is that the abnormally long 5′UTR arose from an error in the prediction of the R726 protein sequence . For instance , an upstream methionine codon could constitute the actual translation initiation . We explored this possibility by searching for R726 homologous sequences in the UniProtKB/Swiss-Prot database using the blast program and the R726 genomic sequence as a query . The 10 best matching proteins ( with an E-value<1e−27 ) are shown in Figure 1E . Two findings emerged from this test . First , the sequence similarity at the protein level was clearly not restricted to the annotated coding region but covered the entire R726 transcript sequence . Second , the alignments of the matching proteins were systematically split between two alternative frames . This suggested that the actual R726 coding region started upstream of the bioinformatic prediction and involved a frameshift . Potential start and stop codons in the three frames are shown in Figure 1F , as well as the most parsimonious path to encode a protein more fully homologous to the other release factors . This resulted into a new gene model ( see Figure 1G ) encoding a full-length protein via two recoding events: a readthrough of the first encountered stop codon in the 5′ ORF , and a frameshift at the next downstream stop codon . To eliminate the trivial possibility that these two stop codons were due to errors in the R726 gene sequence , we first re-sequenced the R726 genomic region using traditional Sanger sequencing . In addition we exploited our very high coverage SOLiD re-sequencing of Mimivirus genomic DNA ( from [3] ) . The R726 genomic sequence was found to be identical in both cases ( Figure S1 ) , including the predicted readthrough and frameshift stop codons . We then examined the more remote possibility that the mRNA sequence could differ from the genomic sequence following RNA editing . For this we first sequenced R726 cDNAs using Sanger sequencing ( see Figure S1 ) . In addition , we mapped the RNA-seq data from two independent experiments from polyadenylated [7] and total RNA [3] to the R726 genomic region . Figure S1 clearly shows that the R726 transcript sequence is identical to the genomic sequence . Therefore the two stop codons must be present at the mRNA level . An alternative explanation for the odd R726 coding sequence could be that the Mimivirus gene is a pseudogene . However , the two previously described RNA-seq datasets ( from [7] and [3] ) consistently ranked R726 as one of the most expressed Mimivirus genes during the replication cycle . Indeed R726 is in the highest quartile of total gene expression ( Figure S2 ) . Furthermore , the R726 ortholog in the Megavirus genome ( mg280 ) presents exactly the same gene structure pattern ( see Figure 2A ) , that is first a readthrough followed by a downstream frameshift in the 5′ region of the gene . It is worth noting that the readthrough stop codon ( UGA ) is strictly conserved between the two viruses , while the Mimivirus UAG frameshift stop codon is substituted by a UAA stop codon in Megavirus . Once reconstructed , the full-length protein sequence from Mimivirus ( R726 ) and Megavirus ( mg280 ) exhibited 47% of identical residues , a percentage comparable to the average sequence similarity of the Mimivirus/Megavirus orthologous protein pairs [15] . The fact that the stop codons and the recoding pattern are conserved between R726 and mg280 despite their level of sequence divergence , strongly suggests that they are translated as predicted here and function as proteins . Furthermore , as Mimivirus and Megavirus only share 50% of their genes [15] , it would be unlikely for these two orthologous ORFs to be conserved if they were in fact pseudogenes . According to our hypothesis , the production of a functional R726 protein requires translation to occasionally proceed beyond the readthrough stop . We thus examined whether this stop codon was likely to be read through . A crucial factor for readthrough to occur is not the stop codon sequence per se ( UAA , UAG or UGA ) but rather the sequence context around it . For instance the first nucleotide downstream of the stop codon is known to be the strongest determinant of readthrough efficiency [32] . We thus compared the tetranucleotides composing Mimivirus and Megavirus readthrough stop codons with available experimental data of readthrough efficiency measurements in eukaryotes ( S . cerevisiae in [32] ) . As shown in Figure 2A and Figure 2B , the UGA-C tetranucleotide of Mimivirus and Megavirus readthrough stop codons is very efficiently read through , i . e . it is a weak terminator . Conversely the two tetranucleotides encompassing the genuine 3′ stop codons ( UAA-U in Mimivirus and UAA-A in Megavirus ) are not favorable to readthrough . If UAA-U and UAA-A really efficiently terminate polypeptide chains while UGA-C promotes frequent readthrough in giant viruses , the Mimivirus and Megavirus stop codons should exhibit a tetranucleotide usage reflecting this bias . As expected , Figure 2C shows that the tetranucleotides of R726 and mg280 genuine stops are among the most frequently used whereas the tetranucleotide of the readthrough stop is very rarely used . We then went on the experimental confirmation that the Mimivirus R726 first stop can be read through . Since no usable system for protein expression in the Mimivirus host ( Acanthamoeba castellanii ) is currently available , we used Escherichia coli as expression host . We reasoned that the occurrence of such recoding events in this organism makes the demonstration possible [33] . Furthermore , the strength of the termination in E . coli depends on sequences that are similar to the ones in eukaryotes [32] , [34]–[36] . We thus first cloned the full-length gene , i . e . containing the readthrough stop and the frameshift stop , into a modified pET vector in frame with an N-terminal 6×His-SUMO tag ( Figure 3A , R726 WT construct ) . We then performed site-directed mutagenesis to get rid of the frameshift stop by removing the first nucleotide of the UAG stop codon to create a +1 translational frameshift . The resulting construct ( R726 FS mutant ) corresponds to the R726 gene containing only the readthrough stop , in frame with a 6×His-SUMO tag ( Figure 3A ) . The R726 FS mutant was then transformed in E . coli for protein expression . The proteins were purified by Nickel affinity chromatography and the elution fraction was analyzed by western blot using antibodies raised against the 6×His tag of the potentially produced proteins . The western blot revealed two bands running around 20 KDa and 60 KDa ( Figure 3B and Figure S3A ) , possibly corresponding to the expected protein products from the R726 FS construct: a short peptide ending at the readthrough stop ( Figure 3A , P1 ) and a full-length protein product resulting from the readthrough of this first stop codon ( Figure 3A , P3 ) . We incubated the elution fraction with the Prescission protease which should cleave the two products if they include the 6×His-SUMO tag . As expected , the 20 KDa and the 60 KDa proteins were no longer detected after cleavage showing that they correspond to the P1 and P3 predicted R726 gene products . In addition , the double mutant construct lacking the two stop codons ( Figure 3A , R726 DM construct ) corresponding to the R726 full-length product and the 60 KDa product migrate at the same position on the gel . These results demonstrate that in E . coli readthrough can occur at the first stop of the Mimivirus R726 gene . We then investigated which amino-acid was incorporated at the first R726 stop codon . In some organisms the UGA stop codon , such as the R726 and mg280 readthrough stops , leads to the incorporation of a selenocysteine ( Sec ) . We failed to identify Sec tRNAs in the Mimivirus and Megavirus genomes . However , we found that the A . castellanii genome encodes a highly expressed Sec tRNA ( see Figure S4 and Figure S5 ) . Similarly , the protein machinery required for Sec insertion is lacking from the Mimivirus and Megavirus genomes but is present in A . castellanii ( see Table S2 ) . Finally , we looked for genes targeted by the Sec incorporation machinery based on the presence of a specific Sec insertion sequence ( SECIS ) element . SECIS elements were indeed found in the 3′UTR of A . castellanii genes encoding homologs to known selenoproteins ( see Table S3 ) , and correlating with the presence of UGA stop codons . By contrast , SECIS elements were found neither in Mimivirus genes encoding homologs to known selenoproteins nor in the R726 gene . Taken together these results suggest that selenocysteine incorporation occurs in A . castellanii but not in Mimivirus . As no cognate tRNA decodes the first R726 stop codon we searched for natural near-cognate tRNAs . Among the only two types of tRNAs shared by Mimivirus and Megavirus ( leucine and tryptophan ) , tryptophan tRNAs ( Trp-tRNAs ) was previously shown to recognize UGA stop codons [37] , [38] . Furthermore , Mimivirus Trp-tRNA is one of the most expressed tRNAs from the Mimivirus/A . castellanii system ( see Figure S5 ) . Interestingly , Mimivirus ( and Megavirus ) Trp-tRNA exhibits an adenine in the D arm that is similar to the mutation in the well-studied Hirsh suppressor ( see Figure S6 ) [39] . This E . coli tRNA derived from a Trp-tRNA recognizes UGA stop codons through a G-to-A mutation in the D arm . Given these congruent elements , we hypothesized that tryptophan is the most likely amino acid to be incorporated at the readthrough stop in Mimivirus and Megavirus class-I RFs . We predict that once the ribosome proceeds beyond the readthrough stop , a frameshift should occur at the downstream stop to produce a functional class-I RF in Mimivirus and Megavirus . Similarly to readthrough recoding events , the frequency of ribosomal frameshifting is highly dependent on the surrounding sequences . Again , it has been shown that the first base downstream of the stop codon is correlated with the frequency of frameshifting [34] . Therefore , we compared the tetranucleotides at both Mimivirus and Megavirus frameshift stops with experimentally determined translational frameshifting efficiency in eukaryotes ( S . cerevisiae from [36] ) . Figure 2A and Figure 2D show that the Mimivirus frameshift stop tetranucleotide ( UAG-C ) and the Megavirus one ( UAA-C ) are amongst the most efficient frameshifting inducers . By contrast , frameshifting frequency is low at the genuine stops ( UAA-U in Mimivirus and UAA-A in Megavirus ) . The tetranucleotide usage in Mimivirus genes stop codons strengthens this observation . As shown in Figure 2C , UAG-C is used at a rate of less than 1% as a translation termination signal in Mimivirus , whereas UAA-U is the most frequently used tetranucleotide ( more than 30% ) . We observed the same trend in Megavirus ( Figure 2C ) . In addition the full “CUU UAG C” motif in Mimivirus and “CUU UAA C” in Megavirus are similar to the conserved “CUU UGA C” shifting motif found in the bacterial RF2 programmed frameshift [30] . Collectively these results support the occurrence of frameshifting recoding events in R726 and mg280 . To experimentally address whether the second stop codon in R726 is prone to frameshifting , we performed site-directed mutagenesis on the wild-type gene to get rid of the readthrough stop . The R726 readthrough stop was thus replaced by a tryptophan , resulting in a construct containing a 6×His-SUMO tag in frame with the 5′ part of the R726 gene ( Figure 3A , R726 RT mutant ) . There are two protein products expected from this construct: a small protein that ends at the frameshift stop ( Figure 3A , P2 ) and a full-length protein resulting from a frameshift recoding event at this locus ( Figure 3A , P3 ) . The plasmid was transformed in E . coli for protein expression . The proteins were then purified by Nickel affinity chromatography and the elution fraction was analyzed by SDS-PAGE and western blotting . The western blot revealed the two expected bands , one corresponding to a 25–30 KDa protein and a second band around 60 KDa ( Figure 3C and Figure S3B ) . We thus incubated the elution fraction with the Prescission protease and , as expected , the two bands disappeared , supporting that they correspond to the P2 and P3 protein products , respectively . Moreover , the 60 KDa band was detectable on a Coomassie blue stained gel ( Figure S3B ) , which allowed us to analyze it by mass spectrometry . We demonstrated without ambiguity ( E-value = 9 . 4e−17 ) that it corresponded to the full-length 6×His-SUMO R726 protein . The identified peptides covered 58% of the full-length protein , from its N-terminal to its extreme C-terminal ( Figure S7 ) . This result clearly shows that +1 translational frameshifting can occur at the R726 second stop in E . coli . At this point we experimentally demonstrated that translation can proceed beyond the two stop codons independently ( the readthrough stop and the frameshift stop ) . Finally , the wild-type gene was expressed to verify whether its translation would result in the predicted full-length R726 protein . The purified product was analyzed by western blot ( Figure 3D and Figure S3C ) and revealed the three expected bands: one highly expressed of 20 KDa , one in the 25–30 KDa range and the 60 KDa full-length protein . Prescission digest of the purified fraction showed that the three bands correspond to the P1 , P2 and P3 protein products , respectively . Altogether these results demonstrate that a full-length R726 protein can be produced from the wild-type Mimivirus gene . We showed that the R726 Mimivirus gene is able to bypass its two internal stop codons and produce a full-length protein , although it remains to be verified whether this protein is a genuine peptide chain release factor . Homology searches using the blast program against the UniProtKB/Swiss-Prot database identified class-I RFs from eukaryotes ( best E-value = 8e−25 ) and archaea ( best E-value = 1e−25 ) as the best matches to the R726 protein sequence . In contrast , no significant match was detected with any of the bacterial RFs ( neither RF1 nor RF2 ) . We then examined the R726 sequence for the presence of key functional elements previously described in the eRF1/aRF1 peptide chain release factors . Figure 4A displays a multiple alignment of R726 and mg280 with representative sequences from eukaryotes and archaea class-I RFs . First , this alignment shows that the giant viruses and the eukaryote/archaea proteins are globally well conserved . Two conserved regions in the N-terminal part of the class-I RFs are well-known to be involved in the recognition of the stop codon . Those are the ( TAS ) NIKS motif ( Figure 4A , red box ) [40] and the YxCxxxF motif ( black box ) [41] . These crucial elements are conserved in the Mimivirus and Megavirus homologs . In addition , the peptidyl-tRNA hydrolysase activity of the class-I RFs requires a universally conserved GGQ motif in the middle of the protein [17] . Again , this essential motif is present in the Mimivirus and Megavirus homologs ( Figure 4A , green box ) . The interaction of class-I RF with class-II RF ( in eukaryotes ) or aEF1α ( in archaea ) , involves amino acids located in the C-terminal part of eRF1/aRF1 . The blue boxes ( Figure 4A ) highlight the regions of known interacting residues in eukaryotes [21] and archaea [24] . The GILRY motif ( Figure 4A , yellow box ) is also known to mediate the interaction between eRF1 and eRF3 [42] . These regions , although less conserved than the N-terminal part of the protein , also exhibit residues that are found in Mimivirus and Megavirus as well . In contrast , none of the essential functional motifs present in the bacterial class-I RFs ( see [43] for review ) are found in R726 and mg280 , with the exception of the GGQ motif . We can thus conclude that R726 has all the sequence hallmarks of a genuine class-I RF of the eukaryotic/archaeal type . Even though eRF1 , aRF1 and R726/mg280 are globally well conserved , the giant viruses' RFs exhibit specific elements ( Figure 4A ) . For instance there is an insertion in the N-terminal part of the protein , as well as a large deletion in the C-terminal domain , partially overlapping a previously identified deletion in Aeropyrum pernix [24] . Mimivirus and Megavirus sequences are clearly the most divergent sequences of the alignment . This visual impression was objectively confirmed by reconstructing the phylogeny of these class-I RFs , using the Phylobayes software with the CAT mixture model [44] . This method was used as it is known to better fit the phylogenetic signal present in giant viruses' genes than traditional evolutionary models [45] . The tree in Figure 4B exhibits a tight grouping of the eukaryotic sequences within one branch , a tight grouping of the archaeal sequences within a second branch , and a third branch consisting of the Mimivirus and Megavirus homologs . Other Bayesian and maximum likelihood methods supported the same three-pronged tree topology with the exception of a deeper branching of an A . castellanii paralog ( Figure S8 ) . This paralog does not contain internal stop codons similarly to the other eukaryotic class-I RFs . Hence R726 and mg280 are representative sequences of a new type of class-I RF . We previously showed that the R726 transcript was strongly expressed . The timing of its expression and the interplay with host's genes is illustrated in Figure 4C . Both A . castellanii genes , the canonical eRF1 and the paralog , see their expression slowly decreasing along the viral replication cycle . In contrast , the expression of the Mimivirus homolog clearly raises in an opposite manner . This negative correlation suggests that the expression of the Mimivirus class-I RF compensates for the decline of the host RF .
An apparent anomaly in the annotation of the predicted Mimivirus class-I release factor homolog led us to investigate in more details its transcript structure . This resulted in the hypothesis that Mimivirus possesses an intricate translation termination process involving the recoding of two stop codons . A similar gene structure in Megavirus strengthens this prediction that was then verified experimentally . To our knowledge such a combined occurrence of a frameshift and a readthrough in the coding sequence of a class-I RF has never been reported in any lineage in the tree of life . Surprisingly , although the sequences of the Mimivirus and Megavirus class-I RF homologs show close proximity with the eukaryotic/archaeal peptide chains release factors , they incorporate an autoregulatory mechanism only found in bacterial class-I RFs . As a central component of the translation apparatus , RFs are not found in viruses with the exception of the two recently described unclassified nucleocytoplasmic large DNA viruses: Marseillevirus [46] and Lausannevirus [47] . However these genes do not contain internal stop codons and are likely recent HGT from their cellular host ( see Figure S9 ) . An increasing number of studies support the idea that giant viruses have ancient origins , possibly predating the radiation of eukaryotes [2] , [11] , [12] , [16] , [48] , [49] . The phylogenetic reconstruction of the Mimivirus and Megavirus RFs genes , deeply branching at the root of eukaryotes and archaea , is consistent with this view ( see Figure 4B ) . Furthermore since Mimivirus/Megavirus RFs bear no clear phylogenetic affinity with any extant cellular homolog , acquisition by recent HGT is very unlikely . Thus , as for other translation components found in giant viruses , the Mimivirus and Megavirus RFs could originate from an ancestral genome encoding a complete translation system [8] , [15] , [16] . According to the current dogma , eukaryotes derived from the archaeal/bacterial domains , therefore one can hypothesize that the giant viruses' release factors regulatory mechanism could have been inherited from their prokaryotic ancestor . This is consistent with the fact that only bacterial RFs are known to exhibit a shifting motif analogous to the one we detected in Mimivirus/Megavirus RFs . Furthermore the only identified recoding event in Mimivirus and Megavirus corresponds to the RF gene , out of the more than 1000 genes encoded by each viral genome . Finally , this unusual recoding event is surprisingly present in the functional homolog to one of the rare bacterial gene exhibiting the same regulatory trick . It is thus tempting to speculate that the cenancestor possessed this regulatory element that was kept in the bacterial and Mimivirus/Megavirus lineages , but lost in the other lineages ( eukaryotes and archaea ) . Nevertheless , this scenario is impossible to prove in the apparent absence of sufficient sequence/structural similarity between the bacterial RF genes and the eukaryotic/archaeal RF genes [17] , [18] . The alternative hypothesis involves the reinvention of a similar regulatory feature in the giant viruses' lineage . This would be a nice example of convergent evolution that could have occurred before the divergence of Mimivirus and Megavirus . The multiple invention of the termination factor frameshifting mechanism in different bacterial lineages has been proposed previously [30] . Finally , the regulatory mechanism might also have been present in the ancestor of giant viruses , archaea and eukaryotes but subsequently lost in the two cellular lineages , and perhaps substituted by other more complex regulatory mechanisms . Effective translation termination requires the interaction of the class-I RF with a GTPase class-II RF ( eRF3 ) in eukaryotes , or a GTPase elongation factor ( aEF1α ) in archaea , through the C-terminal domain [23] , [24] . We showed that the Mimivirus R726 and Megavirus mg280 genes are likely to be class-I RFs of the eukaryotic/archaeal type although they constitute a new separate clade ( Figure 4B ) . They could thus also interact with a translational GTPase , among which the host's eRF3 is a candidate . Such a subtle host-pathogen interaction should be supported by an enhanced similarity of the viral C-terminal class-I RF with the host protein . This is clearly not the case ( see Figure 4A ) , which makes this interaction uncertain . Alternatively the giant viruses could encode their own class-II RF , making them autonomous for the translation termination function . There is no evidence of such class-II RF homologs in Mimivirus and Megavirus genomes , but the interacting protein could be one of the numerous genes of unknown function shared by the two viruses [15] . Another possibility would be that the giant viruses follow the archaeal model and recruit a pluripotent translation GTPase factor [24] encoded in their genome . The Mimivirus R624 gene could be this pluripotent interacting partner as it is annotated as a translation elongation factor , and it shares significant sequence similarity with the eukaryotic eRF3 and the archaeal aEF1α ( best E-values<1e−10 ) proteins referenced in the trGTPbase ( http://www . GTPbase . org . uk ) . However R624 was shown to be related to the GBP-1 subfamily of GTPases [11] , which is consistent with our phylogenetic reconstruction ( Figure S10 ) . The function of GBP-1 is still vague , but it seems to be related to protein synthesis [50] and mRNA surveillance [51] . Finally , one cannot rule out the possibility that the giant viruses' class-I RFs have no class-II RFs interacting partners as is the case in many groups of bacteria [20] , which would further highlight the hybrid bacterial/eukaryotic nature of giant viruses RFs . This last hypothesis is reinforced by previous studies reporting that mutations in the TASNIKS stop codon recognition motif abolish the eRF3 requirement for peptide release at the UAA and UAG stop codons [31] , [52] . Since Mimivirus and Megavirus contain motifs that are not strictly identical to this consensus motif , the class-II RF might thus be dispensable for translation termination . The programmed frameshift in the bacterial RF2 induces an autoregulatory feedback loop that maintains a constant production of termination factor [31] . It has been proposed that such a mechanism primarily aims to prevent excessive RF2 protein concentration which limits false recognition of tryptophan UGG codons as stops [31] . The two internal stop codons in giant viruses' RFs likely induce an even stronger buffering of protein overexpression . The R726 transcript expression appears to compensate for the host class-I RF expression decline , at least during the late phase of infection ( Figure 4C ) . Translation termination function might thus rely on the viral enzyme , and its tight regulation at the translation step is needed to maintain a low yet constant amount of viral termination factor . The strong regulation might be a way to control viral genes that contain stop codons prone to frequent translational frameshifts and readthroughs ( Figure 2 ) and thus produce alternative protein variants . The RF concentration leverage would then directly regulate their final product length . However , we did not find evidence for such regulated genes in the Mimivirus and Megavirus genomes . Beyond this speculative hypothesis , it is clear that the virally-encoded RFs are not strictly functionally redundant to the one provided by the host . Future experimental studies will help to understand how giant viruses rely on their own encoded translation factors , as well as the functional role of such a complex system for translation termination regulation . In addition to their enormous particle and genome size , and the presence of numerous translation components [2] , [15] , the unique combined occurrence of both a frameshift and a readthrough in a translation termination factor is yet another oddity brought about by the study of giant viruses .
The A . castellanii genome assembly ( available at http://www . hgsc . bcm . tmc . edu/microbial-detail . xsp ? project_id=163 ) is composed of 54 , 947 contigs ( 18 , 936 scaffolds ) . We used this basis to perform a complete re-assembly of the genome using all available sequence data . We gathered A . castellanii genomic DNA sequences from the NCBI trace archive . The complete dataset was composed of 689 , 389 Sanger reads and 10 , 556 , 721 454 reads . We performed a hybrid assembly using the Arachne [53] and Phrap ( P . Green , http://www . phrap . org ) assemblers . We finally obtained a 44 Mb A . castellanii genome assembly composed of 549 contigs ( ranging from 3 , 412 nt to 1 , 183 , 386 nt ) with a N50 of 17 , 363 nt . We subsequently performed the genome annotation using the Augustus gene prediction algorithm [54] incorporating gene expression data and protein homology evidences . The complete proteome of Dictyostelium pupureum and Dictyostelium discoideum , as well as the UniProtKB/Swiss-Prot database , were aligned to the A . castellanii genome using exonerate with the protein2genome model [55] . The same program was also used with the est2genome model to map all available A . castellanii ESTs from [7] , from http://www . hgsc . bcm . tmc . edu/microbial-detail . xsp ? project_id=163 and from Genbank , to the A . castellanii genome . All together these data allowed Augustus to predict 14 , 343 protein-coding genes . A total of 491 tRNAs was also predicted using the tRNAscan-SE program [56] . Proteins homologous to known selenoproteins and components of the selenocysteine incorporation machinery were searched using the HMMer program ( http://www . hmmer . org ) with HMM profiles from [57] , against the A . castellanii and Mimivirus proteomes . SECIS elements were searched using the SECISearch program [58] . All protein multiple alignments were performed using the MAFFT algorithm [59] with the L-INS-I parameter . Phylogeny reconstructions were done using the three following methods . We used the maximum likelihood package PhyML [60] with the WAG model and 100 bootstrap replicates . We also used the MrBayes software [61] with the PhyML tree as a starting tree and a Γ distributed rate model . The algorithm was run for 1 , 000 , 000 generations , the first 2 , 500 of which were disregarded and trees were sampled every 100 generations . Finally the phylogeny reported in Figure 3B was performed using the PhyloBayes algorithm [62] with a C60 mixture model and a burnin parameter of 1/5 of the length of the chain . Two chains were run in parallel and the stopping criterions were: discrepancies <0 . 3 and effective sizes >50 . 454 RNA-seq sequences of Mimivirus polyadenylated RNAs were used from [7] . RNA-seq data of total RNA from the Mimivirus/A . castellanii system were used from [3] . The reads sequenced by the SOLiD technology were mapped to the Mimivirus and A . castellanii genomes using the TopHat software [63] as a first pass . We mapped the reads in color space using the following parameters: max-multihits = 1 , min-intron-length = 20 and max-intron-length = 2000 . We then re-aligned the unmapped reads using the Bfast software [64] in color space with a minimum normalized score of 35 . Subsequently we used the Mimivirus and A . castellanii protein-coding and tRNA gene annotations ( see above ) to calculate gene expression levels . For each time point , that is 0 , 1 h , 2 h , 3 h , 4 h , 5 h , 6 h , 7 h and 11 h post-infection , we converted RNA-seq exonic reads density to the standard measurement of reads per Kb per million reads ( RPKM ) as described in [65] . The full-length R726 gene was amplified from Mimivirus genomic DNA using specific primers flanked by SacI and NotI restriction sites . The PCR product was inserted into an in-house modified pET28 plasmid to yield a N-terminally removable His-SUMO tagged protein . Site-directed mutagenesis of the two stop codons was performed using the QuickChange kit ( Stratagene ) to replace the readthrough stop by a tryptophan and/or to get rid of the frameshift stop by creating a +1 translational frameshift . The 4 plasmids containing the wild-type gene , the readthrough stop mutant , the frameshift mutant , or the double mutant , were verified by sequencing . The resulting vectors were transformed into Rosetta strain ( Novagen ) . Cells were grown into 2YT medium containing 100 µg . mL−1 ampicillin and 34 µg . mL−1 chloramphenicol at 30°C to an A600 of 0 . 9 . Temperature was then shifted to 17°C for 15 minutes . The protein expression was induced by adding 0 . 1 mM of isopropyl β-thiogalactopyranoside . Cells were grown 16–18 h post induction . Bacteria were harvested by centrifugation and resuspended in lysis buffer containing 50 mM Tris-HCl pH 8 . 0 , 300 mM NaCl , 10 µg . mL−1 DNase and EDTA-free protease inhibitor cocktail ( Roche ) . Cells were lysed using sonication or by mechanical disruption with the FastPrep system using glass beads ( MP bioscience ) . The crude lysate was clarified by centrifugation at 13 , 000× g for 45 min . The clarified lysate was applied to a 1 ml HisTrap HP Column ( GE Healthcare ) charged with Ni2+ and equilibrated with buffer A ( 50 mM Tris-HCl pH 8 . 0 , 300 mM NaCl ) on an AKTÄ explorer 10S FPLC system ( GE Healthcare ) . The column was washed with 10 column volumes of buffer A , 10 column volumes of buffer A containing 25 mM Imidazole and 20 column volumes of buffer A containing 50 mM Imidazole . Elution fraction was analyzed by SDS-PAGE and given the very low level of protein expression we used antibodies raised against the 6×His tag to reveal the recombinant proteins by western blot . For Mass spectrometry analysis , the band was cut out of the gel , trypsin digested and the resulting peptides were analyzed by MS/MS . | Giant viruses , such as Mimivirus and Megavirus , have huge near-micron-sized particles and possess more genes than several cellular organisms . Furthermore their genomes encode functions not supposed to be in a virus , such as components of the protein translation apparatus . Since Lwoff in 1957 , viruses are defined as ultimate obligate intracellular parasites from their need to hijack the peptide synthesis machinery of their host to replicate . We looked at the Mimivirus and Megavirus proteins that recognize the stop codons , the translation termination factors . We found that these genes contain two internal stop codons , meaning that their translation bypasses two distinct stop codons to produce a functional translation termination factor . These types of autoregulatory mechanisms are found in bacterial termination factors , although it involves only a single internal stop codon and not two , and are absent from their eukaryotic and archaeal homologs . Despite these bacterial-like features , giant viruses' termination factors have sequences that do not resemble bacterial genes but are clearly related to the eukaryotic and archaeal termination factors . Thus , giant viruses' termination factors surprisingly combine elements from eukaryotes/archaea and bacteria . | [
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] | 2012 | Translation in Giant Viruses: A Unique Mixture of Bacterial and Eukaryotic Termination Schemes |
Hair plays an important role in primates and is clearly subject to adaptive selection . While humans have lost most facial hair , eyebrows are a notable exception . Eyebrow thickness is heritable and widely believed to be subject to sexual selection . Nevertheless , few genomic studies have explored its genetic basis . Here , we performed a genome-wide scan for eyebrow thickness in 2961 Han Chinese . We identified two new loci of genome-wide significance , at 3q26 . 33 near SOX2 ( rs1345417: P = 6 . 51×10−10 ) and at 5q13 . 2 near FOXD1 ( rs12651896: P = 1 . 73×10−8 ) . We further replicated our findings in the Uyghurs , a population from China characterized by East Asian-European admixture ( N = 721 ) , the CANDELA cohort from five Latin American countries ( N = 2301 ) , and the Rotterdam Study cohort of Dutch Europeans ( N = 4411 ) . A meta-analysis combining the full GWAS results from the three cohorts of full or partial Asian descent ( Han Chinese , Uyghur and Latin Americans , N = 5983 ) highlighted a third signal of genome-wide significance at 2q12 . 3 ( rs1866188: P = 5 . 81×10−11 ) near EDAR . We performed fine-mapping and prioritized four variants for further experimental verification . CRISPR/Cas9-mediated gene editing provided evidence that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes , which are both involved in hair development . Finally , suitable statistical analyses revealed that none of the associated variants showed clear signals of selection in any of the populations tested . Contrary to popular speculation , we found no evidence that eyebrow thickness is subject to strong selective pressure .
Hair has a range of important functions in primates and has been speculated to be subject to intense natural and sexual selection [1] . Although humans have lost most terminal body hair to allow the development of more efficient sweating as an adaptation to bipedal life , considerable facial hair remains , with great diversity between populations [2] . The eyebrow , an area of thick , short facial hair above the eye that follows the shape of the lower margin of the brow ridge , is one of the most conspicuous features of the face . It is thought that its main function is to prevent sweat , water , and other debris from getting into the eye [2] . As a major facial feature , the eyebrow plays an important role in human communication , facial expression , sexual dimorphism and attractiveness [3–6] . It has been suggested that eyebrows may be subject to sexual selection [7] . Notably , eyebrow thickness varies between and within human populations [8 , 9] . A recent genome-wide association study in Latin Americans found that common DNA variants in the FOXL2 gene are associated with eyebrow thickness . However , these variants have very low minor allele frequencies in East Asians and Europeans , suggesting that in these two populations , eyebrow thickness may well be affected by different genes . To enhance our understanding of the genetic basis underlying the variation of human eyebrow thickness , we conducted a genome-wide association study in East Asians and Eurasians , followed by a trans-ethnic meta-analysis , to identify genetic variants that affect eyebrow thickness in humans . Moreover , in order to validate the findings from our association analyses , we performed fine mapping and conducted functional genetic experiments . Finally , we applied various statistical genetics methods to search for signals of positive selection in and around the identified candidate genes .
Our discovery GWAS included a total of 2961 subjects from the Taizhou Longitudinal Study [10] ( TZL , Han Chinese ) . Replication cohorts were drawn from the Xinjiang Uyghur Study [11] ( UYG , N = 721 , Uyghur , an admixed East Asian-European population ) , the CANDELA study [8] ( CANDELA , N = 2301 , Latin Americans ) and the Rotterdam Study [12 , 13] ( RS , N = 4411 , Northwestern Europeans; for sample characteristics see S1 Table ) . In all cohorts , eyebrow thickness was assessed on an ordered categorical scale using a self-developed photo numeric approach ( S1 Fig ) . Inter-rater ( for the CANDELA cohort , intra-rater ) reliability was reasonable for all cohorts ( Kappa: 0 . 48–0 . 66 , S2 Table ) . There was a higher degree of diversity in eyebrow thickness in Uyghurs ( D = 0 . 58 , Gini-index ) and Northern Europeans ( D = 0 . 57 ) than in Han Chinese ( D = 0 . 52 ) and Latin Americans ( D = 0 . 49 ) ( S1 Table ) . Eyebrows were significantly less thick in females than in males in TZL , UYG , and RS ( PTZL = 7 . 23×10−70 , PUYG = 7 . 31×10−33 , PRS = 4 . 10×10−106; the CANDELA sample included only male subjects ) . Age was negatively correlated with eyebrow thickness in TZL , CANDELA , and RS ( PTZL = 7 . 75×10−20 , PCANDELA = 4 . 20×10−17 , PRS = 1 . 33×10−10; the UYG sample included only young subjects between 18 and 24 years ) . The GWAS in Han Chinese was based on 7 , 119 , 456 SNPs; it identified association signals of genome-wide significance ( P<5×10−8 ) for two genomic regions , located at 3q26 . 33 ( rs1345417: P = 6 . 51×10−10 ) and 5q13 . 2 ( rs12651896: P = 1 . 73×10−8; Fig 1 ) . After conditioning on the genotypes of rs1345417 and rs12651896 , no additional SNPs showed significant association at a genome-wide level ( P<5×10−8 ) . The heritability of eyebrow thickness in TZL was 37 . 6% . The SNPs rs1345417 and rs12651896 explain 3 . 11% and 2 . 55% of this heritability , respectively . Although the GWAS in the Uyghurs , which was based on 7 , 224 , 952 SNPs , did not detect any genome-wide significant signals ( S2 Fig ) , the signals at 3q26 . 33 and 5q13 . 2 showed marginal significance ( rs1345417: P = 3 . 78×10−4; rs12651896: P = 5 . 4×10−2 ) , and allelic effects were consistent with those in TZL ( Table 1 ) . The GWAS in Latin Americans has been published previously; it found that common variants at 3q22 . 3 are associated with eyebrow thickness [8] . We found that it also corroborated our findings at 3q26 . 33 and 5q13 . 2 ( rs1345417: P = 1 . 04×10−7; rs12651896: P = 7 . 54×10−6 ) . The meta-analysis of all three GWAS ( TZL , UYG and CANDELA ) identified four loci reaching genome-wide significance ( P<5×10−8; Fig 2A; S3 Table ) . Apart from the loci described above 3q26 . 33 ( rs1345417: P = 1 . 11×10−19 ) , 5q13 . 2 ( rs12651896: P = 2 . 52×10−13 ) and 3q22 . 3 ( rs112458845: P = 2 . 24×10−9 ) there was one additional locus of genome-wide significance , at 2q12 . 3 ( rs1866188: P = 5 . 81×10−11 ) ( Fig 2 ) . The respective quantile-quantile ( Q-Q ) plots showed no sign of inflation for any of the association tests described above , and the genomic control factor λ was relatively low in all cases ( λTZL = 1 . 041 , λUYG = 1 . 053 , λCANDELA = 1 . 015 , λMeta = 1 . 040 , S3 Fig ) , indicating that the test statistics were not substantially confounded by population sub-stratification . For all three novel loci ( 3q26 . 33 , 5q13 . 2 and 2q12 . 3 ) , the allelic effects acted in the same direction in all populations ( Table 1 ) . There was no significant effect size heterogeneity across populations for any of these three association signals ( S3 Table ) . However , the signal at 3q22 . 3 , which was highly significant in CANDELA , with P = 4 . 95×10−11 at rs112458845 and an effect allele frequency ( EAF ) of 0 . 127 , did not reach nominal significance in our GWAS of TZL ( EAF = 0 . 065 , P = 0 . 729 ) and UYG ( EAF = 0 . 032 , P = 0 . 494 ) . Furthermore , our meta-analysis revealed strong allelic heterogeneity for rs112458845 ( Q = 16 . 284 , I2 = 81 . 58%; S3 Table ) , indicating that the effect at 3q22 . 3 may be specific to Latin Americans . We performed a further replication analysis for all SNPs described above in the Rotterdam Study sample , which comprises 4411 Dutch Europeans . With the exception of rs112458845 at 3q22 . 3 , which was specific to Latin Americans , this analysis replicated all signals at a nominal significance level ( rs1866188: P = 0 . 0141; rs112458845: P = 0 . 366; rs1345417: P = 4 . 03×10−3; rs12651896: P = 2 . 93×10−4; Table 1 ) . The allelic effects at all associated SNPs acted in the same direction as in all other populations tested ( Table 1 ) . Next , we sought to localize the variants driving the association signals at each of the novel loci that attained genome-wide significance in our trans-ethnic meta-analysis . We utilized PAINTOR [14 , 15] to calculate the Bayesian posterior causal probability for each variant included in the signal and identified the sets of variants that collectively explained 99% of the total probability ( credible sets ) . The credible sets for 2q12 . 3 and 3q26 . 33 contained a single SNP each ( S4 Table ) . The credible set for 5q13 . 2 comprised nine variants . Among these , the posterior causal probability was highest for rs12651896 ( posterior probability = 0 . 744; S4 Table ) . Using CADD [16] and DeepSEA [17] to assess the functional consequences of each variant , we found that rs10061469 was consistently predicted to be functionally important ( S4 Table ) . Our interrogation of ENCODE [18] and REMC [19] data revealed that the regions around rs1866188 and rs1345417 show distinct active enhancer signatures defined by epigenetic markers , such as the histone modifications H3K4me1 and H3K27ac , and DNase hypersensitivity in epithelial cells ( Fig 3 ) . The regions around rs12651896 and rs10061469 are involved in long-range chromatin looping interactions with FOXD1 ( Fig 3B ) . Together , these data suggest the presence of putative regulatory regions in the neighborhood of these variants . We thus chose rs1345417 , rs12651896 , rs10061469 and rs1866188 for further experimental verification . To further examine whether the four variants identified in our fine mapping analyses ( rs1345417 , rs12651896 , rs10061469 and rs1866188 ) play a role in the expression of nearby genes , we conducted CRISPR/Cas9-mediated gene editing for each of them . We targeted the genomic regions surrounding each variant using two different sgRNAs ( S4 Fig ) . Our qRT-PCR analysis of annotated transcripts near these SNPs found that mixed clones of A375 cells that were stably infected with rs1345417 targeting lenti-Cas9-sgRNAs displayed significantly reduced SOX2 expression and significantly elevated SOX2-OT expression compared to cells infected with control lenti-Cas9-sgRNAs ( Fig 4A ) . Infection with rs12651896 targeting lenti-Cas9-sgRNAs led to a significant reduction in the expression of FOXD1 ( Fig 4B ) , while infection with rs10061469 targeting lenti-Cas9-sgRNAs showed no effect on nearby genes ( Fig 4C ) . Infection with rs1866188 targeting lenti-Cas9-sgRNAs resulted in a significant reduction in LIMS1 expression of ( Fig 4D ) . Among the genes showing significant changes in expression levels , only SOX2 and FOXD1 were reported to be related to hair growth [20 , 21] . We thus chose to focus on these two genes . For each of the two lenti-Cas9-sgRNAs targeting rs1345417 and rs12651896 , we derived multiple independent single cell clones of infected A375 cells and characterized their exact deletion/mutation status at the target SNP sites ( S4 Fig ) . We found that SOX2 expression was significantly reduced in A375 cell clones carrying deletions/substitutions encompassing rs1345417 , in comparison with cells infected with empty lenti-Cas9 vector or with lenti-Cas9-control sgRNA ( Fig 4E and 4F ) . Importantly , A375 cell clones that were infected with rs1345417 targeting lenti-Cas9-sgRNA but where the SNP site was not successfully edited , did not show reduced SOX2 expression ( Fig 4E and 4F ) . Similarly , FOXD1 expression was significantly reduced in all A375 cell clones carrying a deletion encompassing the rs12651896 site , compared to both controls and unsuccessfully edited infected cell clones ( Fig 4G and 4H ) . In one clone ( 896sg1m1 ) , β-actin expression was also unexpectedly reduced , probably due to off-target effects . To test if a single nucleotide substitution event is sufficient to affect endogenous SOX2 gene expression , we conducted a CRISPR/Cas9-mediated knock-in experiment at rs1345417 , for which native A375 cells are homozygous ( G/G ) . Consistent with our expectations , SOX2 expression levels were significantly lower in an edited , G/C heterozygous A375 clone than in the original G/G A375 cells ( Fig 4I ) . Additionally , we conducted a luciferase reporter experiment for the genomic region surrounding rs1345417 ( S5 Fig ) . We found that a 1 , 495 bp genomic fragment encompassing the rs1345417 site and containing its common G allele can act as a transcriptional enhancer . Its insertion before a SOX2 promoter sequence led to 3 to 4-fold increase in reporter expression ( S5 Fig ) . Moreover , a G>C mutation at rs1345417 on the reporter construct resulted in significantly reduced reporter activity , in line with our observations in the CRISPR/Cas9 experiments above ( Fig 4I ) . Together , our data indicate that genetic variation at rs1345417 and rs12651896 can affect the transcription of SOX2 and FOXD1 , respectively . For some of the top associated loci , allele frequencies varied among populations ( S6 Fig ) . This variation could be caused either by random genetic drift or by local positive selection . To assess whether positive selection could have helped shape variation in the genomic regions associated with human eyebrow thickness , we applied several statistical genetics methods . Using the Integrated Haplotype Score ( iHS ) [22] and the Composite of Multiple Signals ( CMS ) statistic [23] , we did not find any evidence indicating that SOX2 , FOXD1 and FOXL2 are subject to positive selection in East Asian ( CHB ) , European ( CEU ) and African ( YRI ) populations ( S7 and S8 Figs ) . However , there were highly significant signatures of strong positive selection in the EDAR region . This is not surprising , as our top signal near EDAR ( rs1866188 ) is in high LD with rs3827760 , a strongly selected functional SNP causing a number of ectodermal related phenotypic changes , as demonstrated previously [24] . Therefore , the signal of selection observed for rs1866188 may well be explained by the selective pressure on rs3827760 . To test whether the differences in allele frequencies may stem from different local selection pressures , we applied a probabilistic approach described by He and colleagues [25] to quantify local inter-population differences in selection for the four top loci . Apart from rs1866188 at EDAR , we found no significant differences in selection for any other SNP in the associated regions ( S9 Fig ) . To test whether eyebrow thickness might be subject to polygenic selection , we applied the polygenic scores test developed by Berg and Coop [26] . This test measures the total frequency of associated alleles in a population , weighting each allele by its effect size . Loci that have undergone local positive selection will show greater divergence than expected under the neutral model . Previous studies using this method have found excess variance among populations for genetic scores associated to height , demonstrating that height is subject to local positive selection [27] . Here , we calculated the polygenic score for eyebrow thickness based on the top four associated variants . The resulting scores were similar between populations and showed no excess variance due to positive selection ( P = 0 . 567 , S10 Fig ) , indicating that the allele frequency differences observed among populations are better explained by random genetic drift than by selection . Our results thus provide no evidence that eyebrow thickness is under strong positive selection in human populations .
We found that eyebrow thickness is significantly associated with age and gender . As may be expected , older adults tend to have a significantly reduced eyebrow thickness due to the weakened biological function of older follicle cells . The eyebrows of males are generally thicker than those of females . Eyebrow plucking is widespread among females and may thus have affected our results; nevertheless , the large and consistent differences observed between genders are likely to be real , considering that androgens tend to stimulate hair growth [28 , 29] . The top associated SNP at 3q26 . 33 , rs1345417 , is located about 80 kb downstream of the SOX2 gene ( Sex Determining Region Y-Box 2 ) , an important transcription factor that is highly expressed in the dermal condensate ( DC ) and dermal papilla ( DP ) of growing follicles [30 , 31] . Interestingly , fine-tuning of SOX2 expression appears to play an important role in the regulation of hair thickness . In murine skin development , from E18 . 5 onwards , Sox2 expression becomes confined to DPs in thick guard/awl/auchene hairs , but not in thin zigzag hairs [20] . Moreover , conditional ablation of Sox2 from the DP resulted in significant reduction in the length of awl/auchene , but not zigzag hairs [20] . These findings are consistent with our hypothesis that the G>C mutation at rs1345417 may cause reduced eyebrow thickness by downregulating SOX2 expression . The top SNP within the second signal at 5q13 . 2 , rs12651896 , is located 242 kb downstream of FOXD1 ( Forkhead Box D1 ) . This gene belongs to the forkhead family of transcription factors , whose members are characterized by a distinct forkhead domain . FOXD1 is especially enriched in DC cells , a precursor of DP/dermal sheath niche cells within the mature follicle [21] . Finally , rs1866188 on chr2q12 . 3 is located 253 kb upstream of EDAR ( Ectodysplasin A receptor ) , a gene which plays an important role in the development of ectodermal tissues such as hair , teeth , and sweat glands [24] . Previous studies have consistently found EDAR to be implicated in beard density [8] and hair thickness [24] and straightness [11 , 32 , 33] . It is notable that all of the signals identified by our GWAS fall into regulatory regions . To date , hundreds of GWAS have been conducted , resulting in the identification of a large number of genetic variants associated with common diseases and phenotypic variation . The majority ( ~93% ) of variants associated with common traits lie within non-coding sequences , complicating their functional evaluation [34] . Recent studies have found that these variants are concentrated in regulatory DNA . This remains true even after adjusting for microarray SNP ascertainment bias and suggests that non-coding variants may act by causing changes in gene expression levels [34] , as supported by several lines of evidence [35–39] . The association signals found here at 3q26 . 33 ( rs1345417 ) , 5q13 . 2 ( rs12651896 ) , and 2q12 . 3 ( rs1866188 ) are all located within active enhancer regions characterized by epigenetic markers , such as H3K4me1 and H3K27ac histone modifications , and DNase hypersensitivity in epithelial cells . Our functional validation experiments provided evidence for enhancer activity around rs1345417 and rs12651896 . It is thus highly plausible that these variants affect eyebrow thickness by regulating the expression of SOX2 and FOXD1 , respectively . In particular , the CRISPR/Cas9-mediated knock-in experiment provided direct evidence that a single substitution at rs1345417 is sufficient to affect the endogenous gene expression of SOX2 . Our study represents a successful example of how GWAS and CRISPR/Cas9 technology can be combined to demonstrate the involvement of non-coding variants with regulatory functions in common diseases and normal phenotypic variation . The FOXL2 variant rs112458845 has previously been found to be associated with eyebrow thickness in Latin Americans . Here , we found no significant evidence of association of this variant with this phenotype in East Asians or Europeans . This may partly be due to the frequency distribution of the minor allele , which is rare in East Asians ( 4% ) and absent in Europeans ( 0% ) , but relatively common in Native Americans ( 26% ) . Additionally , and perhaps more importantly , allelic effect sizes may be population specific due to a unique yet unidentified environmental or genetic background and may thus vary between Latin Americans on the one hand , and East Asians and Europeans on the other . A similar impact of population heterogeneity on allelic effect sizes has been reported for other traits , such as follicular lymphoma [40] , body bone mineral density [41] , and Type 1 diabetes [42] . It has long been debated whether eyebrow thickness is a selected trait or a ‘neutral feature’ with no apparent link to individual fitness [3 , 6 , 7] . We used several methods to detect potential signatures of positive selection for the variants associated with eyebrow thickness . First , we tested whether any of the associated regions overlapped with signals of positive selection . We then used a recently developed probabilistic method to test and estimate differences in selection of the associated variants between populations . We further tested the presence of polygenic selection by examining subtle allele frequency shifts across multiple loci . Previous studies using the above methods have found signatures of positive selection for a range of human traits , including height , skin color , and BMI [23 , 25 , 26 , 43] . However , apart from the EDAR region around rs3827760 , a SNP known to be under strong selection , we found no significant signatures of positive selection for variants associated with eyebrow thickness . These results suggest that eyebrow thickness may not be subject to strong positive selection , at least not via the genes identified here . In this context , it is worth noting that most studies postulating sexual selection of the eyebrow were based on reconstructed attractiveness [3 , 5 , 7] , and it may be argued that the perception of attractiveness can vary significantly over time [3] . Therefore , while sociological studies have indicated that eyebrow thickness may be subject to sexual selection , our study does not provide any support for this conclusion from an evolutionary or genetic perspective . In conclusion , we identified three novel genetic variants near SOX2 , FOXD1 and EDAR that influence eyebrow thickness . We demonstrated that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes . Furthermore , we found evidence for population heterogeneity in the genetics of eyebrow thickness . Finally , our results suggest that eyebrow thickness may not be subject to strong positive selection .
The Taizhou Longitudinal Study was carried out following protocols approved and oversight by the Institutional Research Board at Fudan University ( Ethics Research Approval No . 85 ) . The Xinjiang Uyghur Study was conducted with the official approval of the Ethics Committee of the Shanghai Institutes for Biological Sciences ( ER-SIBS-261410 ) . The CANDELA ethics approval was obtained from: Universidad Nacional Autonoma de Mexico ( Mexico ) , Universidad de Antioquia ( Colombia ) , Universidad Peruana Cayetano Heredia ( Peru ) , Universidad de Tarapaca ( Chile ) , Universidade Federal do Rio Grande do Sul ( Brasil ) and University College London ( UK , approval number 3351/001 ) . The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC ( registration number MEC 02 . 1015 ) and by the Dutch Ministry of Health , Welfare and Sport ( Population Screening Act WBO , license number 1071272-159521-PG ) . All participants provided written informed consent in these four studies . This study is based on data from four populations ( S1 Table ) : Han Chinese , Uyghurs , Latin Americans and Europeans . 2 , 961 Han Chinese ( including 1 , 060 males and 1 , 901 females , with an age range of 31–87 ) were enrolled in Taizhou , Jiangsu Province , as part of the Taizhou Longitudinal Study ( TZL ) [10] , in 2014 . 721 Uyghurs ( including 282 males and 439 females , with an age range of 17–25 ) were enrolled at Xinjiang Medical University , Urumqi , Xinjiang Province , China , as part of the Xinjiang Uyghur Study ( UYG ) in 2013–2014 . Additionally , we collected summary statistics for a previously conducted GWAS on eyebrow thickness in Latin Americans of the CANDELA cohort , details of which have been previously published [8] . The Rotterdam Study is a population-based prospective study including a main cohort ( RS1 ) and two extensions ( RS2 and RS3 ) [12 , 13] . All participants were examined in detail at baseline . Collection and purification of DNA have been described in detail previously [12] . The Rotterdam Study has been entered into the Netherlands National Trial Register ( NTR; www . trialregister . nl ) and into the WHO International Clinical Trials Registry Platform ( ICTRP; www . who . int/ictrp/network/primary/en/ ) under shared catalogue number NTR6831 . All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians . Eyebrow thickness ( i . e . , density ) was rated by eye on a three-point scale ( TZL , UYG , RS: scarce , normal , and dense; CANDELA: low , medium and high ) , following an established standard and based on photographic imagery ( S1 Fig ) . For the TZL and UYG cohorts , each case was rated independently by two different investigators . The inter-rater reliability was evaluated with the Kappa statistic . Cases where ratings were inconsistent were reviewed by a third investigator , who made the final decision . For the CANDELA cohort , an interview of the volunteers had indicated that most women modified their eyebrows; in this cohort , eyebrow thickness was therefore only scored in men . Phenotyping was performed by an experienced investigator , and intra-rater reliability was assessed for 150 individuals . For RS , three investigators simultaneously and independently evaluated all photos , which were displayed on two identical screens with the same settings . Before evaluation , a total of 50 photos were openly discussed to reach a consensus between the three investigators . Inter-rater reliability was also evaluated with the Kappa statistic . The average score from the three independent ratings was used as the final phenotype in all subsequent analysis . For TZL and UYG , blood samples were collected , and DNA was extracted . All samples were genotyped using the Illumina HumanOmniZhongHua-8 chip , which interrogates 894 , 517 SNPs . To control for genotype quality , we used PLINK 1 . 9 [44] to exclude individuals with more than 5% missing data , related individuals , and those that failed the X-chromosome sex concordance check , or for whom the available information on ethnicity was incompatible with their genetic information . We also excluded SNPs with more than 2% missing data , with a minor allele frequency ( MAF ) below 1% , and those that failed the test for Hardy-Weinberg equilibrium ( P<1×10−5 ) . The chip genotype data were phased using SHAPEIT [45] , and IMPUTE2 [46] was then used to impute genotypes at non-genotyped SNPs using variant positions from the 1000 Genomes Phase 3 data as a reference . SNPs with an imputation quality score ( INFO ) below 0 . 8 or a MAF below 1% were eliminated from further analyses . For the Han Chinese population , a total of 6 , 343 , 243 imputed SNPs passed quality control and were combined with 776 , 213 genotyped SNPs for association analysis . For the Uyghur population , a total of 6 , 414 , 304 imputed SNPs passed quality control and were combined with 810 , 648 genotyped SNPs for further analyses . In RS1 and RS2 , genotyping was carried out using the Infimum II HumanHap550K Genotyping Bead Chip version 3 , which contains 6 , 787 , 905 probes . Complete information on genotyping protocols and quality control measures for RS1 and RS2 have been described previously [47 , 48] . In RS3 , genotyping methods closely followed those established for RS1 and RS2 , but a denser array , the Human 610 Quad Arrays of Illumina with 15 , 880 , 747 probes , was used . Individuals with a call rate < 97 . 5% , gender mismatch with typed X-linked markers , or excess autosomal heterozygosity ( >0 . 33 ) were excluded , as were duplicates or 1st degree relatives identified using IBS probabilities , and outliers using multi-dimensional scaling analysis with reference to the 210 Hap Map samples . Genome-wide imputation in RS3 closely followed the methods used in RS1 and RS2 , as described in detail previously [48] . Genotypes were imputed using MACH [49] based on phased autosomal chromosomes of the 1000 Genome reference panel , orientated on the positive strand . The effects of possible population stratification were corrected using the EIGENSTRAT [50] tool from the EIGENSOFT package . To this end , TZL and UYG data were combined with 1000 Genomes Phase 3 data for YRI , CHB and CEU populations . 102 , 284 SNPs in low linkage equilibrium ( r2<0 . 2 ) were selected for analysis . Principal component ( PC ) analysis did not find any outliers in TZL and UYG ( S11 Fig ) . Initial genome-wide association tests using multiple linear regression with an additive genetic model incorporating gender , age and four genetic PCs as covariates were performed in PLINK 1 . 9 [44] . Expected and observed association results for all tests were visualized in quantile-quantile ( Q-Q ) plots to assess systematic inflation in association resulting from population stratification or other systematic causes of bias . None of the Q-Q plots showed any sign of inflation , the genomic control factor λ being < 1 . 06 in all cases ( S3 Fig ) . To evaluate the presence of additional independent signals at each locus , we performed conditional analyses by adding the dosages of the top SNP at each locus to the regression model . Q-Q , Manhattan and regional association plots [51] were created in R . The proportion of variance in eyebrow thickness explained by the genetic variants identified was estimated using GCTA [52] . The meta-analysis of the TZL , UYG and CANDELA data sets was performed using METAL [53] . Heterogeneity of SNP associations across studies was tested via Cochran’s Q statistic [54] , and its magnitude was expressed by I2 . For SNPs with significant heterogeneity , a random effects model was applied for meta-analysis using METASOFT [55] . We performed fine mapping of each locus for a 1 Mb genomic interval flanking the top SNP ( 500 kb upstream and 500 kb downstream ) using PAINTOR [14 , 15] . For each SNP within this 1 Mb region , the posterior probability that this SNP is driving the region’s association signal was calculated by dividing the SNP’s Bayes factor ( BF ) by the sum of the BFs of all SNPs in the region [56] . A 99% credible set was then constructed by ( 1 ) ranking all variants according to their Bayes factor , and ( 2 ) including ranked variants until their cumulative posterior probability of representing the causal variant at a given locus exceeded 0 . 99 [57] . To further facilitate the prioritization of variants for functional analysis , we used CADD [16] ( Combined Annotation-Dependent Depletion ) and DeepSEA [17] ( deep learning-based sequence analyzer ) to evaluate the possible functional consequences of the variants in the 99% credible set . Candidate genes were chosen based on their distance to the associated loci as well as their function , involvement in biochemical pathways , tissue expression , and involvement in similar phenotypes . The relevant information was obtained from NCBI [58] and Ensemble [59] , as well as available published data . We used HaploReg v4 . 1 [60] to extract a variety of regulatory annotations , including histone modification ( ChIP-seq tracks ) , chromatin state segmentations ( 15-state ) and ChIA-PET [61] ( Chromatin Interaction ) from ENCODE [18] and the Roadmap Epigenomics Project [19] , conserved regions from GERP [62] and Phastcons [63] , and eQTLs from the GTEx [64] and GEUVADIS databases [65] . No cell lines were found in the database of commonly misidentified cell lines maintained by ICLAC and NCBI Biosample . Cell lines were not authenticated . NHEM ( human melanocytes ) and A375 ( human melanoma ) were purchased from the cell bank of the Chinese Academy of Sciences . HACAT ( immortal keratinocytes ) and SCC13 ( skin squamous-cell carcinoma cell line ) were purchased from ATCC . Cell lines were routinely tested for mycoplasma infection . Cells were cultured using DMEM+10%FBS+1%Penicillin-Streptomycin . A 1 , 495 bp genomic fragment comprising the rs1345417 enhancer was amplified from human genomic DNA and cloned into a pGL3-promoter vector , in which the SV40 promoter was replaced by a SOX2 promoter . The G>C mutation was introduced by site-directed mutagenesis ( Takara ) . The inserts in each construct were verified by sequencing . The detail information of primer sequences can be found in S5 Table . Constructs were transfected with equimolar amounts ( 500 ng ) of luciferase reporter plasmids into A375 Melanoma cells using jetPEI ( Polyplus ) , according to the manufacturer’s instructions . Luciferase expression was normalized to 200 ng Renilla luciferase expression ( pRL-SV40 ) . Cells were harvested after 48 h . Luminescence activity was measured with a Berthold Centro LB 960 Microplate Luminometer . Data represent at least three independent experiments . Student’s two-tailed t-test was used to determine statistical significance . For each candidate variant , two sgRNAs were designed , cloned into the lentiCRISPR v2 vector , and packaged into lentivirus as previously described [66] . sgRNAs used for rs1345417: sgRNA1 ( CCTGCTTTTGCCTCAGCCCACAT ) and sgRNA2 ( CCCACATCTTCTCTATTAGTAAG ) . sgRNAs used for rs12651896: sgRNA1 ( CAAAATGTTCTTGCTAGCATATCCA ) and sgRNA2 ( GCATATCCATAACTAGCACAGG ) . sgRNAs used for rs10061469: sgRNA1 ( ACAACCTGCAATAAACTATTAA ) . sgRNAs for rs1866188 site: sgRNA1 ( GAGTGGCCACTCTCTTTTGC ) and sgRNA2 ( GAATGCATAAGGA TCAAATCG ) . Scramble Control sgRNA sequence is ( CCCACATAGTCTCACTTAG TAAG ) . For target site deletion via lentiCRISPR-sgRNA virus infection , stably infected cells were selected on puromycin . For clone selection , stably infected cells were diluted to allow colonial growth . Single colonies were individually picked for DNA sequencing of the target site . Because protospacer adjacent motives were located as far as 100 bp upstream and 22 bp downstream , the deletion at rs1866188 was considerably larger than for the other sites . For G>C substitution at rs1345417 in A375 cells , we used a CRISPR-Cas9 mediated knock-in strategy . Specifically , in order to substitute the G/G of rs1345417 in A375 cell , a ~1000bp DNA fragment encompassing the rs1345417 site from a Chinese individual with a C/C homozygote genotype was PCR amplified and TA cloned into the pMD18T Vector ( Takara ) to construct a C-donor plasmid . We then co-transfected the C-donor plasmid and the lenticrispr-rs1345417-sgRNA2 plasmid into A375 cells using the Effectene reagent ( Qiagen ) . 48 h after transfection , the cells were selected on puromycin for 48 h to eliminate untransfected cells . Successfully transfected cells were then diluted for colonial growth . Single colonies were individually picked for DNA sequencing to screen for substitution at the target site . In total , 44 clones were screened . Sequencing of the rs1345417 site: Genomic DNA was extracted from each cell clone using ZR Genomic DNA-tissue MiniPrep ( Zymo ) following the manufacturer’s protocol . The rs1345417 genomic region was amplified from genomic DNA by nested PCR and TA-cloned into pMD18 T vector for sequencing . At least four individual TA-clones were sequenced for each cell clone . RNA extraction was conducted using Direct-zol RNA MiniPrep Plus ( Zymo ) following the manufacturer’s protocol . Reverse transcription was conducted using the iscript cDNA synthesis kit ( Biorad ) following the manufacturer’s protocol . Real-time PCR experiments were conducted on a VIIA7 Fast Real-Time PCR system ( Applied Biosystem ) using the iTaq universal SYBR Green supermix ( Biorad ) . All statistical analyses were conducted using Microsoft Excel and the GraphPad Prism 6 software . Genomic characteristics resulting from strong recent positive selection include low haplotype diversity and high linkage disequilibrium . We calculated extended haplotype homozygosity ( EHH ) [22 , 67] for all SNPs until EHH < 0 . 05 in CEU , CHB and UYG samples . Next , the integrated haplotype score ( iHS ) [68] was calculated for all SNPs , with an allele frequency bin of 0 . 05 to standardize iHS scores against other SNPs of the same frequency class within the region . Finally , we calculated P values assuming a Gaussian distribution of iHS scores under the neutral model , which was checked by plotting the values against a Gaussian distribution . The empirical significance cutoff was based on the top 0 . 1% iHS scores . We also performed genome-wide CMS [23] analysis in African ( YRI ) , European ( CEU ) , and East Asian ( JPT+CHB ) populations from the 1000 Genome Project [69] to validate our results . The empirical significance cutoff was based on the top 0 . 1% CMS scores . Differences in allele frequencies are indicators of possible differences in selection between populations . To test whether the differences in allele frequencies may result from different local selection pressure , we used a probabilistic method which was recently put forward by He and colleagues [25] . We used this approach to test and estimate selection differences between populations for the four top associated loci . We first estimated differences in selection coefficients between populations ( CHB , CEU and YRI ) using logarithmic odds ratios of allele frequencies . The variance of the estimation was then calculated based on genome-wide variants . Finally , we calculated P values assuming a Gaussian distribution of the statistic under the neutral model . The significance cutoff was P<0 . 005 after multiple testing correlation . To investigate whether the loci associated with eyebrow thickness are more differentiated among populations than expected under neutral genetic drift , for each population m , we calculated the polygenic eyebrow thickness score ( genetic score ) as Zm=2∑i=1Lβlpml where βl is the effect size of the eyebrow thickness increasing allele l , and pml is the frequency of allele l in population m . We first used the four loci identified here in conjunction with allele frequency data from the 1000 Genome Project dataset to estimate the genetic score for eyebrow thickness in each population , with the effect sizes estimated in the meta-analysis . To test whether there was a signature of polygenic adaptation , we then adopted a framework developed by Berg and Coop [26] , which builds a multivariate normal model based on matched , presumably neutral variants , to account for relationships among populations . Traits that have undergone local selection will show excess divergence among populations ( significance cutoff: P<0 . 05 ) . | Hair plays an important role in primates and is clearly subject to adaptive selection . While humans have lost most facial hair , eyebrows are a notable exception . Eyebrow thickness is heritable and widely believed to be subject to sexual selection . Nevertheless , few genomic studies have explored its genetic basis . Here we performed genome-wide association studies for eyebrow thickness in multiple ethnic groups , including Han Chinese , Uyghurs , Latin Americans , and Caucasians . We found solid evidence that novel genetic variants near the SOX2 , FOXD1 and EDAR genes could affect eyebrow thickness . After fine mapping , we prioritized four variants for experimental verification . CRISPR/Cas9-mediated gene editing provided evidence that the variants rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes . This represents a successful example of a combination of GWAS and CRISPR/Cas9 technology to demonstrate how non-coding variants with regulatory functions may play an important role in common diseases and traits . Finally , suitable statistical analyses suggest that , contrary to popular speculation , eyebrow thickness should not be subject to strong selection pressure , including sexual selection . | [
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"genetics"
] | 2018 | Genome-wide association studies and CRISPR/Cas9-mediated gene editing identify regulatory variants influencing eyebrow thickness in humans |
The formation of spore-filled fruiting bodies by myxobacteria is a fascinating case of multicellular self-organization by bacteria . The organization of Myxococcus xanthus into fruiting bodies has long been studied not only as an important example of collective motion of bacteria , but also as a simplified model for developmental morphogenesis . Sporulation within the nascent fruiting body requires signaling between moving cells in order that the rod-shaped self-propelled cells differentiate into spores at the appropriate time . Probing the three-dimensional structure of myxobacteria fruiting bodies has previously presented a challenge due to limitations of different imaging methods . A new technique using Infrared Optical Coherence Tomography ( OCT ) revealed previously unknown details of the internal structure of M . xanthus fruiting bodies consisting of interconnected pockets of relative high and low spore density regions . To make sense of the experimentally observed structure , modeling and computer simulations were used to test a hypothesized mechanism that could produce high-density pockets of spores . The mechanism consists of self-propelled cells aligning with each other and signaling by end-to-end contact to coordinate the process of differentiation resulting in a pattern of clusters observed in the experiment . The integration of novel OCT experimental techniques with computational simulations can provide new insight into the mechanisms that can give rise to the pattern formation seen in other biological systems such as dictyostelids , social amoeba known to form multicellular aggregates observed as slugs under starvation conditions .
The organization of Myxococcus xanthus , the most studied species of the myxobacteria , into structures known as fruiting bodies has long been studied not only as an example of collective motion of bacteria , but also as a simplified model for developmental morphogenesis [1] , [2] . Individual M . xanthus cells do not have flagella and move on a substrate using gliding motility [3] , [4] . The fruiting body process begins when myxobacteria are starved for nutrients and , in response , the population of cells gather into large aggregates containing hundreds of thousands of cells that continue to move around within the aggregate . Eventually , the cells differentiate from motile rod shaped cells to non-motile spherical spores that can wait out the harsh conditions . During this process , a 17 kD protein known as C-signal is transferred between cells and critical to the differentiation process [5] , [6] . It has been shown that C-signal requires end-to-end alignment [5] , that C-signaling requires cells to move[7] , and that C-signal accumulates on cells throughout development process and peaks when spores form [8] . Although the nascent fruiting body contains on the order of cells , only 1% of the cells in a fruiting body become viable spores [9] . The remaining cells , which constitute the bulk volume of the fruiting body , fail to become spores , lyse , and their extracellular material , polysaccharides in particular , is somehow integrated into the internal structure of the fruiting body . Part of the cell debris would serve as a source of nutrients for cells moving in the mound . Despite the fact that Scanning Electron Microscope ( SEM ) images showed what appeared as a dense homogeneous packing of spores [9] , it is difficult to resolve such a homogeneous distribution of spores with the fact that a bulk of the cells never become spores . We present , in this paper , an integrative approach that combines a new experimental technique using infra-red optical coherence tomography ( OCT ) with computational models to study the patterns of spores as they form within a fruiting body . Viewing fruiting bodies by this tomography method revealed that regions of high spore concentrations in the fruiting body were surrounded by less dense regions . Based upon the experimental findings , we developed a hypothesis based on the the underlying biology of M . xanthus that can explain the pattern without chemotaxis or long-range diffusive chemicals which have been used to explain other types of biological patterns . Our hypothesis is that the basic mechanism behind this patterning is that cells move along slime trails and reverse to improve alignment so they can C-signal . The increase of C-signal is done locally by cells which coordinates the differentiation process in order for spores to form in pockets of clusters throughout the mound . We present an extended description of the hypothesis from the biological viewpoint in the Results section . To test if the hypothesis is plausible , we developed two separate models that use different degrees of biological detail . In the two models that we present , we focus on the later stage of the fruiting body process when cells have already aggregated in some domain and the sporulation is beginning . The general modeling approach studies how the coordinated self-propelled cell movement and C-signaling can give rise to the spatial patterns of spore clusters observed in experiments . We begin with a one dimensional ( 1D ) model that tests how jamming and C-signaling generate clustering on a circular track . The second two dimensional ( 2D ) model implements cell shape and movement and utilizes C-signaling that requires end-to-end cell alignment . The simplicity of the 1D model allows us to study a very wide range of parameter values in order to gain insight into the relative importance of two specific aspects of cell clustering — jamming by cells encountering spores and impact of C-signaling . The 2D model is more computationally demanding and cannot explore the same range of parameter values , but instead focuses on adding more biological details such as connecting cell shape and movement with C-signaling that requires alignment . Model simulation results were compared with the experimentally observed clustering of spores . The hypothesized mechanism based on cells aligning and signaling by contact to coordinate sporulation was able to recover the structure of the fruiting body observed in the experiments . In addition to gaining new insight into bacterial fruiting body formation , better understanding of cell self-organization based on cell-cell signaling and interaction is of real importance for developmental biology .
We used CTT agar plates for both normal and starved growing conditions of M . xanthus . CTT-agar plates are made by adding 1 . 5% agar by weight to a TPM ( 10 mM Tris pH 8 . 0 , 1 mM , 8 mM ) buffer which has 1 . 0% Casitone by weight . The fruiting body plates are prepared by reducing the amount of Casitone from 1 . 0% to 0 . 1% . This results in a starvation condition for the cells growing on the surface and the fruiting body process is carried out . Scans of mound were made 1–2 weeks after inoculating the starvation plates . This is well beyond the 2–3 days needed to form the fruiting body mounds and ensured that the mounds were no longer actively forming . In order to examine 3D bacterial density distribution , we employed non-invasive high-resolution infrared optical coherence tomography ( OCT ) [10] . OCT is an interferometric technique for imaging in scattering media which measures an in-depth profile of optical scattering using light of low coherence . Consequently , a cross-sectional image is created by scanning the beam position laterally over the sample . The fundamentals of OCT relies on the fact that in a scattering medium only the reflected ( non-scattered ) light is coherent . Correspondingly , an optical interferometer is used to separate scattered light and detect coherent light . A commercially available imaging system ( Niris OCT , Imalux Corporation , Cleveland , OH ) was used in our work . The time domain OCT system [11] uses common path optical topology , a 1310 nm central wavelength with 55 nm bandwidth , with in-depth resolution of in air and in water . Acquisition time for an image of maximal resolution up to is 1 . 5 seconds . The OCT probe , mounted on flexible cable , has a diameter of 2 . 7 mm and a 2 mm lateral field of view with lateral resolution 25 . It can be easily mounted in close proximity to the sample to image its full depth ( about 1–2 mm above the sample ) . While the OCT depth-scan is performed by the piezofiber delay line , the lateral OCT scan is performed either by moving the sample or the probe beam illuminating the sample . The OCT was recently used for the analysis of collective motion in suspensions of swimming bacteria [12] . Since the resolution of the OCT is of the order , it can only distinguish the large scale structure of the fruiting body , such as cavities and clusters of spores , and not individual bacteria cells . Scanning electron microscopy ( SEM ) is a technique that has been used previously to show that spores within a mound are tightly packed [9] . However , preparation a fruiting body for SEM is invasive and requires dehydration in alcohol and other drying agents which likely compressed the structure , removing regions that contain significant amounts of hydrated polysaccharide and extracellular material . Other researchers concerned with these limitations used laser scanning confocal microscopy ( LSCM ) with fluorescently labeled bacteria to probe the internal structure of mounds [13] . Preparation for LSCM , like OCT , does not require dehydration so the fruiting bodies can be grown and imaged on agar plates without additional processing . However , LSCM also faces limitations concerning the excitation and emission wavelength of Green Fluorescent Protein ( GFP ) . LSCM typically uses optical wavelength light to excitation a sample and capture the emitted light from a particular focal plain by blocking the out-of-focus light . The use of LSCM to explore fruiting bodies mounds raises the following concerns . Researchers observed that GFP expression appeared to form an outer shell for the dome-like mound . They concluded that the core was likely to be a hollowed out region supported by the extracellular polysaccharide . While they did argue against a differential in GFP expression by cells in the shell and cell in the core , there is another possible explanation for the shell-like pattern . GFP fluorescence uses 480 nm excitation light and emits at a wavelength of 510 nm [14] . As light travels through any media , it undergoes both Raleigh scattering by particles smaller than the wavelength of the light and Mie scattering by particles that larger than the wavelength of light . Raleigh scattering is inversely proportional to the wavelength raised to the fourth power . This means that excitation light of 480 nm scatters 60× as much as IR light with wavelength equal to 1310 nm , which allow IR light to probe more deeply than visible light . A fruiting body mound is composed of micron-sized spores as well as countless molecules ranging widely over the nanometer scale ( most importantly smaller than visible wavelength of GFP ) . The infrared light used by the OCT provides a better probe for the internal structure of the fruiting body . The trade-off for better scattering depth is the reduce resolution . In addition to the scattering , M . xanthus is known to produce carotenoids designed to absorb visible light [15] . Production of carotenoids is often avoided in lab conditions by growing plates in the dark . Scattering and absorption will determine the maximum depth at which GFP is visible . It was found that the maximum depth for GFP in lung tissue was [14] . The average height of mounds in [13] was found to be 27 microns with some mounds reaching heights of 45 microns . It is quite possible that visible light of GFP cannot be detected from the core of the fruiting body . This problem is overcome by using longer wavelength light , like the infrared ( IR ) light used by OCT . Microscopy was performed on an inverted Olympus microscope and images were taken with a Spot Boost EMCCD 2100 ( Diagnostic Instruments Inc . ) high sensitivity camera . The camera was still sensitive to the IR probe from the OCT device which appeared as a small white dot in the field of view . This is what enabled the accurate positioning of the probe over specific mounds . In order to obtain 3D OCT scans of fruiting bodies , we search for a desired region using bright field microscopy at low magnification . Then , using a three-axis micrometer driven translational stage , we position the probe head over the site . The inverted microscope allows for accurately positioning the probe because the mounds can still be seen while the probe head is in the optical path of the microscope ( see figure 1b ) . Once positioned , a scan is made by making 2D slices of the mound while the stage is being moved perpendicular to the lateral scan of the probe . The automation was controlled by a LabView program which moved the stage and triggered the OCT for a single slice . The scan parameters could be varied in order to scan a large area of the swarm plate to see many mounds or centered on one particular mound . The distance between slices was usually . The Imalux Imaging system can be set so that the detected signal for a particular bin is averaged over multiple cycles . This is analogous to a longer dwell time per pixel in Laser scanning microscopy . Averaging was typically done for 20 cycles . There is a trade off between resolution and scan period . The depth of scan also affects the scan period . In order to analyze the 3D OCT intensity scan , the 2D slices are loaded into Matlab as a 3D matrix . The raw data is a Red-Green-Blue ( RGB ) -value image that is converted to an 8-bit grayscale image with an intensity range between 0 and 255 . For the OCT scans , the largest intensity values observed were between 180 and 190 . Towards the perimeter of each cross section , the values drop to below 10 corresponding to the surface of the mound . There are interior regions where the intensity values reach as low as 80 . In-plane cross-sections were extracted from the 3D data by fixing the z-value to obtain a 2D image in the xy-plane parallel to the agar surface . For each in-plane cross section , the image moments and central image moments are given bywhere is the grayscale intensity data for the cross section that has ( columnrow ) pixels . The centroid for the cross-section is given by . From the centroid , the second central image moments can be calculated as , , and . The covariance matrix for the cross section is given by . Finally , the eigenvalues and eigenvectors for are used to calculate the orientation , eccentricity , and major and minor axes of the in-plane cross section of a mound . The 3D renderings of the mound are made using the isosurface function in Matlab . The outer shell is an isosurface using an isovalue of 10 and given a large transparency . For the multi-layer isovolume rendering , the highest isovalues which are largely in the interior were rendered with higher opacity . Subsequent lower isovalues were drawn with decreasing opacity so that the internal structure could be visualized . To study how the motion of cells and cell-contact signaling within a developing fruiting body could give rise to the patterns characterized by dense pockets showing up as a kind of bumpiness in the OCT , we use computational models that captures the movement of cells in a fruiting body environment . Previously , a 3D Lattice Gas Cellular Automata model was used to study cell aggregation and fruiting body formation as well as spore transport and spatial organization [16] , [17] . In both models , we begin simulations with cells in an aggregate and accumulating C-signal . While the vast majority of cells die during the fruiting body process , as evidenced by the fact that 1 . 0% or less become spores [9] , a key importance of cell death is that nutrients are made available to the surviving cells . How the cells die is less pertinent to the current study . The nutrients that come from the dead cells are what allow cells to continue moving and C-signaling in order to reach the level needed for sporulation . We make the assumption that the cells in the models have sufficient energy to maintain their movement . While cell death is not explicit in the model , by enforcing that cells continually move in the aggregate we assume that a source of energy is available . Without enough energy to move , the lack of motility would prevent cells from being able to C-signal [7] . In reality , these energy levels would be maintained by nutrients from the cells in the aggregate that lack sufficient energy and lyse . This approach is used to specifically study how the coordinated cell movement and C-signaling can give rise to the spatial patterns of spore clusters .
The use of the OCT method to scan fruiting bodies was expected to accurately reveal the internal structure due to the improved scanning depth of IR light ( technical details for this reasoning are made in the Materials and Methods subsection ‘Comparison of OCT Method with SEM and LSCM’ ) . In fact , we suspect the core of the fruiting body mound cannot be probed with visible light . Evidence for this is seen in the dark appearance of mounds in bright field images ( see figure 3 ) . It was discovered that microscopy images of mound structure could be made by using the IR probe as a transmitted light source for the inverted microscope . We centered the infrared light from the probe over a mound to obtain the images which revealed details not visible in the bright field images of mounds . A side by side comparison of bright field images using optical light and IR light can be seen in figure 3 . While optical light does not transmit through the mound , the IR light passes through the mound and reveals contours and structure not seen in the bright field images . The IR transmitted light image showed structure that is similar to the structure we find in the 3D renderings of the OCT tomograms ( see ‘Large scale inhomogeneous internal structure of fruiting bodies’ below ) . Individual slices made by the OCT can be seen in figure 4 . Early scans suffered from light reflecting back to the probe from the top of the mound . This reflection is problematic because the probe detects an increased signal directly above the mound . Additionally , the reflected light cuts down on the amount of light moving into the mound which reduces the noise to signal ratio . It was also found that dry scans also suffered from a lensing effect due to the change of index of refraction from air to mound . This lensing effect resulted in the OCT instrument detecting higher levels of backscattering underneath the mounds below the surface of the agar . ( See bright area below the mounds in figure 4A and C ) . To improve the quality of image , we place a drop of microscopy oil on the surface of the agar plate and submerged the probe head into the oil . Figure 4 demonstrates the difference between imaging in air and in oil . We also performed tests with glycerol that showed similar improvement . However , because the oil is immiscible with the water in the agar and does not evaporate , it provided a better submersion media for the scans . The submerged scans produced a better contrast for signal to noise within the mound and cut down on the reflected light . It has been shown that the refractive index for bacteria is [21] . This explains why microscopy oil , with a refractive index of 1 . 53 , is an ideal submersion media for the bacterial mounds . While scanning in oil , the OCT device corrected for the index of refraction by adjusting the number of pixels used in a scan . This required rescaling the image in the vertical direction to recover the 1∶1 aspect ratio of the scan . Finally , a ratio of 3 . 3 microns/pixel was adopted . The detailed analysis of an OCT scan of a fruiting body mound was carried out to study its internal structure . The image analysis ( described in Materials and Methods ) was able to quantify both the internal structure and the external shape of the mound . Scans show no indications of a shell and core structure that was suggested by the LSCM study [13] . Instead , they reveal a continuous inhomogeneous density structure containing intensity patches that could reflect variations in the spore concentrations . These domains can be seen in both the three-dimensional renderings ( figure 5 ) as well as individual in-plane cross-sections ( figure 6A ) . In these images , the optical density , measured by intensity , depends on the density of the scatters in the media , i . e . the concentration of spores . Hence , the intensity levels of the OCT scans are proportional to the density of the mound . Each in-plane cross section is analyzed as an elliptical domain whose major and minor axis are obtained from the covariance matrix . The linear decrease of the axes as the height increases is consistent with a cone-like mound ( see figure 7 ) . The average intensity was obtained for each cross-sectional elliptical domain as well as the radial density . Figure 6D shows an example of the intensity distribution for a domain as well as the mean value of the intensity for the cross-section . Radial density plots were obtained for each in-plane cross section by averaging the intensity values for all pixels with in an elliptical annulus ( figures 6B and C ) . The standard deviation from the mean value provides a measure for the variation of the density within a particular ring of a particular domain . The radial density for multiple domains from the mound shown in figure 5 can be seen in figure 6E . The regions of reduced density may reflect cavities or lower spore concentrations , while regions of high density are suggestive of closely packed clusters of spores . The graphs in figure 6D show the highest density at the base of the mound and a consistent average density up the mound until it begins to taper off towards the top . The distribution of intensity values is shown for bottom 12 layers of the mound ( i . e . the in-plane cross sections ) . The graph in figure 6E shows that the average radial density ranges between the values of 120 and 140 for distances up to approximately 15 pixels ( ) before tapering off . The radial density plots for individual cross-sections reveal the variation that exists within a given elliptical ring ( shown as error bars ) as well as intensity variations moving radially outwards from the centroid . For the in-plane cross-section and , we observed the region of increased intensity of approximately 6 pixels ( ) at the distance of 12 pixels . In addition to the radial distribution , we performed measurements of the angular distribution of intensity . This was done by dividing the domain into sectors ( i . e . pie slices ) and averaging the intensity within each sector . The results for one cross-section are shown in figure 8 . The zones of lower concentration are spread out over the domain and is characterized by peaks and valleys in 2D plots of the distribution ( figure 8B ) and a smooth undulation in the polar plot ( figure 8C ) of the distribution . This measurement is repeated in simulations and provides a metric for comparison . The more striking features that are revealed by the 3D OCT scan are the large interconnected structure of caverns with in the mound . Movie S1 in supplemental material provides a more complete view of the internal structure . Scans show what appear to be regions on the scale of that are more dense than surrounding regions suggesting that large regions of highly packed clusters of spores are spread throughout the mound ( see figures 5 and 6A ) . The regions surrounding these clusters contain material that is less optically dense than the clusters of spores . This could be regions of polysaccharide and extra-cellular material or simply a region where the spores concentration is reduced . The findings from the experiments were used to contemplate the bigger picture of fruiting body formation , which is presented here . In the fruiting body process , a mound of constantly moving cells gives rise , after several days , to a mature , spore-filled fruiting body [22] , [23] ( see figure 1 ) . Inasmuch as growing cells have little ATP or other energy reserve , starvation liberates a tiny minority – 1% or fewer of the rod-shaped cells – to cannibalize the other 99% of the developing cells to harvest enough metabolic energy for developmental protein synthesis and for keeping the cells in constant motion as they develop further towards becoming spores . Collisions between moving cells eventually raises the morphogenetic C-signal to a threshold level that is able to trigger differentiation of rod-shaped motile cells into spherical spores [8] , [24] . However , before they sporulate , the developing cells aggregate by moving back and forth in a system of traveling waves that surrounds the swarm edge , a process that has been captured in a time-lapse movie [25] . Like the traveling waves assembled for fruiting body aggregation , very similar waves are observed when M . xanthus feeds and grows on E . coli prey cells [26] , [27] . In fruiting body development , the vast majority of myxobacterial cells are being eaten by their few siblings that are destined to become spores . Although prey protein , nucleic acid , and lipid are consumed for their calories , the polysaccharides are indigestible . The myxobacterial lytic enzymes include no polysaccharide hydrolases except lysozyme [28] . Polysaccharide fibrils [29]–[31] survive in the waves as an extensive elastic meshwork that surrounds and bundles the cells as well as their undigested bits of cellular debris . The few prespore cells that survive are found to be moving on trails of their polysaccharide slime that also is not digested . Trails remain intact and provide a surface favorable for gliding [32] . Departing from the traveling waves , the surviving cells are seen to migrate to the outer edge of each wave crest where they become one of the small motile aggregates [25] . Initially the motile aggregates are spaced one wavelength apart , and waves are thus the first step in fruiting body aggregation . Next , pairs of adjacent small aggregates fuse with each other to form a larger spherical aggregate of the same cell density but twice the volume . The larger aggregates fuse repeatedly with their neighbors until all the motile aggregates have assembled in a single very large aggregate . The diameter of the final aggregate , which shows signs of cell movement inside [25] , is constant from experiment to experiment and is characteristic of mature M . xanthus fruiting bodies . ( See [33] , for example . ) In this way , spores are expected to be formed on debris-laden slime trails that are suspended within a spherical motile aggregate by polysaccharide fibrils . Each slime trail would be expected to trace a truncated arc within the motile aggregate because each cell reverses direction of its motion at regular intervals [19] , [34] . Based on the foregoing description of slime trails in a dynamic motile aggregate , it follows that cells would be clustered and aligned on the many trails that would branch from each other . Since individual cells are eating each other as they move , they are also racing to be one of the predators that survive rather than one of the prey that expire . In such a race , long chains of rod-shaped cells , moving on the same trail , would break into shorter segments of fewer and fewer cells until only 1% — to take some definite number since the number depends on residual nutrient — of starting cells remain on the trails and able to transmit C-signal . When two counter-migrating cells on the same trail collide end-to-end , they exchange C-signal with each other . C-signal transmission continuously raises the signal level in each cells outer membrane through positive feedback and the Act system [8] . Eventually , positive feed-back raises the level of C-signal in each cell to the threshold required to differentiate a rod-shaped cell into a spherical , non-motile , dormant spore [8] . Depending on each cells unique history of C-signaling , individual cells will reach the threshold at different moments . Nevertheless , the closer two cells are found to each other on the same trail , the more correlated their time to reach threshold will be . When a rod-cell becomes a spore , it remains on its debris-laden slime trail , and each trail would form some arc within the aggregate mound . Because most cells are destroyed , there will be many trail arcs each of whose spores will have formed at the same time , while different arcs will have sporulated at different times . Finally , the slime trails collapse around their own cluster of spores . As this nascent fruiting body dries out , the polysaccharides will also lose water and the aggregate will shrink . Within the fruiting body , the spores are likely to be clustered in space on their own arc-shaped trail that collapses into a ball of spores and polysaccharides . To test the hypothesis described in the previous section , we ran simulations with the two models we developed .
A novel imaging technique – infrared optical coherence tomography – revealed that hundreds of thousands of spores in a mature fruiting body of M . xanthus were not packed uniformly , as was surmised previously . Rather , the spores are found clustered in high density pockets , which are no larger than 25 m in diameter , that are separated from each other by domains that have reduced concentration of spores . Why should the fruiting bodies have numerous cavities with relatively fewer spores ? Detailed analysis of the way that fruiting bodies form based on the experimental observation of dense pockets yielded a biological hypothesis of how the movement , alignment and C-signaling of self-propelled rod-shaped cells could coordinate the differentiation process ( presented above in “Biological Hypothesis” ) . In what follows , we discuss the outcome of the computer simulations designed to test the mechanism of the fruiting body formation proposed in the biological hypothesis . First , our 1D track model provided two possible explanations for the formation of the cavernous structures of the fruiting bodies . One explanation was that early sites of spore formation act as focal regions for spore clusters due to jamming of the motile rod-shaped cells that continued to move around the track . This explanation suggested that high levels of clustering could result from spores strongly inhibiting the motility of cells . The highest level of clustering was observed when cells had the smallest passing probability and no C-signal transfer . However , in simulations with higher passing probabilities , ( i . e . motile cells were not strongly inhibited by spores ) , more clustering was seen when C-signal exchange by local cells was present than when only jamming was considered . Experimental movies from our previous study on cell-cell collisions [35] demonstrate the flexibility of myxo cells which would allow easy resolution of collisions with spores . This indicates that having a higher passing probability in the 1D model is more biologically realistic . A mechanism of only low passing probability when spores strongly inhibit cell motility but cells do not signal is not strongly supported by the experimental observations . Rather , the 1D model shows that C-signaling can increase the level of clustering in simulations with higher passing probabilities . The 1D model simulations initially confirmed that contact-based C-signaling would generate spore clusters when the cell-spore interaction was not characterized by strong spatial jamming . These findings were the motivation for focusing on the movement and alignment of cells in a more detailed model . Thus , the 2D model was used which could account for the biological details such as cell-shape , movement , and alignment-dependent C-signaling . The 2D model simulations have shown how the patterns of spore clusters could be produced by cells moving , aligning and C-signaling to coordinate differentiation . In the simulations , spores begin to form within a disc as small clumps ( see figure 10B ) . The reversals of cells within the disc cause them to move back and forth along specific trajectories or arcs within the fruiting body . Cells that spend time moving along the same trajectories in end-to-end alignment accumulate C-signal at similar rates . This leads to spores forming in clusters throughout the mound . The simulations we performed to test the hypothetical mechanism resulted in pattern formation consistent with the experimental data . ( Compare figure 10E with figure 6A ) . To summarize , we first formulated a hypothesis based upon the experimental observation of spore patterns in fruiting bodies . We hypothesized that pockets of dense regions of spores form because cell movement , alignment and signaling result in coordination of the cell differentiation . The 1D simulations demonstrated that cell-signaling was capable of regulating the level of clustering inside a fruiting body . The 2D model simulations determined what patterns of spore clustering would emerge from cells aligned movement along slime trails and C-signaling by the end-to-end contact . In addition , the movement and interaction of cells in the 2D model included cell-cell and cell-spore collisions as well as cell reversals that reinforced alignment within the aggregate . We found that the coordinated movement of cells — by way of self-propelled motion , slime trail following , cell-cell and cell-spore collisions , and cell reversals — can facilitate the contact-dependent signal accumulation that drives cell differentiation into spores . The integration of novel experimental observations with computational simulations provided new insight into the mechanisms that could give rise to the structure with a pattern of dense spore pockets seen during fruiting body formation . This can be improved upon through use of newer OCT devices with better resolution and even applied to other biological systems of cell aggregation such as that seen in dictyostelids , social amoeba known to form multicellular aggregates observed as slugs under starvation conditions . Understanding how cells can undergo differentiation under specific spatial patterning is important to biology in general . It is known that chemical signals and reaction-diffusion processes can lead to coordination of cell patterning and differentiation . In the fruiting body process , we have shown how this patterning and differentiation could arise in the absence of a diffusive signal . | Understanding bacteria self-organization is an active area of research with broad implications in both microbiology and developmental biology . Myxococcus xanthus undergoes multicellular aggregation and differentiation under starvation and is widely used as a model organism for studying bacteria self-organization . In this paper , we present the findings of an innovative non-invasive experimental technique that reveals a heterogeneous structure of the fruiting body not seen in earlier studies . Insight into the biological mechanism for these observed patterns is gained by integrating experiments with biologically relevant computational simulations . The simulations show that a novel mechanism requiring cell alignment , signaling and steric interactions can explain the pockets of spore clusters observed experimentally in the fruiting bodies of M . xanthus . | [
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] | 2012 | Interconnected Cavernous Structure of Bacterial Fruiting Bodies |
The Rift Valley fever virus ( RVFV ) is an arthropod-borne phlebovirus . RVFV mostly causes outbreaks among domestic ruminants with a major economic impact . Human infections are associated with these events , with a fatality rate of 0 . 5–2% . Since the virus is able to use many mosquito species of temperate climates as vectors , it has a high potential to spread to outside Africa . We conducted a stratified , cross-sectional sero-prevalence survey in 1228 participants from Mbeya region , southwestern Tanzania . Samples were selected from 17 , 872 persons who took part in a cohort study in 2007 and 2008 . RVFV IgG status was determined by indirect immunofluorescence . Possible risk factors were analyzed using uni- and multi-variable Poisson regression models . We found a unique local maximum of RVFV IgG prevalence of 29 . 3% in a study site close to Lake Malawi ( N = 150 ) . The overall seroprevalence was 5 . 2% . Seropositivity was significantly associated with higher age , lower socio-economic status , ownership of cattle and decreased with distance to Lake Malawi . A high vegetation density , higher minimum and lower maximum temperatures were found to be associated with RVFV IgG positivity . Altitude of residence , especially on a small scale in the high-prevalence area was strongly correlated ( PR 0 . 87 per meter , 95% CI = 0 . 80–0 . 94 ) . Abundant surface water collections are present in the lower areas of the high-prevalence site . RVF has not been diagnosed clinically , nor an outbreak detected in the high-prevalence area . RVFV is probably circulating endemically in the region . The presence of cattle , dense vegetation and temperate conditions favour mosquito propagation and virus replication in the vector and seem to play major roles in virus transmission and circulation . The environmental risk-factors that we identified could serve to more exactly determine areas at risk for RVFV endemicity .
The Rift Valley fever virus ( RVFV ) , a member of the genus Phlebovirus in the family Bunyaviridae , was first isolated in 1930 during an outbreak in Kenya . Rift Valley fever ( RVF ) occurs endemically and epidemically in most parts of sub-Saharan Africa and epidemically in Egypt , Madagascar and the Comoros . In 2001 it was detected for the first time outside of Africa during an outbreak in Yemen and Saudi-Arabia [1] , [2] , [3] , [4] , [5] . The disease is mostly apparent in epizootic events with large numbers of sick cattle , and a high abortion rate in pregnant animals ( “abortion storm” ) , with adverse economic consequences for cattle herders , including bans on animal trade [4] . Transmission to humans is common during such events . In the majority of cases , human infection is oligo- or asymptomatic , but may cause hepatitis , hemorrhagic fever , encephalitis and retinitis , with fatality rates of 0 . 5 to 2% , and permanent vision impairments after retinitis [4] . Contrary to the assumption of virus persistence and inactivity between outbreaks , some evidence for inter-epidemic circulation of RVFV has been reported from the Senegal and from northern Kenya , using a serology approach to detect antibodies in samples from children born after the last reported outbreak [6] , [7] . The most important vectors for RVFV are Aedes and Culex mosquitoes . However , RVFV has also been isolated from Anopheles spp , Simulium blackflies , sand flies and Amblyomma ticks [2] , [4] , [8] , which may represent remnants of a blood meal rather than the ability to transmit the pathogen . Direct transmission through infectious body fluids is of relevance mainly during epizootic/epidemic events [5] , [9] . As many competent vector species occur outside Africa , a high potential for further geographical spread is attributed to the virus , and RVF is classified as an emerging disease [4] , [10] . RVF outbreaks are known to occur predominantly after unusual flooding events . Aedes mosquito species are seen as vectors and reservoir , since their transovarially infected eggs withstand desiccation and larvae hatch when in contact with water [6] , [11] . Transovarial transmission is assumed as mechanism of virus persistence between epizootic events . After flooding , the Aedes mosquito populations will multiply in the persisting water collections , and develop into infectious adult mosquitoes . The RVFV may amplify in wild and domestic ungulates and may reach epizootic and epidemic dimensions [8] . The presumed link between extraordinary flooding events and RVF outbreaks was validated , among others , by a successful prediction of the 2007 outbreak in Somalia , Kenya and northern Tanzania , using climate modelling [12] . A number of variables associated with higher likelihood for RVFV Immunoglobulin G ( RVFV IgG ) positivity have been identified . Among them are the proximity to perennial surface water bodies and proximity to ruminants [7] , [13] . Here we report a cross-sectional seroprevalence study that used samples from 1228 participants collected during a cohort study ( EMINI ) from the Mbeya region in Southwestern Tanzania , an area from which no RVF disease activity has been reported previously . The objective was to assess any RVFV circulation that had possibly remained undetected , and to describe infection patterns and factors associated with seropositivity .
Both EMINI and this substudy were approved by Mbeya Medical Research and Ethics Committee , Tanzanian National Institute for Medical Research – Medical Research Coordinating Committee , as well as by the Ethical Commission of University of Munich . Each EMINI participant had provided written informed consent before enrolment . Parents consented for participation of their children . Data and samples for this study were collected between June 2007 and June 2008 during the second annual survey of the EMINI ( Evaluating and Monitoring the Impact of New Interventions ) cohort study . Before the start of EMINI , a census of the complete population had been conducted in nine geographically distinct sites of the Mbeya Region in Southwestern Tanzania , which had been selected to represent a wide variety of environmental and infrastructural settings , including urban and rural sites , different proximity to main roads , elevation above sea-level etc ( Figure 1 ) . During the census we collected basic information regarding the households and their inhabitants , and recorded all household positions , using handheld GPS receivers . Ten percent of the census households and all their inhabitants were chosen by geographically stratified random selection to participate in the 5-year longitudinal EMINI cohort study , resulting in a representative sample of the population in the nine study sites . Every year , each participating household was visited to conduct structured interviews and to collect blood and other specimen from all household members . Blood samples were cryo-preserved after cells were separated from serum . For this substudy , we stratified the 17 , 872 participants , who had provided a blood sample in the second EMINI survey , by age , gender , altitude of residence and ownership of domestic animals ( mammals ) , to be able to assess factors of interest that were identified in the literature but might have been underrepresented in the general population . We employed disproportionate random sampling with equal participant numbers for each stratum to identify 1228 samples from participants above the age of 5 years to be tested for RVFV IgG . During the annual EMINI visits , we conducted interviews with the head of each household regarding the socio-economical and infrastructural setting in and around the household . With this information we constructed an SES score that characterizes the socio-economic situation of each household , employing a modification of a method originally proposed by Filmer & Pritchett that uses principal component analysis and has been widely applied to assess wealth and poverty in developing countries [14] , [15] , [16] . The score included the following information: Availability of different items in the household ( clock or watch , radio , television , mobile telephone , refrigerator , hand cart , bicycle , motor cycle , car , savings account ) ; sources of energy and drinking water; materials used to build the main house; number of persons per room in the household and availability and type of latrine used . Population- and livestock-densities were calculated using data and household positions collected during the initial population census . Elevation data were retrieved from the NASA Shuttle Radar Topography Mission ( SRTM ) global digital elevation model , version 2 . 1 [17] , [18] . Land surface temperature ( LST ) and vegetation cover ( EVI = enhanced vegetation index ) data were retrieved from NASA's Moderate-resolution Imaging Spectroradiometer ( MODIS ) Terra mission which “are distributed by the Land Processes Distributed Active Archive Center ( LP DAAC ) , located at the U . S . Geological Survey ( USGS ) Earth Resources Observation and Science ( EROS ) Center ( lpdaac . usgs . gov ) . ” [19] . LST data ( Version MOD11A2 ) have 8 days temporal and ∼1 km spatial resolution , EVI data ( Version MOD13Q1 ) have 16 days temporal and 250 m spatial resolution . Both , LST and EVI data were processed in the following way to produce long-term averages: After download via FTP , data surfaces for every 8 day period for the years 2000 to 2008 ( LST ) and every 16 days period for the year 2007 ( EVI ) were reprojected to Universal Transverse Mercator projection ( zone 36 South ) using the MODIS reprojection tool ( MRT ) [20] and imported into Idrisi GIS software ( version 32 , Clark Labs , Worcester , MA , USA ) . In Idrisi , 8 year averages of annual average and maximum day-LST and average and minimum night-LST and 2007 EVI averages were calculated for each pixel utilising only those pixels that were “good quality” according to the quality assessment layers that are distributed together with the actual data . Then LST was converted to degrees Celsius and EVI was converted back to its native range between −1 and +1 . All above environmental data were then combined with the houshold position data in a GIS database using Manifold System 8 . 0 Professional Edition ( Manifold Net Ltd , Carson City , NV ) . Population- , household- , and livestock-densities , LST , EVI , and elevation data were averaged for a buffer area within 1000 meter radius around each household in order to characterize the ecological situation around the household . This approach was preferred to using the respective spot values at the household position , because spot data are more prone to random error than averages for a wider area . Anti-RVFV IgG was detected by indirect immunofluoerescence assay ( IIFA ) , following a methodology adapted from Swanepoel [21] . Each serum sample was screened for the presence of anti-RVFV IgG , using a commercial biochip with a mixture of infected and non-infected Vero E6 cells on one field ( positive field ) and non-infected Vero E6 cells on a negative control field ( Euroimmun , Lübeck , Germany ) . Sensitivity and specificity of the IIFA test were tested using 20 negative sera from German blood donors and five sera positive for IgG against Sandfly Toscana virus , Sandfly Naples virus , Sandfly Sicilian virus , Puumala virus , Tahyna virus and Bunyamwera virus . No cross reactivities with other members of the genus Phlebovirus or the viruses of other genera of the family Bunyaviridae were detected . Serum samples were screened in a dilution of 1∶10 , using standard procedures for IIFA . A rabbit anti-human IgG FITC-labelled antibody ( DAKO , Hamburg , Germany ) was used as conjugate . A sample was classified as positive if a typical fine granular cytoplasmatic fluorescence in some groups of cells on the positive field of the biochip was detected , with no detectable fluorescing cytoplasmatic signal in the negative field . Each sample was independently assessed by two experienced observers . Results were compared and re-tested if discrepant . A part of the positive sera was re-tested by titration , and all tested sera were found to have IgG titres between 1∶20 and 1∶640 . Stata statistics software ( version 11 , Statacorp , College Station , TX , USA ) was used for all statistical analyses , maps were produced in Manifold System 8 . 0 Professional Edition ( Manifold Net Ltd , Carson City , NV ) . After exploratory data analysis , it became clear that RVFV seroprevalence in Bujonde-Kajunjumele ( BK ) subsite was much higher than in all other study locations . We therefore decided to first analyse data for BK separately before trying to develop models including the data for all sites . Since none of the continuous variables that we examined was normally distributed according to the Shapiro-Wilk and Shapiro-Francia tests for normality , the median and interquartile range ( instead of mean and standard deviation ) of these variables are reported to characterize the study area and population in BK and in all other sites . This is also the reason why the non-parametric Wilcoxon ranksum test was used to assess differences between BK and all other sites regarding continuous variables . Differences between sites regarding binary variables ( RVF seropositivity , gender and cattle ownership ) were assessed by chi square testing . The association of binary RVFV IgG status with possible risk factors was examined using uni- and multi-variable poisson regression models with robust variance estimates adjusted for within household clustering [22] , [23] . Uni-variable regression models were used to identify possible risk-factors for inclusion into the multi-variable model for this site . Variables with a p-value<0 . 2 in uni-variable regression and other variables that did not fulfill this criterion , but where an association with RVFV IgG seemed likely due to biological reasons ( gender , and all variables related to the presence of ruminants ) , were further evaluated in multi-variable regression models and were retained in the final multi-variable model if their p-value was <0 . 1 . Because most variables characterizing the natural environment ( LST , vegetation , elevation and distance to Lake Malawi ) showed strong collinearity , they were not included into the same model but entered one by one into models adjusted for the other variables that were included into the final model . Once the final multi-variable model for BK site was identified , we used the same approach to identify a multi-variable model where data for all sites including BK were pooled . Prevalence ratios ( PR ) and 95% confidence intervals for covariates mentioned in the text refer to multi-variate analysis within BK site , if not mentioned otherwise .
Of the 1228 analyzed sera , 5 . 2% ( 64 sera ) were positive for RVFV IgG . This translates into an estimated overall population prevalence of 3 . 1% when extrapolated from our stratified sample to the underlying population of the 9 study sites . We found a unique local maximum of 29 . 3% ( 95% confidence interval ( CI ) 22 . 2–37 . 3 ) seroprevalence in Bujonde-Kajunjumele ( BK ) , a subsite of the Kyela site , which is situated close to Lake Malawi . The prevalence in the other sites ranged from 0 . 0% to 3 . 4% ( table 1 , Figure 2 , 3 ) . We thus decided to analyze covariates within the high-prevalence setting of BK site , and to compare BK to the low-prevalence sites , in order to better understand possible causes for this marked difference . With an altitude range of 479 to 492 meters , BK is the lowest of our study sites , while the other sites range from 499 m to 2316 m ( table 1 , Figure 1 ) . The two Kyela subsites BK and Katumba-Songwe are the only sites south of the Poroto mountain range , and receive the highest amount of annual rainfall ( 1956 and 2292 mm , respectively ) , whereas the average across all sites is 1473 mm . Further characteristics of BK site , compared to all other sites , are listed in table 1 . Of special relevance are denser vegetation , lower temperature variability ( higher minimum and lower maximum land surface temperatures ) , higher cattle density and more frequent ownership of cattle , which is presumed to be the main animal host of RVFV . During the rainy season , wide areas close to Lake Malawi are flooded , especially where the terrain is marshy and barely above the Kiwira river's water level ( Figure 4 ) . As demonstrated in Figure 5 , RVFV IgG prevalence rises with age in our study population . This is in agreement with the poisson regression results for BK ( table 2; prevalence ratio ( PR ) 1 . 02 per year of age , 95% CI 1 . 01–1 . 03 ) , and for the pooled results from all sites , where age is significantly associated with rising RVFV IgG prevalences , both in uni- and in multi-variable regression models ( table 3; PR 1 . 02 per year of age , 95% CI 1 . 01–1 . 03 ) . Increasing socio-economic status is associated with decreasing RVFV IgG prevalences ( BK site: PR 0 . 60 per unit , 95% CI 0 . 40–0 . 90 ) , whereas gender appears not to influence RVFV IgG prevalence in the study population ( uni-variable PR for male gender as compared to female in BK , 1 . 0 , 95% CI 0 . 61–1 . 65 ) . According to the multi-variable models , cattle ownership is significantly associated with RVFV seroprevalence , both in BK and in all sites ( PR 1 . 81 , 95% CI 1 . 15–2 . 85 for BK; PR 1 . 76 , 95% CI 1 . 15–2 . 71 for all sites ) , although it's uni-variable association in BK is far from significant . Cattle density per square kilometer is a significant prognostic factor in all sites ( PR 2 . 06 per 100/skm , 95% CI 1 . 64 to 2 . 59 , multi-variable model ) , including BK , where mean cattle density is higher than in the other sites . Due to collinearity between the examined environmental variables , these could not be simultaneously included into one model , but were entered one at a time into multi-variable models that were adjusted for age , SES , cattle ownership , and – for the all-sites pooled model – cattle density . Of these environmental variables , vegetation density ( EVI ) results in the model with the best fit , both in BK and in the pooled analysis . However , most other environmental factors are also strongly associated with RVFV IgG prevalence . It is noteworthy though , that maximum and average land surface temperature ( LST ) during the day have significant negative associations ( PR 0 . 87 per °C , 95% CI 0 . 81–0 . 94 for max . LST; PR 0 . 73 , 95% CI 0 . 61–0 . 86 for average day LST , both multi-variable for BK ) , whereas average LST during the night has a positive association with RVFV seroprevalence ( PR 2 . 51 per °C , 95% CI 0 . 94–6 . 70 ) . Minimum LST was less strongly associated than the other LST variables in the pooled analysis and unrelated to RVFV IgG prevalence in BK site . Other factors that we examined ( population density and the ownership of livestock other than cattle ) do not show any strong associations with RVFV infection in our study population ( data not shown ) . Within the EMINI study population our group also collected data on chikungunya virus IgG , P . falciparum malaria ( ICT Malaria P . f . /P . v . ICT Diagnostics , Cape Town , South Africa ) and presence of W . bancrofti filarial antigen ( TropBio® Og4C3 serum ELISA , Townsville , Australia ) . We found that on a household level , RVFV IgG positivity was strongly associated with chikungunya virus IgG in BK site and in all other sites ( PR = 4 . 3; 95% CI 2 . 3–8 . 1; PR = 5 . 3 , 95% CI 2 . 1–13 . 5 , respectively ) , and with filarial antigen ( PR = 2 . 2; 95% CI 1 . 3–3 . 7 ) and P . falciparum malaria ( PR = 4 . 2 , 95% CI 3 . 3–5 . 5 ) in BK . No association was found with Schistosoma haematobium infection in BK , nor in the other sites .
Figure 4 shows that RVFV seroprevalence increases with age , which is in line with the regression results for BK and for all sites . This suggests an endemic circulation of RVFV in our study area , rather than a single outbreak event as reason for the detected seroprevalence . The inverse association of SES with RVFV IgG means that more affluent people are at lower risk of infection . This has been described for many different infectious diseases in a wide range of settings . Importantly , cattle ownership was not used for SES calculation , as it is a direct risk factor . Despite the strong associations that we found for age and SES , most of the examined environmental variables were still significantly associated with RVFV IgG prevalence , when adjusted for possible socio-economic confounding in the multivariable models showing that their association with RVF is independent . Our findings that cattle ownership and density of cattle in the area are important factors for RVFV seropositivity were to be expected , since ruminants are the main animal host of RVFV [4] . Cattle owners in BK have the habit of tethering their animals on the doorsteps of their houses at night for fear of theft , providing an animal reservoir of RVFV in proximity of humans , and reportedly increasing the number of Culex mosquitoes in the house [24] . Some previously described risk factors for RVF were confirmed in our study: dense vegetation and proximity to perennial water bodies were found associated with RVFV seropositivity in ruminant herds in the Senegal and in humans in Gabon [13] , [25] . These and other risk factors seem to make the BK site uniquely favorable for human and animal RVFV infection . In BK , only the low-lying areas close to Lake Malawi are subjected to regular flooding during the rainy season , whereas areas further away from the lake and at slightly higher elevation are not flooded . This phenomenon provides abundant mosquito breeding places in low-lying areas , and is a likely reason for the strong negative association of altitude with RVFV seropositivity , that – in BK site – is already visible on a per meter scale , and for the association with distance from the lake . Frequent waterlogging has lead to large areas of BK site being used for wetland rice cultivation . One report from the 2006–2007 outbreak in Kenya found that soils that retain water were more frequently found in RVF-affected areas than in other areas [26] . Over the entire study area with an altitude range of 479 to 2313 m , it seems obvious that higher elevation negatively affects mosquito breeding and survival . The association of RVFV seropositivity with other mosquito-borne diseases transmitted by Anopheles , Aedes and Culex species is in agreement with above considerations regarding the role of mosquito favorable habitats as an important factor contributing to RVF prevalence in BK site . However , in our study , RVFV seropositivity is not associated with S . haematobium , a water-borne disease . Bulinus snails , the intermediate hosts of S . haematobium , require permanent water-bodies [27] . Thus , RVFV infection in BK does not seem to depend on proximity to Lake Malawi itself , which is a reservoir for S . haematobium , but is more likely a consequence of the seasonal surface water collections that are more common close to the lake . Although it is difficult to single out the predominant factors causing the observed difference between BK and the neighboring low-prevalence site Katumba-Songwe , we presume that the difference in altitude and distance to the lake , and their impact on surface water collections , are the most important reasons . According to our results , RVFV seropositivity seems to be associated with an optimum temperature range . An adverse effect of low temperatures has been shown for RVFV replication and infectiousness , e . g . in the vector Culex pipiens [28] , [29] , [30] , while higher temperatures above 27–32°C adversely affect hatching success and size of adult Aedes aegypti mosquitoes [31] . The correlation with EVI may be explained by dense vegetation protecting water pools from being heated in the sunlight , and from cooling off at night . Furthermore , vegetation density can be regarded as a proxy for the presence of water . Seasonal increases in vegetation are associated with RVF outbreaks on a larger scale and are used for predicition [32] , [33] . Our results confirm this association on a small scale . It is a limitation of this study that only serologic findings were available for analysis , specific questioning regarding RVF-related symptoms and sequelae to assess clinical significance of serological findings was not possible because samples were analysed retrospectively . However , conduct of this study within the very well characterised EMINI cohort allowed for a detailed analysis of socio-economic , spatial and ecological covariates on a small scale . Given the persistence of IgG responses over several years , the actual date of infection cannot be deduced from these examinations , and socio-economic and environmental conditions at the time of infections may have differed from the time of participant assessment for EMINI . The presence of RVFV IgG in the younger age groups suggests an ongoing or recent virus circulation . There are no previous reports of RVF in Mbeya region , and to our knowledge the disease was never diagnosed clinically in Kyela . Since no virus isolation has yet been done in our study , it remains to be elucidated whether the cycling virus is a less virulent RVFV strain such as the apathogenic “clone 13” from the Central African Republic [34] , [35] , or whether acute cases of RVF have been overlooked in the past . Taking into account the relatively high prevalence for malaria and HIV in the area [36] , RVFV encephalitis , retinitis and hemorrhagic fever would be comparatively rare events , which may have been misdiagnosed as malaria- or HIV related morbidity , as is often the case with febrile illnesses in malaria-endemic areas [37] , [38] . In conclusion , this study finds a relatively high RVFV IgG prevalence in an area without previous reports of RVF , and identifies several environmental factors that are associated with RVF infection , independently of age and socio-economic status . If confirmed in future studies , these findings have important implications in the areas close to Lake Malawi , where health facilities and their staff should be made aware of RVF as a possible diagnosis for their patients . The environmental risk-factors for RVF infection that we identified could serve to predict areas of RVFV endemicity , in addition to outbreak prediction which can be done based on rainfall and vegetation data . It would be interesting to do further studies in similar high risk areas , since it is likely that undetected endemic cycling of RVFV is occurring in many areas apart from our study site . | We describe a high seropositivity rate for Rift Valley fever virus , in up to 29 . 3% of tested individuals from the shore of Lake Malawi in southwestern Tanzania , and much lower rates from areas distant to the lake . Rift Valley fever disease or outbreaks have not been observed there in the past , which suggests that the virus is circulating under locally favorable conditions and is either a non-pathogenic strain , or that occasional occurrence of disease is missed . We were able to identify a low socio-economic status and cattle ownership as possible socio-economic risk factors for an individual to be seropositive . Environmental risk factors associated with seropositivity include dense vegetation , and ambient land surface temperatures which may be important for breeding success of the mosquitoes which transmit Rift Valley fever , and for efficient multiplication of the virus in the mosquito . Low elevation of the home , and proximity to Lake Malawi probably lead to abundant surface water collections , which serve as breeding places for mosquitoes . These findings will inform patient care in the areas close to Lake Malawi , and may help to design models which predict low-level virus circulation . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"neglected",
"tropical",
"diseases"
] | 2012 | High Seroprevalence of Rift Valley Fever and Evidence for Endemic Circulation in Mbeya Region, Tanzania, in a Cross-Sectional Study |
An important question in dengue pathogenesis is the identity of immune cells involved in the control of dengue virus infection at the site of the mosquito bite . There is evidence that infection of immature myeloid dendritic cells plays a crucial role in dengue pathogenesis and that the interaction of the viral envelope E glycoprotein with CD209/DC-SIGN is a key element for their productive infection . Dermal macrophages express CD209 , yet little is known about their role in dengue virus infection . Here , we showed that dermal macrophages bound recombinant envelope E glycoprotein fused to green fluorescent protein . Because dermal macrophages stain for IL-10 in situ , we generated dermal-type macrophages from monocytes in the presence of IL-10 to study their infection by dengue virus . The macrophages were able to internalize the virus , but progeny virus production was undetectable in the infected cells . In addition , no IFN-α was produced in response to the virus . The inability of dengue virus to grow in the macrophages was attributable to accumulation of internalized virus particles into poorly-acidified phagosomes . Aborting infection by viral sequestration in early phagosomes would present a novel means to curb infection of enveloped virus and may constitute a prime defense system to prevent dengue virus spread shortly after the bite of the infected mosquito .
Dengue is probably the most important mosquito-transmitted viral disease of humans worldwide . It is caused by dengue virus ( DV ) , which exists as four serotypes ( DV1-4 ) and circulates in an endemic-epidemic mode in most tropical and sub-tropical territories . Transmission of DV to humans occurs when an infected mosquito probes for blood vessels and during a blood meal , through injection of infectious saliva into the human dermis . As a member of the Flaviviridae family , DV infection involves virus uptake into endosomal vesicles that undergo acidification . The low pH induces structural alterations in the envelope ( E ) protein that lead to membrane fusion and the release of the nucleocapsid into the cytoplasm [1] . After uncoating , the RNA genome is translated to initiate virus replication . It has been proposed that non-neutralizing antibodies raised against one DV serotype may enhance infection by a heterotypic serotype [2] . This may explain why secondary infections are often associated with the more severe forms of dengue fever ( hemorrhagic fever with or without shock ) . Much research on DV relies on relevant human cell culture models due to the difficulty of establishing appropriate animal models . Progress has been made by showing that DV E protein recognizes the C-type lectin CD209 and its homologue L-SIGN and that expression of either of these lectins is sufficient to render cells permissive to DV grown in mosquito cells [3] , [4] . Recently , the mannose receptor ( MR ) has also been shown to mediate DV binding and infection [5] . Dendritic cells ( DC ) , generated from monocytes in the presence of GM-CSF and IL-4 , express CD209 , L-SIGN and the MR and are highly susceptible to DV infection [3] , [4] , [6] . These monocyte-derived DC are thought to be representative of dermal DC ( dDC ) , yet there is increasing evidence that CD209 is not expressed by dDC but primarily by dermal macrophages ( dMφ ) [7]–[9] . This underscores the importance of dMφ in early infection events and raises the question of whether dMφ are permissive for productive DV infection . Studies of these cells have been hampered by the lack of suitable isolation techniques from human skin and culture methods to generate the cells from monocytic precursors . Here , we confirmed that human dMφ express CD209 and showed that they bind DV E protein . Based on the finding that dMφ stained for intracellular IL-10 , we developed a method to generate the cells from monocytes in the presence of IL-10 . The monocyte-derived dMφ bound E protein and acquired DV in intracellular vesicles , but were resistant to viral replication . The inability of DV to grow in these dermal-type Mφ was attributable to accumulation of internalized virus particles into poorly-acidified phagosomes . These findings advance our understanding of the host innate resistance to DV at the early stages of infection and have implications for other pathogens recognizing CD209 .
Before blood and tissue samples were collected for the study , all healthy donors and patients gave written informed consent in agreement with the Helsinki Declaration and French legislation . A prospective IRB approval was not obtained since there was no need as specified by French law of the health protection act when employing healthy material destined for disposal or one-time biomedical research . A retrospective IRB approval was given . Fresh skin ( about 50 cm2 ) was obtained from patients undergoing breast reduction surgery or abdominoplasty . The skin was trypsinized to peel off the epidermis and the remaining dermis was processed as described elsewhere [10] with the modification that only collagenase type I ( 1 mg/ml , Invitrogen ) was used for 18 h at 37°C . The resulting cell suspension was pipetted and serially filtered through 100 µm and 70 µm cell strainers ( BD Biosciences ) to remove undigested tissue fragments and to obtain a homogeneous cell suspension . A DNA fragment containing the DV3 genomic region ( Swiss-Prot accession number P27915 ) coding for the prM-E protein ( 1674 nt in total , including all of prM and the E ectodomain , ending at codon 392 of E , at the end of domain III ) was amplified by PCR with forward primer 5′TTATGCATATTACTGGCCGTCGTGGCC and reverse primer 5′CTCGCCCGCAGACATGGCCTTATCGTCATCGTCGGGCCCCTTCCTGTACCA-GTTGATTTT and inserted into the plasmid pT352 . This is a shuttle vector containing selection markers for yeast and E . coli , as well as a metallotheionein-inducible expression cassette for Drosophila cells . In the construct , called pT352/DV3 sE-GFP , the DV prM-E sequence is in-frame with the Drosophila BiP signal peptide , which directs the recombinant protein to the secretory pathway . Drosophila S2 cells ( Invitrogen ) were co-transfected with pT352/DV3 sE-GFP and a vector conferring resistance to blasticidine , using the effectene transfection reagent ( Qiagen ) . The selected cells were adapted to serum-free growth medium and grown to high density before induction with CuSO4 at 500 µM . The supernatant was collected 10 days later and concentrated using a flow concentration system with a 10 KDa-cutoff membrane ( Vivascience ) , and DV3 sE-GFP was purified by affinity chromatography using a Steptactin column . The eluate was concentrated and further purified by size-exclusion chromatography , using a Superdex 200 10/300 column ( GE Healthcare ) with 0 . 5 M NaCl and 50 mM Tris ( pH 8 . 0 ) . Purified DV3 sE was concentrated to 10 g/liter in Vivaspin ultrafiltration spin columns ( Sartorius ) . Dermal cells were collected 48 h after culturing in complete medium , RPMI medium supplemented 10% fetal calf serum ( FCS ) and antibiotics ( Invitrogen ) , and 3×105 cells were incubated with 1 , 2 , 4 or 8 µg recombinant DV3 sE-eGFP fusion protein in 0 . 1 ml complete medium at 37°C for 30 min . The cells were then washed twice with complete medium and incubated with anti-CD14-APC , anti-CD1a-PE and anti-HLA-DR-PerCP mAb ( BD Biosciences ) in PBS/2% FCS for 15 min . Following 3 washes , the cells were fixed in 0 . 4% formaldehyde and analyzed by flow cytometry ( FACS Calibur , BD Biosciences ) . The relative MFI for 3 donors was determined in triplicate after gating for CD1a+HLA-DR+ or CD14+HLA-DR+ cells using the following formula: ( MFI ( FL1 ) protein sE-eGFP – MFI ( FL1 ) no protein sE-eGFP ) /MFI ( FL1 ) no protein sE-eGFP . To determine CD209 expression , 3×105 cells were incubated with anti-CD209-PerCPCy5 . 5 ( clone DCN46 , BD Biosciences ) , anti-CD14-APC , anti-CD1a-PE and anti-HLA-DR-PerCP mAb in PBS/2% FCS for 15 min and , after washing , fixed and analyzed by flow cytometry . Formaldehyde-fixed , paraffin sections were rehydrated and antigen was retrieved in citrate buffer pH 6 at 97°C for 45 min . Biotin was blocked using the avidin-biotin blocking kit ( Vector Inc . ) , and sections were saturated in 5% human serum at room temperature for 40 min . The following primary Abs were used: goat-anti IL-10 ( 1∶75 dilution , R&D Systems ) , mouse anti-CD209 ( 2 µg/ml , R&D Systems ) , mouse anti-CD1a ( Immunotech ) and mouse anti-CD14 ( 1∶40 dilution , Novocastra ) . The secondary Ab ( Jackson ) were: biotin-conjugated donkey anti-goat followed by streptavidin-Alexa 488 ( Molecular Probes-Invitrogen ) and F ( ab ) '2 rabbit anti-mouse followed by Cy3-conjugated donkey anti-rabbit . Sections were observed by confocal microscopy ( LSM510 Zeiss ) . Monocytes were isolated from 200 ml of adult human peripheral blood using negative-depletion beads ( Dynal-Invitrogen ) or by counterflow centrifugal elutriation . To obtain MDdMφ , 3×106 monocytes were cultured for 5 days in 5 ml of complete medium containing 10 ng/ml M-CSF ( R&D Systems ) , 20 ng/ml IL-10 ( Immunotools ) and 20 ng/ml GM-CSF ( Schering-Plough ) with refreshment of GM-CSF ( 10 ng/ml ) and IL-10 ( 10 ng/ml ) at day 3 . For MDDC , 3×106 monocytes were cultured for 5 days in 5 ml of complete medium containing 50 ng/ml GM-CSF and 10 ng/ml IL-4 ( Schering-Plough ) with readdition of cytokines at day 3 . Non-adherent cells were harvested . Expression of markers was measured by FACS using specific antibodies and their corresponding isotype controls . To assay for DV3 sE protein binding , cells were pre-incubated for 10 min in complete medium in the absence or presence of 5 mM EDTA before adding 3 µg DV3 sE-eGFP protein . After 30 min at 37°C , the cells were washed three times in complete medium and analyzed by flow cytometry . 5×105 MDdMφ and MDDC were exposed to DV serotype 1 ( strain FGA/NA d1d ) [11] , serotype 2 ( strain 16681 ) , or serotype 3 ( strain PaH 881 , isolated in 1988 in Thailand ) in RPMI medium supplemented with 0 . 2% bovine serum albumin for 2 h . Viral growth was determined at 40 h post-infection . Virus titration was performed as previously described [3] . Infectivity titers were expressed as focus forming unit ( FFU ) on mosquito AP61 cell line ( DV1 and DV3 ) or plaque forming unit ( PFU ) on mammalian BHK cell line ( DV2 ) . Different titering assays were performed to independently confirm our findings , despite the fact both methods may not be equivalent . The limit of titer determination was fixed at 103 , below which viral production was considered non-significant . For FACS analysis , infected cells were fixed and labeled for intracellular viral antigens with antiserum raised in mice that had received intracerebral DV injection [3] . IFN-α released from DV1-infected MDdMφ and MDDC was measured by ELISA ( R&D Systems ) . To observe live DV internalization by MDDC and MDdMφ , the cells were exposed to DV1 at an MOI of 100 at 4°C for 30 min or at 37°C for 1 h and fixed in 2 . 5% glutaraldehyde . Cells were postfixed in osmium tetroxide , dehydrated in ethanol containing 1% uranyl acetate , treated with propylene oxide and embedded in resin ( Durcupan ACM , Fluka ) . Ultrathin sections were stained with lead citrate and examined by transmission electron microscopy ( TEM ) ( Hitachi H600 ) . Images were acquired using a CCD camera ( Hamamatsu ) . To visualize DV3 sE-eGFP internalization and endosomal acidification , cells were incubated with 10 µM LysoSensor Blue DND-167 ( Molecular Probes-Invitrogen ) for 30 min at 37°C . Protein sE-eGFP was added at a concentration of 3 µg/ml , and cells were viewed after different incubation times using a confocal microscope ( LSM510 , Zeiss ) . The blue color emitted by the LysoSensor dye was digitally converted into red . For TEM , cells were fixed in 2% paraformaldehyde and 0 . 2% glutaraldehyde . Cells were embedded in 1% agarose , permeabilized with 0 . 2% saponin and saturated with 2% BSA before incubation with 5 µg/ml polyclonal rabbit anti-GFP antibody ( Rockland ) . The antibody was visualized by pre-embedding labeling using a goat anti-rabbit IgG conjugated to 0 . 8 nm gold particles , according to manufacturer's instructions ( Aurion ) . Cells were fixed in 1% glutaraldehyde , and gold particles were enhanced using a silver kit ( HQ silver , Nanoprobes ) . Cells were then treated and observed as above .
We wished to determine whether human dMφ are targets of DV infection . To this end , healthy human skin from patients undergoing plastic surgery was processed to obtain a dermal cell suspension . The cells were then cultured without additional cytokines for 48 h to allow re-expression of cell surface markers , such as CD1a and CD209 , lost during the collagenase treatment ( data not shown ) . Binding of DV3 E protein to dermal cells was assessed by flow cytometry after staining with CD14 and CD1a-specific antibodies . CD14 is expressed by dMφ and CD1a by dDC [7]–[9] . To detect E protein binding , the soluble form of DV3 E protein ( sE ) was fused to the reporter protein eGFP and purified from a Drosophila expression system . As shown in Figure 1A , CD1a+ dDC showed only a limited capacity to interact with DV3 sE protein , whereas CD14+ dMφ readily bound the protein . This is corroborated by the distinct expression of CD209 by dMφ ( Fig . 1A ) , whereas dDC expressed little , if any , CD209 ( data not shown ) . Increasing amounts of DV3 sE protein were added to the dermal cell suspension to test if dDC bound the protein at higher concentrations . Figure 1B shows that even at high concentrations , there was little binding of DV3 sE protein to dDC , whereas it bound to dMφ in a dose-dependent fashion . These findings identify dMφ as potential key cellular targets of DV . To address the question of whether dMφ are infected by DV and whether they are permissive for viral production , we established cell culture conditions to generate dermal-type Mφ from monocytes . We observed on human skin tissue sections that dMφ expressing CD14 or CD209 , but not the CD1a+ dDC , stained for IL-10 ( Fig . 2A ) . When purified human monocytes were cultured in M-CSF and increasing concentrations of IL-10 , the cells expressed CD14 and CD209 in an IL-10 dose-dependent manner ( Fig . 2B ) . Similar to DC [12] , the addition of GM-CSF increased CD209 levels ( Fig . 2B ) , so that a homogeneous CD14+CD209+ cell population could be obtained with CD209 expression nearly identical to that of DC derived from monocytes in the presence of GM-CSF and IL-4 ( Figure S1A ) . Western blotting of cell lysates confirmed the presence of CD209 as a major band of 49 kDa in both cell-types [13] ( Figure S1B ) . The Mφ expressed coagulation factor XIIIa and CD163 , two other cell surface markers of dMφ [14] ( Fig . 2C ) . The Mφ and the DC were both able to bind eGFP-tagged DV3 sE protein , which was inhibited by EDTA ( Fig . 2C ) . This distinguishes the monocyte-derived DC from dDC . Upon activation by lipopolysaccharide ( LPS ) , the Mφ rapidly released IL-10 , whereas DC or monocytes produced little of this cytokine ( Figure S1C ) . Monocyte-derived dMφ ( MDdMφ ) and monocyte-derived DC ( MDDC ) were analyzed for DV infection using low-passage DV1 and DV3 strains grown in mosquito cells [3] as well as the prototype DV2 strain 16681 [15] . The cells were exposed to DV1 at a multiplicity of infection ( MOI ) of 1 for 2 h , washed , and then cultured for 40 h . As shown in Figure 3A , intracellular viral antigen was clearly detected in MDDC by flow cytometry , whereas no specific immuno-labeling was observed in MDdMφ . An analysis of DV replication in these cells infected at an MOI of 1 ( DV1 and DV3 ) or 2 ( DV2 ) showed that MDDC were highly permissive to productive infection ( ∼105 FFU/ml or PFU/ml ) ( Fig . 3B ) ; in contrast , progeny virus production was undetectable in DV-infected MDdMφ ( <103 FFU/ml or PFU/ml ) . Consistent with this finding , no IFN-α was produced by DV-infected MDdMφ , even at an MOI of 10 , whereas MDDC readily released IFN-α when infected with DV at an MOI of 1 or 10 [16] ( Fig . 3C ) . To verify that MDdMφ acquired the virus , both myeloid cell-types were exposed to high DV input ( MOI of 100 ) and electron microscopy analysis was performed after 30 min at 4°C and after 1 h at 37°C ( Fig . 3D ) . Cell surface-bound ( at 4°C ) and endosomal vesicle-associated virus particles ( at 37°C ) were clearly detected in both cell-types . Thus , internalization of DV can occur in MDdMφ but does not result in productive infection . In an effort to define the molecular basis of the inability of DV to grow in MDdMφ . we asked whether internalized DV was sequestered in a manner that hampers productive infection , using DV3 sE-eGFP fusion protein . To monitor DV3 sE protein internalization in MDdMφ and MDDC , the cells were incubated with pH-sensitive LysoSensor dye and analyzed by confocal microscopy ( Fig . 4 ) . This dye accumulates in acidic organelles , where its fluorescence emission is highest . After 5 min at 37°C , DV3 sE protein was observed in vesicle-like structures in both cell-types . By 30 min and 60 min , DV3 sE protein dispersed to acidified perinuclear lysosomes in MDDC . In marked contrast , when MDdMφ were examined at these time-points , a large fraction of internalized DV3 sE protein was excluded from the acidic compartment and remained in non-acidic , large endosomes . Electron microscopy analysis using a colloidal gold-conjugated antibody to GFP demonstrated that DV3 sE protein accumulated in large phagosomes in MDdMφ , located close to the plasma membrane ( Fig . 5 ) . On the other hand , at 30 min , in MDDC , DV3 sE protein was mostly found in small perinuclear vesicles in the environment of the endoplasmic reticulum . Taken together , these data suggest that the inability of DV to productively infect MDdMφ is due to accumulation of virus particles in immature endosomal vesicles whose pH does not allow efficient viral-cell membrane fusion and subsequent virus uncoating .
In the present study , we demonstrated for the first time the interaction of dMφ with DV3 sE glycoprotein , which correlates with the expression of the DV attachment receptor CD209 . Dermal DC displayed only a limited capacity to interact with DV3 sE protein and expressed little CD209 . In accordance with these findings , in situ immuno-labeling of human skin section revealed CD209 expression by dMφ but little on DC [7]–[9] . Both cell types carry the MR [7] , which also recognizes DV E protein [5] . Due to the nature of our binding assay , the dermal cells with the highest affinity for DV3 sE protein would acquire the most DV3 sE protein , suggesting that dDC may capture the recombinant envelope protein when physically isolated from dMφ . In the skin , the abundance , the location and the co-expression of CD209 , L-SIGN and MR are likely to determine the nature of the DV-capturing immune cell . Based on the observations that dMφ stained for intracellular IL-10 in situ and that IL-10 is produced by dMφ ex vivo [17] , [18] , we tested the effect of IL-10 on the formation of dMφ from monocytes . By combining IL-10 , M-CSF and GM-CSF , a homogenous cell population was obtained which carried CD209 and other markers characteristic of dMφ , rapidly produced IL-10 in response to LPS or other toll-like receptor ligands ( data not shown ) , and bound DV3 sE protein . Like MDDC , the MDdMφ were capable of internalizing live DV but , distinct from MDDC , they displayed an inherent resistance to viral growth . In contrast to DV3 sE protein found in acidified compartments in MDDC , we observed that DV3 sE protein accumulated in non-acidified phagosomes in MDdMφ . The DC vesicles containing DV3 sE protein or live virus were bell-shaped or tubular , whereas they were round , larger and close to the plasma membrane in the Mφ . To our knowledge , this identifies MDdMφ as the first innate immune cell capable of protecting the human host from DV infection and virus propagation . From this data , we propose that dMφ can act to trap infecting virions in a fusion-incompetent endosomal environment and thus to prevent DV spread to dDC at the anatomical site of the mosquito bite . We cannot formally exclude the possibility that downstream delays in the viral life cycle contribute to the inability of DV to replicate in MDdMφ , but the finding that West Nile virus productively infects these cells ( data not shown ) indicates that they are not generally refractory to flavivirus growth . IL-10 , required for CD209 expression and blockage of endosome acidification , is likely to be produced by the dMφ themselves , constitutively , or in response to stimuli such as UV-light [17] . In this context , a key question is whether mosquito salivary proteins , co-injected with the infectious virus , would also trigger IL-10 production by dMφ or , on the contrary , provoke an inflammatory response . Inflammatory cytokines of the Th2 T-helper cell type , IL-4 and IL-13 , may be responsible for the formation of CD209+MR+ DC , which are permissive for DV infection and viral progeny production [3]–[6] . Alternatively , the presence of anti-DV non-neutralizing antibodies raised against a heterotypic DV serotype may render dDC susceptible to DV infection at the site of the mosquito bite . The abundance and strategic position of the Mφ in the dermis is consistent with their function as first defense barrier against pathogens by isolating and eliminating them and thus avoiding unnecessary immune activation . However , other pathogens that recognize C-type lectins , such as mycobacteria , may exploit these cells to escape immune attack . Accumulating CD209+ Mφ in leprosy skin lesions have been associated with mycobacterial persistence [19] . Important questions to address in future are whether DV is eliminated in MDdMφ , whether infected MDdMφ gradually release DV , as shown for the foot-and-mouth disease virus and pulmonary Mφ [20] , and whether rapid DV growth can occur when the Mα convert to DC . Improved knowledge of the molecular mechanisms for suppressing pathogen growth in MDdMφ will provide new insight into the crucial role of dMφ in protective immunity to infectious agents at the skin level . | Mosquito-transmitted pathogens are a major challenge to humans due to ever-increasing distribution of the vector worldwide . Dengue virus causes morbidity and mortality , and no anti-viral treatment or vaccine are currently available . The virus is injected into the skin when an infected mosquito probes for blood . Among the skin immunocytes , dendritic cells and macrophages are equipped with pathogen-sensing receptors . Our work has shown that dermal macrophages bind the dengue virus envelope protein . We demonstrate that monocyte-derived dermal macrophages are resistant to infection and present evidence that this is due to sequestration of the virus into fusion-incompetent intracellular vesicles . This identifies skin macrophages as the first innate immune cell potentially capable of protecting the human host from infection by dengue virus shortly after a mosquito bite . These findings have important implications for better understanding the early infection events of dengue virus and of other skin-penetrating pathogens . | [
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] | 2008 | Dermal-Type Macrophages Expressing CD209/DC-SIGN Show Inherent Resistance to Dengue Virus Growth |
Tick-borne encephalitis virus ( TBEV ) , a member of the genus Flavivirus ( Flaviviridae ) , is a causative agent of a severe neuroinfection . Recently , several flaviviruses have been shown to interact with host protein synthesis . In order to determine whether TBEV interacts with this host process in its natural target cells , we analysed de novo protein synthesis in a human cell line derived from cerebellar medulloblastoma ( DAOY HTB-186 ) . We observed a significant decrease in the rate of host protein synthesis , including the housekeeping genes HPRT1 and GAPDH and the known interferon-stimulated gene viperin . In addition , TBEV infection resulted in a specific decrease of RNA polymerase I ( POLR1 ) transcripts , 18S and 28S rRNAs and their precursor , 45-47S pre-rRNA , but had no effect on the POLR3 transcribed 5S rRNA levels . To our knowledge , this is the first report of flavivirus-induced decrease of specifically POLR1 rRNA transcripts accompanied by host translational shut-off .
The Flaviviridae family contains arthropod-borne viruses including medically important pathogens with worldwide distribution and impact , such as dengue virus ( DENV ) , yellow fever virus ( YFV ) , West Nile virus ( WNV ) , Japanese encephalitis virus ( JEV ) , Zika virus ( ZIKV ) , and tick-borne encephalitis virus ( TBEV ) [1] . TBEV causes a severe neuroinfection known as tick-borne encephalitis , which affects thousands of people across Eurasia annually [2 , 3] . In recent years , an increase in TBEV infection rates in affected countries and in its geographical distribution has been observed , involving previously unaffected areas such as Switzerland and northern Germany [4–6] . Although the disease is not always fatal ( mortality rate of 1–2% ) , a high percentage of patients ( 35–58% ) suffer from permanent sequelae , such as cognitive or neuropsychiatric afflictions , balance disorders , headaches , dysphasia , hearing defects , and spinal paralysis after overcoming the main symptoms [2 , 7] . Specific antiviral therapy for tick-borne encephalitis does not exist . Neurons are the primary target for TBEV infection in mice and humans , and according to post mortem studies of TBEV-infected patients , the cerebellum is one of the main foci affected [8–10] . Understanding the interaction between TBEV and human neural cells is essential as it could lead to possible new treatment targets and a better understanding of why TBEV infection can result in severe neurological symptoms . Like all flaviviruses , TBEV is an enveloped virus with a single-stranded RNA ( ssRNA ) genome of positive polarity ( approx . 11 kb ) with a 7-methylguanosine cap at the 5´end . The coding segment is flanked on both ends by untranslated regions ( UTR ) . Viral proteins are encoded in a single open reading frame that is translated in one poly-protein which is then proteolytically processed into three structural ( C , prM , E ) and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , NS5 ) [11–13] . While the structural proteins are the main building units of the viral particle , the non-structural proteins are crucial in the TBEV life cycle . They are essential components of viral replication within the host endoplasmic reticulum or Golgi apparatus-derived membrane compartments and the virion assembly processes and are involved in immune response evasion/counteractions [14–16] . Virus replication is reliant on the host protein synthesis apparatus and manipulates it in favour of viral requirements . There are various strategies viruses use to accomplish this goal and generally aim at three levels: host translational shut-off , processing of host mRNA , and host transcriptional shut-off [17 , 18] . Translation of eukaryotic and viral proteins is often controlled at the rate-limiting step of initiation and viruses such as Bunyamwera virus , influenza A virus or poliovirus were shown to target initiation factors [19–23] . More specifically for flaviviruses , a recent study [24] documented repression of the host translation initiation step during DENV infection and general translational repression was also recorded for WNV and ZIKV . While inducing host translational shut-off , viral proteins are still synthesised thanks to alternative translation initiation strategies , such as cap-independent translation [20 , 25–27] . Transcription in eukaryotic organisms is carried out by three RNA polymerases: RNA polymerase I , II , and III . Each of the RNA polymerase complexes is responsible for the transcription of different genes . RNA polymerase I ( POLR1 ) yields a single transcription unit 45-47S pre-rRNA , which undergoes a complex maturation process that generates 5 . 8S , 18S , and 28S rRNA [28 , 29] . RNA polymerase III ( POLR3 ) produces 5S rRNA , tRNAs , and specific small RNAs [29] . RNA polymerase II ( POLR2 ) transcribes protein-coding genes and certain small RNAs [30] . Out of all the transcripts synthesised in the eukaryotic cell , ribosomal RNA is the most abundant and a key component of ribosomes . Virus-induced interference with transcription and subsequent processing of host rRNA has been described for influenza A virus [31] , herpes simplex virus type I [32] , human papillomavirus type 8 [33] , human cytomegalovirus [34] , and human immunodeficiency virus ( HIV ) [35] . However , this was not described for flaviviruses . Given the indications for flaviviruses affecting host translation [24] , we aimed at exploring this topic further in TBEV infection of naturally permissive host cells of neural origin , that represent a key cell type responsible for tick-borne encephalitis manifestation . We found that TBEV triggered host translational shut-off that involved lowered expression of GAPDH and HPRT1 housekeeping genes as well as the interferon-induced protein viperin . TBEV further specifically impaired the production of POLR1-transcribed rRNAs . Therefore , we postulate that TBEV specifically targets POLR1-mediated transcription of rRNA and rate of host translation thus promoting virus replication .
The human medulloblastoma ( DAOY HTB-186; ATCC ) , human lung adenocarcinoma ( A549; a gift from R . Randall , University of St . Andrews , UK ) , and Vero ( green monkey kidney; Biology Centre , CAS , CZ ) cell lines were grown in low glucose DMEM medium supplemented with 10% foetal bovine serum ( FBS ) , 1% antibiotics-antimycotics ( amphotericin B 0 . 25 μg/ml , penicillin G 100 units/ml , streptomycin 100 μg/ml ) , and 1% L-alanyl-L-glutamine . DAOY HTB-186 cell line is derived from desmoplastic cerebellar medulloblastoma of a 4-year-old Caucasian male [36] . A549s are derived from a lung cancerous tissue ( alveolar basal epithelial cells ) of a 58-year-old Caucasian male [37] . Vero cells are derived from kidney epithelial cells from African green monkey ( Cercopithecus aethiops ) . PS cells ( porcine kidney stable ) were grown in L15 medium with 3% new-born calf serum ( NCS ) , 1% antibiotics-antimycotics , and 1% L-alanyl-L-glutamine [38] . The human osteosarcoma cell line MG-63 ( Sigma-Aldrich ) was grown in RPMI 1640 medium supplemented with 10% FBS , 1% antibiotics-antimycotics , 1% L-alanyl-L-glutamine , and 50 nM β-mercaptoethanol . These were explanted from a 14-year-old Caucasian male [39] . For metabolic labelling experiments , all cell lines were grown in RPMI 1640 medium supplemented with 10% FBS , 1% antibiotics-antimycotics , 1% L-alanyl-L-glutamine , and 50 nM β-mercaptoethanol . All cell lines were grown at 37°C and 5% CO2; with the exception of PS cells ( 37°C without additional CO2 ) . PolyJet In Vitro Transfection Reagent ( SignaGen; #SL100688 ) was used for transfection . The procedure was carried out according to the manufacturer’s protocol . For GFP and Renilla luciferase expression , the mammalian expression vectors phMGFP ( Promega ) and pRL-CMV ( Promega ) were used , respectively . The wt viperin mammalian expression vector was a kind gift from Lisa F . P . Ng ( Singapore Immunology Network , Agency for Science , Technology and Research ( A* STAR ) , Singapore ) , in which the viperin gene with C-terminal c-myc tag is transcribed under the control of the CMV promoter [40] . Two representatives of the West-European TBEV subtype with different degrees of virulence were used–medium ( Neudoerfl ) and severe ( Hypr ) . Both strains differ in their coding sequences by only 12 nonconservative amino acid substitutions [41] , and in the length and structure of the 3´UTR [42] . When mice were infected peripherally , the Hypr strain exhibited pronounced neuroinvasiveness and caused shorter survival than strain Neudoerfl [41] . The low passage TBEV strain , Neudoerfl ( fourth passage in suckling mice brains; GenBank accession no . TEU27495 ) , was provided by Prof . F . X . Heinz ( Medical University of Vienna , Austria ) [43] . The low passage TBEV strain , Hypr ( fourth passage in suckling mice brains; GenBank accession no . TEU39292 ) , is available at the Institute of Parasitology , Biology Centre of CAS , České Budějovice , Czech Republic [44] . Viruses were handled under biosafety level 3 conditions . TBEV was added to the cells one day post seeding . Cells were then incubated for 2 hours , washed with PBS , and finally fresh pre-warmed medium was added . Brain suspension from uninfected suckling mice was used as a negative control . Viral titres were determined by plaque assay as described [45] , with minor modifications . Briefly , PS cell monolayers ( 9x104 cells per well ) were grown in 24-well plates and incubated with 10x serial dilutions of infectious samples for 4 hours at 37°C . The samples were then covered by 1:1 ( v/v ) overlay mixture ( carboxymethyl cellulose and 2x L15 medium including 6% NCS , 2% antibiotics-antimycotics , and 2% L-glutamine ) . After five days , medium with overlay was removed , cells washed with physiological solution , subsequently fixed and stained ( 0 . 1% naphthalene black in 6% acetic acid solution ) for 45 minutes . Virus-produced plaques were counted , and titres are stated as PFU/ml . The following primary antibodies were used: anti-TBEV C polyclonal antibody ( produced in-house ) , anti-TBEV NS3 polyclonal antibody ( a kind gift from Dr . M . Bloom , NIAID , USA ) , anti-HPRT1 Polyclonal Antibody ( Thermo Fisher Scientific; #PA5-22281 ) , anti-GAPDH Antibody [EPR16891] ( Abcam; #ab181602 ) , Monoclonal Antibody to Mouse Viperin ( Hycult Biotech; #HM1016 ) , anti-NPM1 Monoclonal Antibody FC-61991 ( Thermo Fisher Scientific; #MA1-1560 ) , and anti-POLR1A Antibody ( Abcam; #ab222065 ) . The following secondary/tertiary antibodies were used: HRP Goat Anti-Guinea Pig ( Novex; #A18769 ) , HRP Rabbit Anti-Chicken IgY ( H+L ) Secondary Antibody ( Thermo Fisher Scientific; #A16130 ) , HRP Horse Anti-Mouse IgG Antibody ( VectorLabs; #PI-2000 ) , HRP Goat Anti-Rabbit IgG Antibody ( VectorLabs; #PI-1000 ) , Biotinylated Anti-Streptavidin Antibody ( VectorLabs; #BA-0500 ) , AP-conjugated Streptavidin ( VectorLabs; #SA-5100 ) , Streptavidin-DyLight 549 ( VectorLabs; Cat#SA-5549 ) , Goat Anti-Rabbit IgG-DyLight 594 ( Abcam; #ab96897 ) , Goat Anti-Guinea Pig DyLight 594 ( Abcam; #ab150188 ) , and Goat Anti-Chicken IgY H&L-DyLight 488 ( Abcam; #ab96947 ) . L-azidohomoalanine ( Click Chemistry Tools; #1066–25 ) and 5-ethynyl-uridine ( Click Chemistry Tools; #1261–25 ) were used for metabolic labelling of nascent proteins or RNA , respectively . Biotin-PEG4-Alkyne ( Click Chemistry Tools; #TA105-25 ) and Biotin Picolyl Azide ( Click Chemistry Tools; #1167–25 ) were used for subsequent detection of incorporated L-azidohomoalanine or 5-ethynyl-uridine , respectively . Cycloheximide was purchased from Sigma-Aldrich ( #01810-1G ) . DAOY cells were seeded one day prior to infection in the 12-well plate at a density of 2 . 5×105 cells/well . At the indicated time intervals post-TBEV infection , cells were washed with PBS , trypsinized , and fixed by 4% paraformaldehyde in PBS ( Roth ) . After permeabilization ( 0 . 1% Triton X-100 ) , cells were stained using guinea pig anti-TBEV C antibodies ( 1:1500 dilution ) and anti-guinea pig DyLight 594 ( 1:500 dilution ) secondary antibodies . Flow cytometry was performed on a FACS Canto II cytometer and data analysed using FACS DIVA software v . 5 . 0 ( BD Biosciences ) . Total cellular RNA was isolated using Trizol-based RNA Blue reagent ( Top-Bio; #R013 ) according to the manufacturer’s instructions . RNA pellets were dissolved in DEPC-treated water and directly used for either real-time PCR or analysis on an RNA denaturing gel . The quantity and integrity of rRNA in total RNA samples were analysed on a 2100 Bioanalyzer using Agilent RNA 6000 Nano kit ( Agilent Technologies; #5067–1511 ) . The concentration of each sample was determined spectrophotometrically prior the Bioanalyzer measurement and samples were diluted according to the cell number ratio ( resulting concentrations were between 10–20 ng/μl ) . 1 μl of the diluted RNA samples was loaded on the Bioanalyzer chip and the electrophoretic assay was performed according to the manufacturer’s instructions . All samples were analysed in technical triplicates . 1 . 2% agarose MOPS-buffered denaturing gel ( with 6 . 7% formaldehyde ) was used for fractionation of isolated total RNA . RNA was visualised by addition of the GelRed dye ( Biotium ) into the gel . The signal was subsequently quantified using Fiji software . We observed that the viability of TBEV Hypr-infected cells was negatively affected at 36 and 48 hours p . i . ( Fig 1D ) . Therefore , in order to diminish the effect of this phenomenon on our data , we decided to standardise in our experiments to cell counts . Normalisation to cell numbers was performed for real-time PCR , western blotting , northern blotting , and metabolic labelling analyses . For this , we established a viability-based method using alamarBlue reagent ( Thermo Fisher Scientific; #DAL1025 ) . Our data demonstrate that viability measurement is directly proportional to the cell number , and therefore this method is fully suitable for normalisation to the cell number ( S1 Fig ) . The procedure was performed according to the manufacturer’s instructions . Briefly , cells were washed with PBS and fresh pre-warmed growth medium with diluted alamarBlue reagent was added ( 1:10 dilution ratio; v/v ) . Cells were incubated for 2–2 . 5 hours and fluorescence of the reduced product was measured on a BioTek plate reader ( λex = 550 nm; λem = 590 nm ) . Growth medium with alamarBlue without cells was used as a blank . All samples were analysed in technical triplicates . Average fluorescence values for TBEV-treated sample were normalized to the respective mock control cells . The viability factor ( f ) was subsequently used as a normalisation factor for the calculation of RNA/protein input based on the pre-set mock control input . For real-time qPCR analyses , the KAPA SYBR FAST Universal One-Step qRT-PCR Kit was used according to the manufacturer’s protocol . Data obtained were processed via relative quantification using the delta ct ( Δ-ct ) method; the amount of RNA was adjusted to the cell number instead of the ct values of the housekeeping reference gene . All samples were treated with dsDNase and subsequently 5× diluted in RNAse-free water before the real-time PCR analysis . All samples were analysed in technical triplicates . List of primers used can be found in S1 Table . Cells were washed with PBS and RIPA buffer ( 25 mM Tris-HCl pH 7 . 6 , 150 mM NaCl , 1% NP-40 , 0 . 1% SDS , 1% sodium deoxycholate ) with protease inhibitors ( Thermo Fisher Scientific; #78430 ) was added . Cell lysis was performed for 15 minutes on ice while gently shaking . Sonicated and cleared protein lysates in RIPA buffer were separated on 12% denaturing polyacrylamide gels and blotted onto PVDF membranes . The quantity of proteins was normalised to the cell number . Membranes were blocked ( 5% skimmed milk in PBS-T ) and incubated with primary , secondary , and alternatively also tertiary antibodies; between each staining step , membranes were washed three times in PBS-T . Primary antibodies used were guinea pig anti-C ( produced in-house; 1:1500 ) , chicken anti-NS3 ( M . Bloom laboratory; 1:5000 ) , rabbit anti-GAPDH ( Abcam; 1:1000 ) , anti-HPRT1 ( Thermo Fisher Scientific; 1:500 ) , anti-viperin ( Hycult Biotech; 1:500 ) . Secondary/tertiary antibodies used were goat anti-rabbit HRP ( VectorLabs; 1:1000 ) , rabbit anti-chicken HRP ( Thermo Fisher Scientific; 1:1000 ) , and horse anti-mouse HRP ( VectorLabs; 1:1000 ) . Chemiluminescent signal was developed using either Novex CDP-Star kit for alkaline phosphatase ( Thermo Fisher Scientific ) or WesternBright Quantum kit for horseradish peroxidase ( Advansta; #K-12042-D20 ) . The signal was subsequently quantified using Fiji software [46] . For stripping of antibodies , membranes were incubated with stripping solution ( 62 . 5 mM Tris HCl pH 6 . 8 , 2% SDS , 0 . 8% β-mercaptoethanol ) for 45 minutes at 50°C . Subsequently , membranes were extensively washed six times with PBS . Following this , membranes were blocked , and immunostaining was again performed as described above . For analyses of Renilla luciferase activity in CHX-treated cells , Renilla Luciferase Assay Kit from Promega ( #E2810 ) was used according to the manufacturer’s instructions . Briefly , 5×104 DAOY cells per well were seeded on a 96-well plate . Cells were transfected with 100 ng of pRL-CMV vector per well using PolyJet transfection reagent and incubated with cycloheximide ( 50–300 μg/ml ) for 2 , 4 , 6 , 14 , and 24 hours . At 24 hours post-transfection , the viability of cells was measured using alamarBlue . Subsequently , cells were lysed and Renilla luciferase activity was determined . Cells were seeded in 6-well plates at a density of 1×106 ( Vero , A549 ) or 5×105 ( DAOY , MG-63 ) cells per well . At indicated time intervals p . i . , cells were washed with PBS and starved for 1 hour by addition of complete methionine-free RPMI medium ( methionine-free RPMI medium containing 10% FBS , 1% L-alanyl-L-glutamine , 1% antibiotics/antimycotics , and 0 . 27 mM L-cystine ) . Subsequently , fresh complete methionine-free RPMI medium was added with 50 μM L-azidohomoalanine ( AHA ) and 1× AlamarBlue reagent . Metabolic labelling with AHA was performed for 2 hours . Afterwards , cell viability was measured as described earlier . Cells were then washed with PBS and lysed on ice for 15 minutes in 200 μl RIPA buffer with protease inhibitors ( Halt Protease Inhibitor Single-Use Cocktail; Thermo Fisher Scientific ) . Lysates were separated on 12% polyacrylamide gels and transferred by electroblotting onto the PVDF membrane . The quantity of proteins loaded onto the gel was normalised to the cell numbers . Subsequently , the modified detection method Click-on-membrane was performed according to Kočová et al . ( in preparation ) . Briefly , membranes were washed in 0 . 1 M potassium phosphate buffer pH 7 . 0 and the Click reaction was performed as follows: membranes were incubated in Click reaction buffer ( 0 . 1 M potassium phosphate buffer pH 7 . 0 with 0 . 25 mM sodium ascorbate , 0 . 5 mM THPTA , 0 . 1 mM CuSO4 , and 10 μM biotin-alkyne ) for 1 hour in the dark at room temperature . Membranes were washed three times with PBS , blocked ( 5% skimmed milk in PBS-T ) and incubated with primary ( AP-streptavidin; VectorLabs; 1:500 ) , secondary ( biotinylated anti-streptavidin; VectorLabs; 1:1000 ) and tertiary antibodies ( AP-streptavidin; VectorLabs; 1:2000 ) . Between each staining step , membranes were washed three times in PBS-T . Chemiluminescence signal was developed using Novex CDP-Star kit ( Invitrogen; #WP20002 ) . Signal was subsequently quantified using Fiji software [46] . DAOY cells were seeded in 6-well plates at a density of 5×105 cells per well . At the indicated time intervals p . i . , 5-ethynyl uridine ( 5-EU ) was added to the cells ( final concentration of 5-EU was 1 mM ) as well as alamarBlue reagent . Metabolic labelling with 5-EU was performed for 2 hours . Cell viability was measured as described earlier . Cells were then washed with PBS and lysed using RNA Blue reagent . Total RNA was isolated according to the manufacturer’s instructions . Next , RNA was separated in MOPS-buffered denaturing gel , as described above . The quantity of RNA was normalised to the cell number . Capillary blotting of RNA to the PVDF membrane ( GE Healthcare ) using 20× SSC buffering system was performed afterwards . Subsequently , the modified detection method Click-on-membrane was performed according to the method described by Kočová et . al . ( in preparation ) . Briefly , the UV-fixed membrane was washed in 0 . 1 M potassium phosphate buffer pH 7 . 0 and the Click reaction on membrane was performed as follows: membranes were incubated in Click reaction buffer ( 0 . 1 M potassium phosphate buffer pH 7 . 0 with 0 . 25 mM sodium ascorbate , 0 . 5 mM THPTA , 0 . 1 mM CuSO4 , and 10 μM picolyl biotin azide ) for 1 hour in the dark at room temperature . Blocking and triple labelling using biotin-streptavidin system was performed as described above . The chemiluminescence signal was developed using Novex CDP-Star kit ( Invitrogen; #WP20002 ) , and signal was subsequently quantified using Fiji software [46] . DAOY cells were seeded in chamber slides ( 0 , 3 cm2/well; 5×103 cells/well ) and at the indicated time intervals p . i . processed as previously described [47] . Rabbit anti-POLR1A ( Abcam; 1:200 ) and chicken anti-NS3 ( a kind gift from Dr . M . Bloom , NIAID , NIH; 1:5000 ) antibodies were used . As the secondary antibodies , anti-rabbit DyLight 594 ( Abcam; 1:500 ) and anti-chicken DyLight 488 ( Abcam; 1:500 ) , were used . In the case of metabolic labelling of nascent RNA , the Click reaction was performed in situ before the blocking step . 10 μM Picolyl biotin azide was used for the detection of incorporated 5-EU . For subsequent fluorescent labelling , streptavidin conjugated with DyLight 549 was used ( VectorLabs; 1:500 ) . Slides were eventually mounted in Vectashield mounting medium ( VectorLabs ) . The Olympus Fluoview FV10i confocal microscope was used for imaging and subsequent export of images was done in FV10-ASW software ( v . 1 . 7 ) . All statistical analyses were performed in MS Excel using one-sample two-tailed Studentʼs t-test . Only in case of qPCR analysis of over-expressed viperin and GFP , an unpaired two-tailed Student’s t-test was used . In this case , datasets were first tested for the equality of variances by F-test . If the experiment was performed in technical replicates , the statistics was performed using the means of the independent biological replicates .
Recent studies have shown that DENV decreases the rate of de novo protein synthesis in host cells [24 , 48] . In order to establish whether TBEV also affects translation , de novo protein synthesis kinetics was measured in TBEV-infected cells using Click chemistry [49] . For this purpose , we utilized a suitable in vitro experimental system of the cerebellum-derived human medulloblastoma cell line DAOY HTB-186 to broaden previous findings [47] . Two closely related members of the European subtype of TBEV with different virulence were used for comparative purposes: a medium virulent prototype strain , Neudoerfl , and a highly virulent strain , Hypr [41] . Initially , we characterized the course of infection for both TBEV strains . DAOY cells were infected at an MOI of 5 with either strain and at 12 , 18 , 24 , 36 , 48 hours p . i . , replication kinetics , infection rate , viral protein ( C , NS3 ) production and viability of infected cells were determined . Both strains successfully replicated in DAOY cells , with the Hypr strain reaching at least one order of magnitude higher titres during the course of infection until 48 hours p . i . , when both strains eventually produced equal titres ( Fig 1A ) . The infection rate was also considerably higher for the Hypr strain , culminating at 36 hours p . i . ( 87 . 5% of infected cells ) , whereas the Neudoerfl strain infected only 43 . 6% of cells ( Fig 1A ) . Relative quantification of genomic RNA at 24 and 48 hours p . i . revealed that Hypr replicated with higher efficiency than Neudoerfl ( Fig 1B ) . TBEV C and TBEV NS3 protein detection corresponded to replication kinetics and for both strains proteins could be detected earliest at 18 hours p . i . , increasing thereafter ( Fig 1C ) . While TBEV Neudoerfl affected the viability of the infected cells only mildly ( maximal decrease by 16 . 6% at 36 hours p . i . ) , TBEV Hypr lowered the viability of the infected cells by 23 . 8% and 62 . 5% in comparison to mock-infected control at 36 and 48 hours p . i . , respectively ( Fig 1D ) . Therefore , in order to compensate the potential bias originating from cell death , we standardised our experiments to viability which is directly proportional to the number of living cells ( S1 Fig ) . In the following experiments we pursued interaction of TBEV with DAOY cells during the period of productive infection for both TBEV strains , ranging from 24 to 48 hours p . i . After this detailed characterization of our in vitro model , de novo protein synthesis and quantification was performed . DAOY cells were infected with either TBEV Hypr or Neudoerfl and metabolic labelling was carried out for 2 hours at 24 , 36 , and 48 hours p . i . using the methionine analogue L-azidohomoalanine ( AHA ) . At 24 hours p . i . , translation levels were comparable in control and infected cells , but infection resulted in a significant decrease of AHA-labelled proteins at 36 and 48 hours p . i . in TBEV Hypr-infected cells and at 48 hours p . i . in TBEV Neudoerfl-infected cells ( Fig 2A and 2B; S2A Fig ) . Interestingly , the viral NS3 protein levels increased over the course of the infection with both strains ( Fig 2A , lower panel ) . Furthermore , TBEV-induced host translational shut-off was also documented for cell lines of non-neural origin ( A549 cells , Vero cells , and MG-63 cells ) at 48 hours p . i . , for both TBEV strains ( Fig 2C; S2B Fig ) . Interestingly , despite the observed host translational shut-off both TBEV strains were able to replicate ( Fig 1B ) successfully and reached high titres ( Fig 1A ) in DAOY cells throughout the infection . Since these experiments revealed a significant decrease in host protein synthesis upon TBEV infection on a global level , we evaluated the specificity of this for particular host proteins . First , the effect of TBEV infection on common housekeeping genes GAPDH and HPRT1 was determined by analysing their mRNA and protein levels . Relative quantification of GAPDH and HPRT1 mRNAs revealed a strong inhibition of expression for both genes and TBEV strains at 48 hours p . i . ( Fig 3A and 3B; upper panel ) . Similar results were observed for their protein levels , although the more virulent strain Hypr elicited a stronger reduction ( Fig 3A and 3B; lower panel ) . As the subversion of host translation process can be used as an immune evasion strategy by viruses [17] , we investigated the effect of translational shut-off on the interferon-inducible gene viperin . Viperin has been described so far as an antiviral protein that interferes with TBEV on multiple levels [50] . A time course of viperin mRNA production in response to TBEV infection in DAOY cells was determined . Induction of viperin mRNA expression was detected at 24 hours p . i . and increasing throughout next 24 hours ( Fig 3C; upper panel ) . Despite significantly increased viperin mRNA levels , none or very small amounts of viperin protein were detected in cell lysates from TBEV-infected DAOY cells by western blot analysis ( Fig 3C; lower panel ) . As a positive control , DAOY cells treated with INF-β ( 12 hours; 50 ng/ml ) as well as DAOY cells transfected with a human viperin expression vector [40] were used . To assess whether the effect of TBEV on endogenous viperin production can be overcome by artificial over-expression , DAOY cells were first infected ( TBEV Neudoerfl and Hypr; MOI 5 ) and subsequently transfected with a wt-viperin expression construct at 12 hours p . i . Viperin mRNA , as well as protein levels , were analysed at 12 hours post-transfection ( S3A Fig ) . As a control , GFP expression construct was used . S3B Fig shows that viperin protein was produced; however , the protein levels were significantly reduced in TBEV-infected cells compared to control cells . Hypr strain infection also resulted in a statistically significant decrease in mRNA levels of viperin . As expected , GFP production in TBEV infected cells was negatively affected in case of both TBEV strains ( S3C Fig ) . Again , Hypr strain infection also caused a significant decrease in GFP mRNA . Consequently , TBEV induces a general translational shut-off , which can negatively affect even the production of overexpressed transcripts . Nevertheless , viral titres were increasing throughout the infection ( Fig 1 ) . Previous data revealed a significant decrease in RNA encoding genes including 5 . 8S rRNA and 7SL RNA following TBEV infection [47] . Here , we verified the link between the TBEV-induced translational shut-off and production of host rRNAs . We quantified the levels of 18S and 28S rRNAs in total cellular RNA from TBEV-infected DAOY cells at 24 and 48 hours p . i . We found that infection by both TBEV strains significantly decreased the 18S and 28S rRNA ( S4 Fig ) . 18S rRNA levels decreased to 50 ± 6% or 33 ± 1% for TBEV Neudoerfl- or Hypr-infected cells compared to controls , respectively ( Fig 4A ) . For 28S rRNA , its transcription levels fell to 49 ± 5% or 28 ± 2% for TBEV Neudoerfl- or Hypr-infected cells , respectively ( Fig 4B ) . Both 18S and 28S rRNAs are transcripts of POLR1 . Interestingly , the POLR3 transcript 5S rRNA levels remained unaffected by TBEV infection ( Fig 4C ) . These data imply that the effect of TBEV infection on host cells also involves the transcription of specific ribosomal RNA genes . In order to elucidate at which step TBEV interferes with rRNA production , we first analysed the integrity of mature rRNA molecules . No degradation products were observed following infection with either TBEV strains at 24 or 48 hours p . i . in DAOY cells ( Fig 5A and 5B ) . Next , we investigated the rRNA expression and processing via quantification of de novo synthesised RNA in TBEV-infected DAOY cells . We labelled nascent RNA in TBEV-infected DAOY cells at 24 , 36 and 48 hours p . i . with 5-ethynyl uridine ( EU ) . Incorporated EU was visualised using Click chemistry and the biotin-streptavidin detection system . The presence of TBEV Hypr strain resulted in a decreased quantity of 45-47S pre-rRNA transcripts at 36 and 48 hours p . i . , whereas infection with TBEV Neudoerfl strain reduced de novo synthesis of 45-47S pre-rRNA at 48 hours p . i . ( Fig 5C ) . Previously , a link between the inhibition of expression of 45-47S pre-rRNA and nucleolar stress was documented [31] . There are several hallmarks typical for nucleolar stress including disruption of nucleolus structure [51] . We , therefore , characterised the localization and production of nascent RNA at the cellular level and also investigated the structure of the nucleolus . DAOY cells infected with TBEV Hypr strain were analysed at 24 , 36 , and 48 hours p . i . using in situ Click reaction with 10 μM picolyl biotin azide and subsequent visualisation via streptavidin conjugated with DyLight-549 . As shown in Fig 5D , the overall production of nascent RNA in TBEV-infected cells started to decrease from 36 hours p . i . ; de novo synthesised RNA was exclusively detected in nuclei with foci of nascent RNA molecules localised in nucleoli . In addition , these nascent RNA foci were not structurally altered upon TBEV infection . The specificity of the labelling reaction was determined using EU-unlabelled cells in the Click reaction ( S5A Fig ) . In order to further verify that TBEV did not induce nucleolar re-arrangement due to nucleolar stress , we analysed the nucleolar structure upon TBEV Hypr infection using nucleophosmin ( NPM1; a nucleolar marker ) . As a positive control , cells were treated with 1 mM H2O2 for 45 minutes . No disruption of nucleoli in TBEV-infected cells was observed ( S5B Fig ) . These data imply that TBEV inhibits 45-47S pre-rRNA production without triggering the nucleolar stress pathway . Based on the observed TBEV interference with rRNA production on the transcriptional level , we sought to investigate if the levels and cellular localization of POLR1 changes in infected cells . As shown in Fig 6A and 6B , POLR1 was localised exclusively to the nuclei , and no translocation occurred in infected cells at any time interval tested . Nevertheless , POLR1 protein levels were impaired in TBEV Hypr-infected cells at 48 hours p . i . This may be a result of the previously mentioned translational shut-off since it coincided at 48 hours p . i . Besides , POLR1 mRNA levels were negatively affected by TBEV infection , too ( Fig 6C ) . In particular , POLR1A ( the largest subunit of the RNA polymerase I complex ) mRNA levels dropped to 60 ± 5% or 25 ± 1% in TBEV Neudoerfl- or Hypr-infected DAOY cells at 48 hours p . i . , respectively . TBEV-induced translational shut-off and the decrease in production of nascent 45-47S pre-rRNA raised the question whether these processes are casually interconnected . We analysed the rate of rRNA production in DAOY cells after treatment with cycloheximide ( CHX ) , an inhibitor of translation elongation . First , we determined the time- and dosage-dependent effect of CHX in DAOY cells using a Renilla ( RL ) luciferase-based reporter system . DAOY cells were first transfected with pRL-CMV and treated with CHX ( 50 , 100 , and 300 μg/ml ) . As shown in S6 Fig , all CHX concentrations tested decreased the production of luciferase . Moreover , the inhibition rate of luciferase production increased with longer exposure to CHX . Next , rRNA production in DAOY cells with decreased translational rate was assessed . Cells were treated with CHX ( 100 μg/ml ) for 6 or 14 hours and de novo RNA synthesis in CHX-treated cells was subsequently determined . Fig 7A shows a statistically significant decrease in levels of nascent 45-47S pre-rRNA for both intervals . In particular , the levels decreased to 22 ± 9% or 56 ± 16% during CHX treatment for 14 or 6 hours , respectively . In addition , total levels of mature 18S and 28S rRNAs were quantified in CHX-treated cells . Significant decreases in 18S rRNA levels were observed after a 14-hour incubation ( 65 ± 9%; Fig 7B ) . 28S rRNA levels were reduced to 81 ± 4% compared to control cells; however , this effect was not statistically significant ( Fig 7B ) . Quantification of 5S rRNA , a POLR3 transcript , revealed a statistically significant decrease even for this rRNA species after 14 hours of CHX treatment ( 46 ± 11%; Fig 7C ) . These data demonstrated that during translation inhibition induced by CHX , the quantity of rRNA transcripts of both RNA polymerases ( POLR1 and POLR3 ) were decreased . In comparison to the general rRNA synthesis shut-down resulting from the action of CHX , TBEV infection induced only a decrease in POLR1 rRNA transcripts ( Fig 7D ) . This suggests that TBEV infection specifically targeted POLR1 , which may subsequently result in translational shut-off .
TBEV infection is spreading through Europe , resulting in increased numbers of TBEV cases and emergence in previously unaffected areas . TBEV is known to be able to cause neurological symptoms in some infected patients , though little is known about its interplay with neural cells . The molecular basis of damage to the CNS following TBEV infection is still not fully understood . So far , it seems that it is a complex phenomenon combining multiple factors including host immune system [52] . Therefore , understanding the TBEV interaction with target cells and detailed description of processes of viral or host response can help to reveal new targets and ideas on how to treat this disease more successfully . To what extent the outcome of these infection-induced processes is reflected on longer term sequelae remains unrevealed . Metabolic labelling experiments demonstrated that TBEV infection interferes with the global de novo protein synthesis in infected cells . Surprisingly , the effect of translational arrest was so robust that even the protein levels of two commonly used housekeeping genes , GAPDH and HPRT1 , were significantly lowered ( Fig 3A and 3B ) . Cell lines of both neural and non-neural origin underwent translational shut-off , demonstrating thus the general nature of this phenomenon upon TBEV infection . However , the rate of reduction varied substantially in individual cell lines suggesting cell-dependent effects . TBEV Hypr strain caused a greater translational shut-off in all cell lines compared to the Neudoerfl strain . This may be due to the increased virulence and neuroinvasiveness of the Hypr strain [53] or due to susceptibility and tropism of the virus strains to specific cell types . Recent studies have demonstrated that some flaviviruses can cause translation suppression via diverse mechanisms [24 , 48] . These findings together with our results revise the idea of flaviviruses as “non-host cell protein synthesis influencers” [25 , 54 , 55] . Indeed , flaviviruses have been thought to avoid the host-cell protein synthesis shut-off as they replicate at a slower rate and global protein synthesis manipulation might have potentially deleterious effects on cell viability and virus yields [56 , 57] . However , reduced synthesis of host proteins had no adverse effect on the production of viral NS3 and C proteins ( Fig 1C ) , viral gRNA ( Fig 1B ) or production of viral progeny ( Fig 1A ) . This suggests that protein synthesis shut-off does not stop TBEV from successful replication . Viperin is a known interferon-stimulated gene ( ISG ) and has been described as a potent antiviral protein against members of the Flaviviridae family , especially TBEV [50 , 58–61] . Thereby it is anticipated to see an increase in viperin mRNA levels upon TBEV infection in DAOY cells . However , the absence of endogenous viperin protein in TBEV-infected cells is surprising . Thus , translational shut-off may yield multiple advantages to TBEV . Apart from gearing the host protein synthesis apparatus to the purposes of the virus , it may also perform as an immune evasion strategy by preventing ISG production . A widely used stable overexpression approach in an ISG/viperin study [59] might therefore mask the real interactions among flaviviruses and host cells during the infection . In general , our data highlight the importance of careful experimental design when studying virus-host interactions and ISG function specifically . To our knowledge virus-driven reduction in host rRNA levels has not been described before for any flavivirus . Only scarce information is available regarding the virus-induced reduction of rRNA expression , production , and maturation . For example , murine hepatitis virus directly reduces the levels of mature 28S rRNA [62]; Autographa californica multiple nucleopolyhedrovirus was shown to decrease both , 18S and 28S rRNAs [63] . Additionally , over-expression of HIV Tat protein in Drosophila melanogaster led to the impairment of 45S pre-rRNA precursor processing [35] . Similarly , herpes simplex virus 1 decreased the rate of rRNA maturation despite unaltered levels of 45-47S pre-rRNA and unchanged POLR1 activity [32] . The reduction of rRNA levels can be associated with the induction of nucleolar stress , which is characterized by several hallmarks including nucleolar and ribosomal disruption eventually leading to the activation of the p53 signalling pathway . A possible link between flaviviral pathogenesis and nucleolar stress was suggested previously . DENV and ZIKV , but not WNV were shown to induce nucleolar stress in infected cells by disruption of nucleoli , which resulted in an increased rate of apoptosis via the p53 signalling cascade [64] . However , no disruption of nucleoli was observed in the case of TBEV-infected DAOY cells ( S5B Fig ) , possibly not surprising as the TBEV infection specifically affects only the POLR1 activity . We propose alternative ways by which TBEV could interfere with transcription and/or translation in DAOY cells: 1 ) TBEV negatively affects the translation of host proteins , including POLR1 , transcription factors , and ribosomal proteins; their lower levels subsequently result in a decline in synthesis of all rRNA species; or 2 ) TBEV directly interferes with de novo synthesis of 45-47S pre-rRNA ( but not 5S rRNA ) via a POLR1 specific mechanism , which reduces the levels of 18S and 28S rRNAs and this leads to the decline of translational rate in host cells; 3 ) transcription and translation can be modified independently by both viral or cellular factors as a result of infection ( summarised in Fig 8 ) . Translational shut-off can otherwise be elicited by host cell defence mechanisms , such as activation of protein kinase R ( PKR ) or PKR-like endoplasmic reticulum kinase ( PERK ) [65–67] . To elucidate the exact mechanism of the inhibition of host protein and rRNA production and actual involvement of viral and host factors further experiments will be needed . These may for example assess whether viral proteins can directly inhibit transcription and/or translation . The present study does not elucidate this question and more work will be required to understand the processes; underlying the effects described here . An overall translational inhibition induced by CHX treatment results in reduced de novo synthesis of 45-47S pre-rRNA precursor as well as the levels of 5S rRNA in DAOY cells . In contrast , TBEV infection only affected the 45-47S pre-rRNA precursor ( and mature 18S and 28S rRNA levels ) and did not affect 5S rRNA . This suggests TBEV-specific inhibition of POLR1 activity , which could result in reduced production of host proteins . Further analyses are needed to characterise the connection between rRNA production arrests and translational shut-off upon TBEV infection . In summary , our results give new insights into the flavivirus-host interactions at the transcriptional/translational level . Moreover , a virus-induced rRNA decrease was described for flaviviral infection for the first time . The research here can contribute to understanding the mechanisms which determine at least to some extent the subsequent pathological processes . However , the relatively late onset of effects described in this study cannot completely rule out the possibility that our observations are due to cellular responses to TBEV infection rather than virus-mediated , or even combinations of both cellular and viral effects . More work is required to assess these possibilities in detail . | Tick-borne encephalitis virus ( TBEV ) is a causative agent of a severe human neuroinfection that threatens Europe and Asia . Little is known about the interaction of this neurotropic virus with neural cells , even though this may be important to better understand why or how TBEV can cause high pathogenicity in humans , especially following neural cell infection . Here , we showed that TBEV induced host translational shut-off in cells of neural origin . In addition , TBEV interfered also with the expression of host ribosomal RNAs . Interestingly , the transcriptional shut-off was documented for rRNA species transcribed by RNA polymerase I ( 18S rRNA , 28S rRNA and their precursor 45-47S pre-rRNA ) , but not for RNA polymerase III rRNA transcripts ( 5S rRNA ) . Artificial inhibition of host translation using cycloheximide resulted in the decrease of all rRNA species . Based on these data , TBEV seems to specifically target transcription of RNA polymerase I . These new findings further increase our understanding of TBEV interactions with a key target cell type . | [
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] | 2019 | Tick-borne encephalitis virus inhibits rRNA synthesis and host protein production in human cells of neural origin |
Nuclear factor ( NF ) -κB is a major survival pathway engaged by the Human T-Lymphotropic Virus type 1 ( HTLV-1 ) Tax protein . Tax1 activation of NF-κB occurs predominantly in the cytoplasm , where Tax1 binds NF-κB Essential Modulator ( NEMO/IKKγ ) and triggers the activation of IκB kinases . Several independent studies have shown that Tax1-mediated NF-κB activation is dependent on Tax1 ubiquitination . Here , we identify by co-immunoprecipitation assays NEMO-Related Protein ( NRP/Optineurin ) as a binding partner for Tax1 in HTLV-1 infected and Tax1/NRP co-expressing cells . Immunofluorescence studies reveal that Tax1 , NRP and NEMO colocalize in Golgi-associated structures . The interaction between Tax1 and NRP requires the ubiquitin-binding activity of NRP and the ubiquitination sites of Tax1 . In addition , we observe that NRP increases the ubiquitination of Tax1 along with Tax1-dependent NF-κB signaling . Surprisingly , we find that in addition to Tax1 , NRP interacts cooperatively with the Tax1 binding protein TAX1BP1 , and that NRP and TAX1BP1 cooperate to modulate Tax1 ubiquitination and NF-κB activation . Our data strongly suggest for the first time that NRP is a critical adaptor that regulates the assembly of TAX1BP1 and post-translationally modified forms of Tax1 , leading to sustained NF-κB activation .
Human T-Lymphotropic Virus type 1 ( HTLV-1 ) is the etiological agent of Adult T cell Leukemia/Lymphoma ( ATL ) and of HTLV-Associated Myelopathy/Tropical Spastic Paraparesis ( HAM/TSP ) [1]–[3] . HTLV-1 contains a unique pX region in the 3′ portion of its genome , which encodes regulatory and accessory proteins that are involved in viral replication and cell proliferation . Among them , Tax1 plays a critical role by triggering cell immortalization through various mechanisms [4] , including activation of signaling pathways such as NF-κB [5] . The NF-κB family of transcription factors plays an important role in the regulation of cellular activation , proliferation , and survival . A large number of stimuli including bacterial lipopolysaccharide ( LPS ) , tumor necrosis factor ( TNF ) -α , interleukin ( IL ) -1 and antigens can activate NF-κB . NF-κB activity is tightly regulated by inhibitory IκB proteins . Upon stimulation , signals are transduced that lead to the degradation of IκB , allowing NF-κB to translocate into the nucleus and to activate its target genes . IκB degradation by the 26S proteasome is triggered by its phosphorylation by a multisubunit IκB kinase ( IKK ) complex that contains two homologous catalytic subunits ( IKKα and IKKβ ) and a regulatory subunit , NF-κB Essential Modulator ( NEMO/IKKγ ) . An important mechanism in the NF-κB pathway is the interaction between NEMO and K63-linked polyubiquitin chains . In the case of TNF-α stimulation , the attachment of the polyubiquitin chains to RIP1 serves to bring the NEMO/IKK complex to the TNF-α receptor and is required for NF-κB activation [6] . Other studies have shown that TCR and IL-1 stimulations induce the attachment of K63-linked polyubiquitin chains to Bcl10 and IRAK1 respectively , which are required for binding to NEMO and subsequent activation of NF-κB [7] , [8] . One of the main mechanisms restricting this process is the NF-κB-mediated induction of deubiquitinases such as A20 and CYLD [9] , [10] . NF-κB activation plays a critical role in HTLV-1-mediated oncogenesis . This process occurs predominantly in the cytoplasm where HTLV-1 Tax1 binds NEMO and triggers the activation of IKKα and IKKβ [11]–[13] . Tax1 can also stimulate the alternative pathway of NF-κB activation through the IKKα-dependent processing of NF-κB p100 precursor protein [14] . Independent studies have shown that Tax1 ubiquitination is dependent on the E2 ubiquitin-conjugating enzyme Ubc13 and is critical for Tax1 binding to NEMO and the subsequent NF-κB activation [15]–[18] . In addition , Tax1 binding protein TAX1BP1 [19] , [20] is involved in the recruitment of A20 deubiquitinase and the negative control of TNF-α- , IL-1- and LPS-mediated NF-κB activation [17] , suggesting that Tax1-dependent activation of NF-κB could also be more complex than originally thought . Because cellular factors other than NEMO and Ubc13 could contribute to the activation of NF-κB by Tax1 , we searched for novel interactors of Tax1 and Tax2 , the equivalent of Tax1 for HTLV-2 . Here we report the identification of NEMO-Related Protein ( NRP ) as a novel Tax interactor . NRP is ubiquitously expressed and exhibits strong homologies to NEMO ( 53% sequence similarity ) , but its function is still unknown [21] . Mutations in its sequence have been associated with primary open-angle glaucoma ( POAG ) , and for this reason , NRP was also named Optineurin for “optic neuropathy inducing” protein [22] . We show that both NRP and TAX1BP1 form a functional complex with Tax1 , and we demonstrate that a synergistic interaction between TAX1BP1 and NRP contributes to Tax1-mediated NF-κB activation .
To identify novel binding partners of Tax proteins , we used a standard yeast two-hybrid screening procedure . Full-length Tax1 or Tax2 proteins were fused to Gal4 DNA binding domain ( Gal4-BD ) and used as baits to screen a library of human spleen cDNA fused to Gal4 transactivation domain ( Gal4-AD ) . Screens were performed by mating to reach a five-time coverage of the cDNA library complexity , and yielded 59 positive yeast colonies with Tax1 and 36 with Tax2 . Using Tax1 as bait , we retrieved three previously known interactors of Tax1 [TAX1BP1 ( 2 colonies ) , TAX1BP3 ( 37 colonies ) and SRF ( 3 colonies ) ] , which demonstrates the specificity of the screen . NEMO was only found using Tax2 as bait ( 2 colonies ) . This corresponds to a known limitation of the two-hybrid system since numerous pairs of interacting proteins fail to rebuild a functional transcription factor in yeast when fused to Gal4-DB and Gal4-AD . From the screen performed with Tax2 , we essentially selected yeast colonies expressing a new interactor of Tax: NEMO-Related Protein NRP ( 23 colonies ) . Although various lengths of the NRP protein were encoded , the smallest NRP fragment shown to interact with Tax2 was spanning amino acid 411 to the C-terminus end of the protein . This led us to test the ability of Tax1 to interact with NRP using a different binding assay . In order to confirm Tax/NRP interaction , Tax1 and Tax2 were co-expressed in 293T cells with VSV-tagged NRP and co-immunoprecipitations were performed . Both Tax2 ( Figure 1A , lane 2 ) and Tax1 ( Figure 1B , left panel , lane 3 ) were detected in VSV-NRP immunoprecipitates . The reverse experiment ( i . e . immunoprecipitation of Tax1 followed by immunoblotting for VSV-NRP ) confirmed the interaction between the two proteins ( Figure 1B , right panel , lane 3 ) . We then aimed to demonstrate the interaction between Tax1 and endogenous NRP in two HTLV1-infected cell lines , C8166 and C91PL . The uninfected cell line CEM was used here as a negative control . As expected , Tax1 could be specifically recovered from NRP immunoprecipitates in both HTLV-1-infected cell lines ( Figure 1C , compare lanes 3 and 5 with lane 1 ) . The specificity of these interactions was controlled using an irrelevant antibody for the immunoprecipitation ( Figure 1C , lanes 2 , 4 and 6 ) . Hence , NRP interacts with Tax1 both in transfected and in infected cells . In order to confirm the interaction with an alternative biochemical method , we determined whether Tax1 and NRP could be found in the same fractions after gel filtration ( Figure 1D ) . Experiments using glycerol gradients were also performed ( Figure S1 ) . Tax1 and HA-tagged NRP were co-expressed in HeLa cells . After gel filtration , extracts were analyzed by western blot . As shown in Figure 1D , Tax1 was recovered from fractions 33 to 35 ( Figure 1D , lower panel ) , corresponding to low molecular mass fractions . This subset of Tax1 molecules essentially represents free molecules . Tax1 was also recovered from fraction 25 , suggesting that a subset of Tax1 molecules was present in high molecular mass complexes . HA-NRP was recovered from fractions 22 to 26 ( Figure 1D , upper panel ) , showing that a majority of HA-NRP molecules were found in high molecular mass complexes . Co-fractionation of Tax1 and HA-NRP in fraction 25 indicated that both proteins could be found in the same complexes . These results obtained with distinct biochemical methods support the interaction between Tax1 and NRP . Tax1 has been described as having distinct localizations depending on its post-translational modification status [15] , [16] . Ubiquitinated Tax1 was reported to localize in the cytoplasm , more specifically in Golgi/centrosome-associated structures [18] , where it interacts with NEMO and induces NF-κB activation . SUMOylated Tax1 , however , was found in the nucleus [15] , [16] . NRP , on the other hand , is predominantly localized at the Golgi apparatus [21] . Since Tax1 was shown to promote the relocalization of the NEMO/IKK complex to the Golgi apparatus [23] , we suspected that NRP might also interact with Tax1 at the Golgi apparatus . We performed a series of immunofluorescence stainings in HeLa cells expressing a C-terminal GFP-tagged Tax1 plasmid , which was previously demonstrated to have a subcellular localization and an ability to activate NF-κB similar to those of untagged Tax1 ( [24] and data not shown ) . To evaluate whether NRP colocalizes with Tax1-GFP and NEMO at the Golgi apparatus , we performed a double staining for NRP and either NEMO or GM130 in order to visualize the Golgi apparatus ( Figure 2 ) . As previously described , Tax1-GFP showed a discrete granular appearance in the cytoplasm , which was more intense at the Golgi apparatus as shown by colocalization with GM130 staining ( Figure 2A , green and blue ) . Staining for NEMO indicated that expression of Tax1-GFP induced the recruitment of this protein at the Golgi apparatus where both proteins colocalize ( Figure 2B , green and blue ) . As expected , NRP was predominantly localized at the Golgi apparatus , and its localization was not affected by Tax1-GFP expression ( Figure 2A and B , red ) . Interestingly , we also observed colocalization between Tax1-GFP and NRP in these Golgi-associated structures ( Figure 2A and B ) . Because HTLV-1 infects mainly T cells in vivo , we performed similar immunofluorescence microscopy studies using Jurkat T cells ( Figure S2 ) . Consistent with the results obtained in HeLa cells , a subset of Tax1-positive cells harbored a cytoplasmic staining for Tax1-GFP , which colocalized with NRP ( Figure S2A and B , red ) and NEMO ( Figure S2B , blue ) in perinuclear structures that were associated with the Golgi apparatus ( Figure S2A , blue ) . As in HeLa cells , we observed that Tax1-GFP expression had no effect on NRP localization ( Figure S2 , compare A and C ) . These results are consistent with the observed interaction between Tax1 and NRP in vivo , and suggest that these interactions occur at the Golgi apparatus where NEMO is recruited . Because it has been reported that Tax1 interacts with NEMO at the Golgi apparatus in an ubiquitin-dependent manner , we hypothesized that a similar mechanism could be involved in the Tax1/NRP interaction . In order to map the domain ( s ) of NRP involved in the binding to Tax1 , we performed co-immunoprecipitation experiments using a series of NRP mutants . We first co-expressed Tax1 with either a N-terminal or a C-terminal deletion mutant of NRP ( Figure 3A ) . Immunoprecipitation of NRP ΔN alone led to the recovery of Tax1 from cell lysates ( Figure 3B , upper panel , lane 3 ) while NRP ΔC was unable to interact with Tax1 ( Figure 3B , upper panel , lane 2 ) , showing that Tax1 binds to the C-terminal part of NRP . Immunoblotting for VSV-NRP confirmed that these mutants were expressed at similar levels when compared to the wild-type NRP ( Figure 3B , lower panel ) , and both were efficiently immunoprecipitated with the anti-VSV antibody ( Figure 3B , middle panel ) . Because the C-terminal domain of NRP encompasses an ubiquitin-binding domain ( UBD ) ( Figure 3A ) , we tested the contribution of this domain to the interaction with Tax1 ( Figure 3C ) . Interestingly , a single point mutation in this domain known to disrupt the binding to K63-linked polyubiquitin ( D474N ) [25] was sufficient to severely reduce the interaction of NRP with Tax1 ( Figure 3C , compare lane 1 and 2 ) . This result suggests that NRP binding to Tax1 is mediated by an interaction between NRP UBD and K63-linked polyubiquitin chains conjugated to Tax1 . Tax1 displays 10 lysine residues to which ubiquitination chains may be linked . It has been shown that lysines 4 to 8 significantly contribute to Tax1 polyubiquitination [15] , [16] , [26] . 293T cells were therefore transfected with Tax1 mutants in which all ( K1–10R ) or only a subset ( K7–8R ) of lysines were mutated into arginines . Interestingly , lysine-less Tax1 was impaired for NRP binding ( Figure 3D , upper panel , lane 2 ) , although this mutant was efficiently precipitated by the anti-Tax1 antibody ( Figure 3D , middle panel , lane 2 ) . Furthermore , the defect in NRP binding was not observed with mutant K7–8R ( Figure 3D , upper panel , lane 3 ) . These results strongly suggest that the integrity of Tax1 acceptor sites for ubiquitin is critical for the interaction with NRP . Taken together , these results further support the hypothesis that NRP binds to Tax1 through polyubiquitin chains on Tax1 . Tax1 ubiquitination is critical for its binding to the NEMO/IKK complex and its subsequent activation [15] , [16] , [18] . Given that NRP binds to polyubiquitinated Tax1 , we wondered whether it could modulate Tax1 polyubiquitination status , and hence its ability to activate the NEMO/IKK complex . We thus analyzed the effect of silencing NRP on the level of polyubiquitinated Tax1 . His-tagged Tax1 and HA-tagged ubiquitin were co-expressed in 293T cells , with a control siRNA ( Figure 4A , lane 1 ) or with a siRNA targeting NRP ( Figure 4A , lane 2 ) . Ni-NTA pulldown was then performed in highly reducing and denaturating conditions , in order to avoid any deubiquitination and to ensure that only products covalently linked to Tax1 would be purified . By blotting for HA-ubiquitin , we assessed the level of polyubiquitinated Tax1 in each sample , which appears as high-molecular-weight products . When compared to control cells , the level of polyubiquitinated Tax1 was strikingly reduced in NRP-silenced cells , ( Figure 4A , upper panel , compare lane 1 and 2 ) . As control , we analyzed the level of NRP in cell lysates , and showed that it was indeed reduced in cells transfected with NRP-directed siRNA ( Figure 4A , lower panel , compare lane 1 and 2 ) . Thus , in the absence of NRP , polyubiquitinated Tax1 is less abundant . One model that could account for this observation is that polyubiquitinated Tax1 is stabilized through its interaction with NRP . To test whether the effect of NRP on Tax1 polyubiquitination was dependent upon the interaction between both proteins , we performed the same type of experiments in cells over-expressing wild-type or mutant forms of NRP ( NRP ΔN , ΔC and D474N ) ( Figure 4B ) . We predicted that wild-type NRP or NRP ΔN , which are able to bind to Tax1 , would stabilize polyubiquitinated Tax1 , whereas NRP ΔC and NRP D474N , which have lost the potential to bind to Tax1 , would not . In order to highlight the differences among the lanes , a very short exposure time is shown , which accounts for the weak HA signal obtained in the absence of over-expressed NRP ( Figure 4B , upper panel , lane 3 ) . However , longer exposures revealed the presence of polyubiquitinated Tax1 in this lane ( data not shown ) . As expected , we observed a correlation between the ability of NRP variants to bind to Tax1 , and the level of polyubiquitinated Tax1 ( Figure 4B , upper panel , compare lanes 5 and 9 with 7 and 11 ) . However , we also observed that the levels of polyubiquitinated Tax1 were enhanced in cells over-expressing NRP ΔC or NRP D474N when compared to cells expressing endogenous NRP only ( Figure 4B , compare lanes 7 and 11 to lane 3 ) . This might be the consequence of a potential residual interaction between these mutants and Tax1 . To determine the linkage specificity of Tax1 polyubiquitination , we used an ubiquitin mutant ( HA-Ub K0 ) , in which all lysine residues are mutated to arginine ( HA-Ub K0 ) and that can therefore no longer build conventional polyubiquitin chains . As expected , the HA-Ub K0 did not support Tax1 ubiquitination , indicating that the high-molecular-weight products of Tax1 indeed represented polyubiquitin chains ( Figure 4B , lanes 4 , 6 , 8 , 10 and 12 ) . Altogether , these data suggest that NRP binds and stabilizes the polyubiquitin chains linked to Tax1 . Because previous studies have suggested that Tax1 ubiquitination is critical for its ability to activate the NF-κB pathway [15] , [16] , we hypothesized that stabilization of Tax1 polyubiquitination by NRP would enhance activation of this pathway . We therefore tested the effect of increasing or decreasing NRP expression on Tax1-induced NF-κB activation using an NF-κB reporter gene assay ( Figure 5 ) . Jurkat cells were transfected with an NF-κB reporter gene together with Tax1 and increasing amounts of VSV-tagged NRP . As previously reported [5] , NF-κB activity was induced upon Tax1 over-expression , and VSV-NRP further enhanced NF-κB activity in a dose-dependent manner ( Figure 5A , left panel ) . We then evaluated the effect of NRP knockdown on Tax1-induced NF-κB activity . Compared with control siRNA , a two-fold decrease in Tax1-mediated NF-κB activity was observed when expression of NRP was silenced ( Figure 5B , left panel ) , whereas no significant impact on the basal activity of the promoter could be measured in the absence of Tax1 . To ensure the specificity of NRP's effect on Tax1-mediated NF-κB activation , we performed the same experiments using a HTLV-1-LTR-luc reporter plasmid , which is known to be under the control of CREB rather than NF-κB . Using this construct , no or only a limited effect of NRP over-expression or silencing was observed ( Figure 5A , right panel , and Figure 5B , right panel ) . As a control , NRP expression was determined in the presence or absence of siRNA directed against NRP ( Figure 5B , left and right panels ) . Thus , NRP specifically enhances the activity of Tax1 on the NF-κB pathway . We also tested whether the potentiating effect of NRP on Tax1-dependent NF-κB activity was dependent upon the interaction between Tax1 and NRP . Because we showed that NRP-D474N was impaired for the binding to Tax1 ( Figure 3C ) as well as for the stabilization of Tax1 polyubiquitination ( Figure 4B ) , we compared the ability of wild-type NRP and NRP-D474N to potentiate Tax1 activity on the NF-κB reporter plasmid . As expected , NRP-D474N had no effect on Tax1 activity when compared to wild-type NRP , although expression levels of both constructs were similar ( Figure 5C ) . Taken together , these results indicate that NRP specifically modulates Tax1-induced NF-κB activation , possibly by stabilizing Tax1 polyubiquitination in an interaction-dependent manner . Since we observed that NRP stabilizes Tax1 poly-ubiquitination , we wondered whether Tax1-binding protein 1 ( TAX1BP1 ) , which is also involved in ubiquitin-dependent regulation of NF-κB , could participate in this process . TAX1BP1 was originally identified as a binding partner of Tax1 [19] , [20] . More recently , TAX1BP1 was reported to interact with A20 , Itch and RNF11 to form a functional ubiquitin-editing complex that regulates the ubiquitination of RIP1 and TRAF6 [27] . Thus , we hypothesized that TAX1BP1 acts together with NRP to modulate Tax1 ubiquitination . 293T cells were cotransfected with VSV-tagged NRP , Flag-tagged TAX1BP1 and Tax1 , and immunoprecipitation of either Tax1 , Flag-TAX1BP1 or VSV-NRP was performed ( Figure 6A , B and C , respectively ) . Immunoprecipitates were then blotted with anti-Flag , anti-VSV or anti-Tax1 antibodies . These experiments confirmed that TAX1BP1 interacts with Tax1 ( Figure 6A , lane 2 ) . In addition , we observed that TAX1BP1 also interacts with NRP ( Figure 6C , lane 4 ) . More interestingly , the amount of TAX1BP1 associated with Tax1 was increased when NRP was co-expressed ( Figure 6A , compare lanes 2 and 4 ) . Similarly , the interaction between NRP and Tax1 was strongly induced in the presence of TAX1BP1 ( Figure 6A , compare lanes 3 and 4 ) and the interaction between NRP and TAX1BP1 was also increased by the expression of Tax1 ( Figure 6B , compare lanes 2 and 3 , and Figure 6C , compare lanes 4 and 6 ) . Thus , these results demonstrate that these three proteins interact with each other and suggest that NRP can be part of a ternary complex with Tax1 and TAX1BP1 . To study the functionality of this complex , we used several approaches . First , we performed immunofluorescence imaging to determine whether NRP or TAX1BP1 alone or in association could affect the localization of Tax1 to the Golgi apparatus and the recruitment of the NEMO/IKK complex by Tax1 to this organelle . Because NRP is localized at the Golgi apparatus , we first asked whether Tax1 localization to the Golgi-associated structures was dependent upon NRP expression . NRP-specific siRNA was used to specifically knockdown NRP expression in HeLa cells and Tax1-GFP localization was then investigated ( Figure S3A and S3C ) . Silencing NRP did not impair the localization of Tax1-GFP to the Golgi apparatus ( compare Figure S3B and S3C , upper panel ) , where it was still able to colocalize with NEMO ( Figure S3C , lower panel ) . We then tested the effect of depleting either TAX1BP1 alone ( Figure S3A and S3D ) or together with NRP ( Figure S3A and S3E ) on the localization of Tax1 and NEMO to the Golgi apparatus . Preventing the expression of TAX1BP1 or of both TAX1BP1 and NRP had no effect on the subcellular distribution of Tax1 and NEMO ( compare Figure S3B , S3D and S3E ) . Collectively , these results suggest that NRP and TAX1BP1 are not critical for the localization of Tax1 and for the recruitment of NEMO/IKK complex to the Golgi apparatus . In another approach , we determined whether depleting TAX1BP1 could affect the regulatory effect of NRP on Tax1 ubiquitination and NF-κB activation ( Figure 7 ) . Interestingly , silencing TAX1BP1 expression precluded the stabilization of Tax1 ubiquitination that was observed when over-expressing NRP ( Figure 7A , compare lanes 2 and 4 ) . In addition , over-expressed TAX1BP1 up-regulated Tax1-dependent NF-κB activation ( Figure 7B , left panel , lane 2 ) and this effect was decreased by silencing NRP ( Figure 7B , left panel , lane 4 ) . Furthermore , to determine whether NRP and TAX1BP1 act synergistically or not , we examined the effect of double-siRNA knock-down of NRP and TAX1BP1 on the activation of NF-κB mediated by Tax1 . As expected from the ubiquitination assay ( Figure 7A ) , we observed that depletion of TAX1BP1 decreased Tax1-mediated NF-κB activation ( Figure 7B , right panel , lane 2 ) . Interestingly , following the double depletion , the level of NF-κB activation was not further decreased as compared to the single depletion of TAX1BP1 ( Figure 7B , right panel , lane 4 ) . As controls , NRP , TAX1BP1 and Tax1 expression levels were determined by western blot ( Figure 7A and 7B ) . Altogether , these results suggest that NRP and TAX1BP1 cooperate in Tax1-mediated NF-κB activation .
It is well established that one of the primary actions of Tax1 is to permanently activate the NF-κB signaling in the cytoplasm , and several models have been suggested to explain how this occurs . An important advance came first from experiments showing that Tax1 directly interacts with NEMO , which then functionally recruits Tax1 into the large NEMO/IKKα/IKKβ complex that phosphorylates IκB molecules [11]–[13] . How this interaction is regulated is not completely understood . To gain insight into this , we searched for additional Tax-interacting proteins . This led to the identification of NRP , also named Optineurin , as an interacting protein for Tax . This interaction is mediated through the ubiquitin-binding domain of NRP . Interestingly , mutations of the ubiquitination sites of Tax1 prevented its association with NRP , strongly suggesting that Tax1 ubiquitination is required for its interaction with NRP . Ubiquitination of Tax1 provides an important regulatory mechanism that promotes Tax1-mediated activation of NF-κB [15]–[17] , [23] . Tax1 polyubiquitin chains are composed predominantly of K63-linked chains and the ubiquitination of Tax1 is dependent on the E2 ubiquitin-conjugating enzyme Ubc13 [17] , [18] . The NF-κB activation process occurs through the direct binding of the ubiquitinated form of Tax1 to NEMO [16] in a specific subcellular compartment [18] , [23] , [28] . Different studies have suggested that Tax ubiquitination regulates IKK relocalization to the Golgi apparatus [23] or to the centrosome [18] . This relocalization involves the accumulation of Tax1 in Golgi-associated lipid rafts allowing the recruitment of the NEMO/IKK complex to these microdomains [28] . The interaction of NEMO with polyubiquitinated substrates is involved in transporting the NEMO/IKK complex towards its site of activation . This has been well described for the stimulation of NF-κB by the TNF-R and TCR [6] , [29] , allowing the recruitment of NEMO to these receptors and the subsequent phosphorylation of IKKβ by the upstream kinase TAK1 . Similarly , DNA damage causes the translocation of NEMO from the cytoplasm to the nucleus and its phosphorylation by ATM [30] . Although previous studies have suggested that Tax1 ubiquitination is correlated with the localization of the NEMO/IKK complex at the Golgi apparatus , modulation of this process is still poorly understood . Our model is that NRP is a positive player in Tax1-induced NF-κB activation by increasing the polyubiquitination of Tax1 . This model is supported by our results showing that increasing the level of NRP increases Tax1 polyubiquitination ( Figure 4 ) and that the level of NRP expression correlates with Tax1-induced NF-κB activation ( Figure 5 ) . Whether NEMO also exerts a similar effect on Tax1 ubiquitination still remains unknown . Of note , a different effect of NRP was obtained with TNF-α-induced NF-κB activation , where NRP was shown to compete with NEMO for the binding to polyubiquitinated RIP and consequently to inhibit NF-κB activation [25] . The situation with Tax1 is unusual since the polyubiquitinated substrate that binds to NEMO and NRP is Tax1 itself , an NF-κB activator . Concerning the regulation of Tax1 ubiquitination by NRP , the simplest interpretation of our experiments is that NRP either interacts directly or indirectly with an ubiquitin ligase , or that it prevents the interaction of Tax1 with a deubiquitinase . The E3 ubiquitin ligase ( s ) responsible for K63-linked polyubiquitination and the deubiquitinase responsible for the cleavage of these ubiquitin chains on Tax1 remain to be identified . TAX1BP1 is a cellular protein that binds to Tax1 and acts as an ubiquitin-dependent negative regulator of NF-κB signaling in response to TNF-α stimulation [27] . It has recently been shown that this negative regulation is mediated by a quaternary complex containing TAX1BP1 , A20 , Itch and RNF11 [31]–[34] . Our results indicate that TAX1BP1 interacts with NRP and Tax1 individually and also with both proteins together to form a ternary complex , raising the possibility that TAX1BP1 participates in NRP-mediated enhancement of Tax1 ubiquitination . We have observed that the stabilization of Tax1 ubiquitination by NRP was completely impaired in the absence of TAX1BP1 ( Figure 7A ) and that NRP and TAX1BP1 cooperated to positively regulate Tax1-induced NF-κB activation ( Figure 7B ) . Thus , we can propose that the negative regulation of TNF-α-induced NF-κB activation is mediated by a quaternary A20/TAX1BP1/Itch/RNF11 complex , as opposed to the positive regulation of Tax1-induced NF-κB activation , which is mediated by a ternary complex containing Tax1 , TAX1BP1 and NRP . Since it has been shown that Tax1 inactivates A20 by disrupting the TAX1BP1/Itch/A20 complex , thus counteracting its negative function [33] , we speculate that NRP also is involved in this process . Furthermore , since RNF11 has been shown to interact with NRP [35] , it will be important to determine whether this protein is also present in the TAX1BP1/Tax1/NRP complex and whether it regulates Tax1 ubiquitination . Future studies are needed to specifically address the mechanism whereby the Tax1/NRP/TAX1BP1 complex positively regulates Tax1 ubiquitination and subsequent NF-κB activation .
HeLa and 293T cell lines were grown in DMEM medium . HTLV-1-infected C8166 and C91PL and uninfected CEM and Jurkat cell lines were grown in RPMI 1640 medium . In all cases , the medium was supplemented with fetal bovine serum ( 10% ) and antibiotics ( 100 units/ml penicillin and 100 µg/ml streptomycin ) , and cells were maintained at 37°C in 5% CO2 . pSG5M-Tax1 , pSG5M-Tax2 , Tax1-6His , Tax1-GFP plasmids were previously described [18] , [36] . Tax mutants harboring substitutions of all ( K1–10R ) or some ( K7–8R ) lysines into arginines were described elsewhere and were kindly provided by C . Pique [26] . HA- and VSV-tagged NRP plasmids were obtained by cloning NRP ORF ( aa 1–577 ) into pT7link-HA or pcDNA3/pT7-link-GVSV vectors at EcoRI sites . To identify the domains of NRP required for in vivo interaction with Tax1 , modified forms of NRP ( VSV-NRP-ΔC ( aa 1–278 ) and VSV-NRP-ΔN ( aa 300–577 ) ) ( Figure 3A ) were generated by site-directed mutagenesis with a PCR-based strategy and inserted at EcoRI sites into pcDNA3/pT7-link-GVSV vector . VSV-NRP D474N plasmid was generated by site-directed mutagenesis . HA-tagged wild-type and lysine-less ( K0 , in which all lysine residues are mutated into arginine ) ubiquitin constructs were obtained from P . Jalinot [37] . Flag-tagged TAX1BP1 plasmid was a kind gift from E . Harhaj [33] . NRP double-stranded siRNA ( GGAGACUGUUGGAAGCGAAGU ) and β-globin double-stranded siRNA ( control , GGUGAAUGUGGAAGAAGUU ) were purchased from Proligo ( Sigma ) . SMART pool siRNA directed against TAX1BP1 was purchased from Dharmacon . The following antibodies were used: anti-Tax1 ( Tab172 ) , anti-Tax2 ( GP3738 ) [36] , anti-NRP ( 100 000 , Cayman Chemical ) , anti-HA ( MMS-101R , Covance ) , anti-VSV ( V 5507 , Sigma-Aldrich , or ascite fluid of clone P5D4 ) , anti-GM130 ( 610823 , BD Transduction Laboratories ) , anti-NEMO ( 611306 , BD Transduction Laboratories ) , anti-Flag M2 ( F-3165 , Sigma-Aldrich ) , anti-TAX1BP1 ( sc-81390 , Santa Cruz Biotechnology ) , anti-His ( sc-804 , Santa Cruz Biotechnology ) . Yeast culture mediums were prepared and screens were performed as previously described [38] . Tax1 and Tax2 coding sequences were cloned by in vitro recombination using the Gateway® technology ( Invitrogen ) into Gal4-BD yeast two-hybrid vector pDEST32 ( Invitrogen ) , and transformed into AH109 yeast strain ( Clontech ) using a standard Lithium/Acetate procedure . GAL4-BD-Tax1 and -Tax2 fusion proteins did not induce autonomous transactivation of HIS3 reporter gene , and screens were performed on synthetic medium lacking histidine ( −His medium ) and supplemented with 10 mM of 3-amino-1 , 2 , 4-triazole ( 3-AT , Sigma-Aldrich ) . A mating strategy was used for screening the human spleen cDNA library cloned in the GAL4-AD pPC86 vector ( Invitrogen ) and previously transformed into Y187 yeast strain ( Clontech ) . After 6 days of culture on selective medium , [His+] colonies were selected and purified over 3 weeks by culture on selective medium to eliminate false-positives . AD-cDNAs were amplified by PCR from zymolase-treated yeast colonies using primers that hybridize within the pPC86 regions flanking cDNA inserts . PCR products were sequenced and cellular interactors were identified by BLAST analysis . 293T cells were transfected using either the Polyfect reagent ( Qiagen ) or Lipofectamine 2000 ( Invitrogen ) . For luciferase assays , 293T were first transfected with siRNA using Icafectin 442 ( Eurogentec ) , followed 48 h later by DNA and siRNA transfection using Lipofectamine 2000 . Jurkat cells were transfected using the Superfect reagent ( Qiagen ) , except in Figure S2 where they were nuleofected using the Amaxa Nucleofector Technology ( Amaxa Biosystems ) . HeLa cells were transfected using the Effectene reagent ( Qiagen ) . 293T , CEM , C8166 and C91PL cell lines cells were lysed in Chris buffer ( 50 mM Tris , pH 8 . 0 , 0 . 5% Nonidet P-40 , 200 mM NaCl , and 0 . 1 mM EDTA ) supplemented with a cocktail of protease inhibitors ( Complete , Roche ) , and the phosphatase inhibitors sodium fluoride ( 100 mM ) and sodium orthovanadate ( 2 mM ) . Proteins were then recovered by immunoprecipitation from an equivalent amount of proteins , using one of the following antibodies: anti-Tax , anti-VSV , anti-NRP , anti-Flag . Immune complexes were recovered with magnetic Staphylococcus aureus Protein A or Protein G beads ( Bio-Adembeads , Ademtech ) . Immunoprecipitates were then washed with lysis buffer , eluted and resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis . Subsequent immunoblots were performed according to a previously described protocol [36] and proteins transferred to nitrocellulose ( I-Blot , Invitrogen ) or Immobilon membranes ( Millipore ) were revealed with ECL Westen Blotting Substrate ( Pierce ) or ECL Plus Western Blotting Detection Reagent ( Amersham ) . HeLa cells were lysed in a lysis buffer ( 50 mM Tris-HCl pH 7 . 4 , 120 mM NaCl , 5 mM EDTA , 0 . 5% Nonidet-P40 , 0 . 2 mM Na3VO4 , 1 mM DTT , 1 mM PMSF ) in the presence of protease inhibitors ( Complete , Boehringer ) by 15 passages through a 24-gauge needle . Whole cell extracts were fractionated by centrifugation at 39000 rpm for 24 hrs on a 10–40% glycerol gradient ( 12 ml ) , using an Sw 41 rotor ( Beckmann ) . The glycerol-containing buffers were prepared with the same composition as the lysis buffer . Twenty-four fractions ( 0 . 5 ml each ) were then collected and 20 µl of each fraction were processed for immunoblot analysis . For the gel filtration analysis , whole cell extracts were subjected to another centrifugation at 15000 rpm for 15 min , and then purified using Quick Spin Sephadex G25 columns ( Roche ) . Gel filtration chromatography was carried out on a Superpose 6 column ( Pharmacia ) in a FPLC running buffer ( 50 mM Tris-HCl pH 7 . 4 , 120 mM NaCl , 5 mM EDTA , 0 . 1% Nonidet-P40 , 0 . 2 mM Na3VO4 , 1 mM DTT , 1 mM PMSF , 10% glycerol ) in the presence of protease inhibitors ( Complete , Boehringer ) . The columns were precalibrated with albumin ( 67 kDa ) , aldolase ( 158 kDa ) , catalase ( 232 kDa ) , ferritin ( 440 kDa ) , thyroglobulin ( 669 kDa ) and blue dextran ( 2000 kDa ) . Several glycerol gradients were loaded and analyzed with the same proteins , with the addition of the ovalbumin ( 43 kDa ) . Forty fractions of 0 . 5 ml each were then collected and 20 µl of fractions 16 to 40 were processed for immunoblot analysis . Forty-eight hours after transfection , cells were harvested in cold PBS and lysed under highly denaturating and reductive conditions in Guanidium Buffer ( 10 mM Tris HCl pH 8 . 0 , 100 mM Na2HPO4/NaH2PO4 , 6 M Guanidium ) [26] . Cell lysates were then incubated with Ni-NTA beads ( His-select HF Agarose Beads , Sigma-Aldrich ) at room temperature . Beads were then extensively washed with Guanidium Buffer , Urea Buffer ( 10 mM Tris HCl pH 6 . 4 , 100 mM Na2HPO4/NaH2PO4 , 8 M Urea ) and cold PBS . Bound proteins were finally eluted and processed for immunoblot analysis . Twenty-four hours post-transfection , cells were fixed with 4% paraformaldehyde , rinsed and permeabilized in PBS containing 0 . 5% Triton X-100 . Following pre-incubation with PBS containing 5% BSA , cells were incubated with primary antibodies in PBS containing 1% BSA for 1 h at room temperature . Samples were then stained with Alexa Fluor 568-conjugated goat anti-rabbit IgG ( A-11010 , Invitrogen ) , Cy5-conjugated donkey anti-mouse IgG ( 715-175-150 , Jackson ImmunoResearch Laboratories ) , or AMCA-conjugated horse anti-mouse IgG ( CI-2000 , Vector Laboratories ) for 1 h at room temperature . Where indicated , an additional staining of nuclei was performed with DAPI ( Sigma ) for 5 min . The coverslips were washed , mounted with Vectashield Mounting Medium ( H-1000 , Vector Laboratories ) , and examined under a Zeiss Axioplan 2 microscope , using the Zeiss ApoTome system and the Zeiss Axiovision 4 . 4 software . Jurkat and 293T cells were transiently transfected with either an HTLV-1-LTR-luc or an Igκ- ( κB ) 3-luc plasmid together with the indicated plasmids or siRNA . The amount of total DNA was equalized using a pSG5M backbone vector , as previously reported [36] . All transfections were carried out in the presence of a renilla luciferase vector ( phRG-TK ) in order to normalize the results for transfection efficiency . Luciferase activity was assayed 18 h post-transfection using the Dual-Luciferase Reporter Assay System ( Promega ) on a Berthold LB9500C luminometer as reported previously [39] . | Oncogenic viruses ( i . e . , viruses that can induce cancer ) have usually been found to deregulate several cellular signaling pathways controlling cell survival and proliferation . Among those , the NF-κB pathway is particularly important . In this study , we focus on the Human T-Lymphotropic Virus type 1 ( HTLV-1 ) , which infects immune T cells , and is associated with the development of a severe hematological disease , termed adult T cell leukemia . The viral Tax oncoprotein is known to activate the NF-κB pathway , but the precise mechanism is still under investigation . In cells , proteins can undergo modifications that can modulate their function . In the case of Tax , a modified form of the protein ( ubiquitinated Tax ) is able to activate the NF-κB pathway . Our aim was to identify cellular proteins that participate in the modification of Tax , and in turn in the regulation of its function . We show for the first time that the cellular protein NRP/Optineurin interacts with Tax and increases its ubiquitination , thus leading to an enhanced NF-κB activation . We further demonstrate that TAX1BP1 , another cellular protein that had been previously identified as a partner of Tax , also participates in this regulation . Thus , this study uncovers new actors of the virally induced cell signaling . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
] | [
"molecular",
"biology/post-translational",
"regulation",
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"gene",
"expression",
"virology/virulence",
"factors",
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"mechanisms",
"infectious",
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"infections",
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] | 2009 | NRP/Optineurin Cooperates with TAX1BP1 to Potentiate the Activation of NF-κB by Human T-Lymphotropic Virus Type 1 Tax Protein |
Mutations in a number of genes have been linked to inherited dilated cardiomyopathy ( DCM ) . However , such mutations account for only a small proportion of the clinical cases emphasising the need for alternative discovery approaches to uncovering novel pathogenic mutations in hitherto unidentified pathways . Accordingly , as part of a large-scale N-ethyl-N-nitrosourea mutagenesis screen , we identified a mouse mutant , Python , which develops DCM . We demonstrate that the Python phenotype is attributable to a dominant fully penetrant mutation in the dynamin-1-like ( Dnm1l ) gene , which has been shown to be critical for mitochondrial fission . The C452F mutation is in a highly conserved region of the M domain of Dnm1l that alters protein interactions in a yeast two-hybrid system , suggesting that the mutation might alter intramolecular interactions within the Dnm1l monomer . Heterozygous Python fibroblasts exhibit abnormal mitochondria and peroxisomes . Homozygosity for the mutation results in the death of embryos midway though gestation . Heterozygous Python hearts show reduced levels of mitochondria enzyme complexes and suffer from cardiac ATP depletion . The resulting energy deficiency may contribute to cardiomyopathy . This is the first demonstration that a defect in a gene involved in mitochondrial remodelling can result in cardiomyopathy , showing that the function of this gene is needed for the maintenance of normal cellular function in a relatively tissue-specific manner . This disease model attests to the importance of mitochondrial remodelling in the heart; similar defects might underlie human heart muscle disease .
Idiopathic dilated cardiomyopathy ( DCM ) is characterised by unexplained left ventricular ( LV ) cavity enlargement with LV systolic impairment [1] . DCM is an important cause of congestive heart failure ( CHF ) with a prevalence of 36 cases per 100 , 000 in the United States [2] . Although the molecular pathways responsible for DCM remain largely unknown , it is estimated that between 20–50% of DCM cases are familial in nature , the large majority being inherited as an autosomal dominant trait [3] . Accordingly , the study of single gene disorders that remodel the heart to cause DCM may provide a valuable opportunity to identify critical molecules involved in disease pathways [4] . Over the past decade , DCM-causing mutations have been identified in genes encoding sarcomeric , cytoskeletal , nuclear envelope , intermediary filament , sarcoplasmic reticulum and desmosomal proteins . These findings have implicated pathogenic mechanisms whereby structural integrity , contractile force dynamics , and calcium regulation within the cardiomyocyte are perturbed . Yet such mutations only account for a minority of DCM cases [5] and many of the genes underlying DCM remain to be elucidated . A number of gene knockouts in the mouse produce features of DCM , but these phenotypes are usually recessive and so do not model the human disease . One approach that has been applied successfully to the characterisation of new disease alleles is the use of N-ethyl-N-nitrosourea ( ENU ) mutagenesis [6] . Treatment of mice with ENU results in a high frequency of predominantly single point mutations in the mouse germline that recapitulate the spectrum of mutations observed in many human genetic diseases . Screening of offspring reveals phenotypic variants , and the identification of the mutations underlying the abnormal phenotype can reveal new genetic regulators and novel pathways associated with disease pathogenesis . Such an approach is ‘hypothesis neutral’ , making no prior assumptions about the nature of the genes or pathways involved . Using this approach we describe a novel mouse model of DCM in which a mutation in the Dynamin-1-like gene ( Dnm1l ) leads to autosomal dominant DCM and congestive heart failure .
The Python mouse was identified on the basis of rapid size increase , piloerection , and shallow rapid breathing in a visual screen of adult G1 offspring of ENU-mutagenized BALB/cAnNCrl males crossed with C3H/HeH females ( Figure 1A ) . The phenotype was inherited in an autosomal dominant fashion with complete penetrance in both sexes . The median age of onset of overt symptoms of CHF on a C3H/HeN genetic background was 91 days for females and 83 days for males . A similar phenotype with much later onset occurred on a C57BL/6J background ( median age of onset = 164 days for females , 171 days for males; Figure 1B ) suggesting that strain-specific genetic modifiers influence disease onset . The hearts of Python mice were grossly dilated by the time of overt CHF ( Figure 1C ) . The Python hearts exhibited both biatrial and biventricular thinning and dilatation consistent with DCM ( Figure 1D ) . The visible increase in size of Python mice was the result of substantial ascites and subcutaneous oedema ( Figure 1E ) accompanied on occasion by pleural effusion . In the heart , myocyte hypertrophy and interstitial fibrosis ( Figure 1F ) was evident . Morphometric analysis of MSB-stained sections revealed that collagenous tissue increased by almost 7-fold in hearts of terminal Python mice ( Figure 1G ) . Prominent cardiac calcification was also evident ( Figure 1H ) . Python mice developed CHF under specified pathogen-free ( SPF ) conditions , where infectious agents capable of causing myocarditis were absent . There was no microscopic evidence of myocarditis , coronary artery disease or amyloidosis , nor was there hypertrophy of pulmonary blood vessels that would indicate pulmonary hypertension . MRI analysis of embryonic hearts did not reveal any obvious anatomical abnormalities ( data not shown ) . TUNEL staining did not reveal accelerated apoptosis in late-stage Python hearts ( data not shown ) . We did observe hepatic congestion at the time of overt CHF , preceded by increases in the plasma levels of liver enzymes aspartate aminotransferase and alanine aminotransferase ( data not shown ) , possibly reflecting congestive cardiac hepatopathy secondary to heart failure . Cardiac function was analysed in male Python mice on the C3H/HeN background aged 71–78 days , i . e . approximately 2 weeks before overt clinical signs of CHF become evident . The results are summarised in Table 1 . Heart rates in these conscious mice did not significantly differ between Python mice and controls , and there were no differences in any measurement of ECG interval duration ( data not shown ) . However , LV catheterisation revealed that pressure generation was severely impaired in Python mice , with LV end-systolic pressure 22 mmHg lower than in littermate controls , and contractility 40% lower , as measured by dP/dtmax . This reduction was not due to differences in loading conditions since dP/dtmax remained impaired after normalisation to instantaneous pressure . Furthermore , the Python mice had significantly elevated end-diastolic pressure commensurate with impaired contractile function . Under conditions of maximal b-adrenergic stimulation with dobutamine , Python mice had a reduced contractile reserve and a severely impaired maximum contractility . Consistent with these findings , aortic blood pressures were significantly reduced . Relaxation was also impaired in Python mice with dP/dtmin 46% lower than controls , and significant prolongation of the isovolumetric constant of relaxation ( Tau ) , which is less sensitive to loading conditions . These changes occurred in the absence of LV hypertrophy or dilatation at this time point and were confirmed at post-mortem by no differences in LV or RV weights . Although at this stage lung weights were normal , indicating the absence of significant pulmonary congestion , later , at the time of appearance of overt CHF ( approximately 2 weeks later ) gross ascites and pulmonary congestion become rapidly manifest , consistent with the precipitous deterioration in LV function . No thrombi were observed in the atria , nor , indeed , in any heart chamber ( data not shown ) . Finally , haematocrits were normal in all mice ( data not shown ) , ruling out a low red blood cell count as a mechanism for LV dysfunction . The Python mutation was generated on a BALB/cAnN genetic background , and the original mouse exhibiting the CHF phenotype was a BALB/cAnNCrl x C3H/HeH F1 hybrid male . This individual was backcrossed to C3H/HeH and offspring exhibiting the CHF phenotype were examined with a panel of 53 polymorphic SNP markers spanning the entire genome at regular intervals . A strong linkage signal was observed on chromosome 16 at marker D8Mit213 ( LOD score of 3 . 3 for a recombination fraction = 0 . 1 ) . Subsequent fine mapping by backcrossing to C3H/HeH , C3H/HeN and , finally , to C57BL/6J mice narrowed the critical region containing the mutation to 787 Kb ( Figure 2A ) . Three genes are located within this region - Pkp2 , Fdg4 and Dnm1l . Sequencing of all exons and exon-intron boundaries identified only a single mutation in Python mice; a G/C to T/A transversion in exon 11 of the Dnm1l gene ( the official gene name as specified by the International Committee on Standardized Genetic Nomenclature for Mice but often referred to in the literature as Drp1 or Dlp1 ) ( Figure 2B ) . Based on the known ENU-induced mutation rate for this strain and ENU dose ( 1 mutation per 1 . 8 Mb ) [7] , the probability of there being an additional intronic or intergenic mutation anywhere in this region is extremely low ( P = 0 . 00001 ) [8] . There was complete concordance between animals suffering from CHF and the Python mutation . We retrospectively regenotyped 145 DNA samples isolated from C3H and C57BL/6J Python mice that had suffered from CHF and all contained the mutation while it was absent in 189 samples from non-affected littermate control mice . None of the wild types strain examined ( BALB/cAnNCrl , C3H/HeN , C3H/HeH , C57BL/6J , DBA/2J , CBA/J , 101/H , 129/S5 ) contained the mutation . Although PKP2 dominant mutations have been associated with arrhythmogenic right ventricular cardiomyopathy in humans [9] an intronic or intergenic mutation is unlikely to account for the Python phenotype as all human PKP2 mutations found to date occur in the coding region or splice sites which are not mutated in Python mice , the homozygous phenotype for the null Pkp2 mutation is very different from the Python homozygote [10] and Pkp2 mRNA level is not altered in Python hearts as judged by microarray analysis ( data not shown ) suggesting no disease-causing non-coding regulatory changes . These facts , coupled with the observation that ENU-induced mutations resulting in detectable phenotypes occur almost exclusively in the coding exons or exon-intron boundaries of genes [11] , strongly suggested that this base change was the Python mutation . The Python mutation results in the replacement of the cysteine by a phenylalanine at position 452 in the predicted Dnm1l protein ( amino acid numbering according to EBI reference protein Accession No . Q8K1M6 ) ( Figure 2C ) . This cysteine is located within the middle ( M ) domain of the protein and is fully conserved in all Dnm1l orthologues , and even the yeast dynamin homologue DNM1 ( Figure 2D ) . The degree of evolutionary conservation of the Dnm1l protein is very high . For example , overall homology between human and mouse Dnm1l is 98% , and between zebrafish and mouse is 89% . The M domain conservation is even higher with 96% sequence conservation between mouse and zebrafish over the 291 amino acids of this domain . The cysteine residue is also conserved in the M domain of the mouse homologues of Dnm1 , Dnm2 and Dnm3 ( Figure 2E ) despite overall homology with these domains being less than 40% ( Table S1 ) suggesting that this cysteine plays an important role in M domain function . There is no available crystal structure of any mammalian dynamin proteins but a crystal structure has been described for a bacterial dynamin-like protein . In this structure the M domain forms an elongated alpha-helical domain where the tip of the M domain helices interact with a similar region of the ‘mate’ in the dynamin homodimer [12] . Accordingly , a model of mouse Dnm1l was constructed on the basis of comparative sequence homology to the bacterial dynamin-like protein BDLP for which there is a crystal structure [12] and an electron cryomicroscopy reconstruction of BDLP assembled around a lipid tube [13] . A predicted structure could be created for most of the protein , apart from one region where there is no homology in BDLP ( indicated by an ‘a’ in Figure 3A ) . The predicted structure of the dimeric asymmetric repeating unit in the extended confirmation ( i . e . after lipid binding ) is shown in Figure 3A . There are six mutations , all dominant or semi-dominant , that have been reported in the M of domain of DNM1L or its yeast homologue DNM1–three in yeast [14] , and one each in a human patient [15] , a CHO cell line [16] , and Python ( Figure S1 ) . These were mapped on to the predicted structure ( Figure 3A ) . The Python mutation is located in an alpha-helix that is not predicted to affect interaction between Dnm1l monomers . However , it is located close to several other helical regions of the domain M . Furthermore , a helix-wheel projection of the region around the Python mutation-containing region predicts that one face of the predicted helix contains principally hydrophobic residues ( Figure 3B ) . Taken together , these findings are suggestive of this face being involved in an intramolecular interaction within the Dnm1l monomer . To test this further , we used the yeast two-hybrid assay based on GAL4 DNA binding and activation domain interactions to examine whether interactions between regions of Dnm1l could be altered by the Python mutation . We used regions of the protein that have been used by others in similar assays [17]–[22] and examined all possible reciprocal interactions of bait ( in pDEST32 ) and prey ( in pDEST22 ) proteins for regions of Dnm1l: full-length , N-terminal region , C-terminal region , M domain and GED ( GTPase Effector Domain ) ( Figure 3C ) . On the basis of ability to grow on medium lacking histidine and b-galactosidase activity , the only strong interactions we identified were interactions between the full-length proteins , the N terminal and C-terminal regions of the proteins as reported by Zhu et al . [20] , and the N-terminal region and the GED ( Figure 3D ) . We found that while the Python mutation had negligible influence on the ability of the full-length proteins to interact ( Figure 3D and 3E ) , it abrogated the ability of the N-terminal region to interact with both the C-terminal region and the GED alone ( Figure 3D ) . Quantitative assays for b-galactosidase activity confirmed the substantial effect the Python mutation had on these interactions ( Figure 3E ) . Given that the Python mutation occurs in a highly conserved domain of the Dnm1l protein and alters protein interaction in vitro , its effect on in vivo functions associated with Dnm1l were examined . Protein levels of Dnm1l were not altered in either heart or brain ( Figure 4A ) suggesting that there is no haploinsufficiency ( i . e . the Python protein is assumed to be present ) . Dnm1l was distributed diffusely within the cell in both Python and wild type cultured skin fibroblasts ( Figure 4B ) suggesting that introduction of the Python protein did not drastically alter trafficking of Dnm1l . As Dnm1l function is involved in mitochondrial and peroxisomal dynamics , cultured neonatal skin fibroblasts were examined for morphology of both these organelles . Mitochondrial morphology was altered . Python mitochondria were highly elongated compared to wild type controls ( Figure 4C ) , as were peroxisomes ( Figure 4D ) . To determine if mitochondrial volume was altered , we utilized a novel assay where cells were loaded with a fluorescent mitochondrial marker and analyzed by flow cytometry . Intensity of fluorescence should reflect mitochondrial volume in the cell . As shown in Figure 4E , there was no difference in overall mitochondrial volume between Python and wild type fibroblasts , despite the significant changes in mitochondrial shape . This indicates that the Python mutation affects the in vivo functional activity of Dnm1l , thereby impairing mitochondrial fission . To discern the functional effects of the Python mutation , we intercrossed heterozygotes to obtain homozygous animals . Genotyping of embryos demonstrated that no homozygous Python embryos could be recovered from E12 . 5 onwards ( Figure 5A ) . Homozygous embryos appear to be normal up to approximately E9 . 5 . At E11 . 5 , Py/Py embryos were severely retarded in growth and exhibited a posterior truncation ( Figure 5B ) . The homozygous embryonic phenotype is very similar to that recently reported for the Dnm1l-null mutation [23]–[24] . Embryos die at a similar stage and their morphology is similar . Mouse Embryonic Fibroblasts ( MEFs ) cultured from homozygous E9 . 5 embryos survived poorly in culture . Few cells attached and there was no proliferation ( Figure 5C ) . Mitochondria of Py/+ MEFs were abnormal with numerous long tubular mitochondria ( Figure 5C ) , similar to Py/+ skin fibroblasts . In contrast , homozygous Python MEFs had grossly abnormal mitochondria . Whereas a tubular mitochondrial network evenly distributed throughout the cytoplasm characterized mitochondria in +/+ and Py/+ MEFs , some mitochondria of Py/Py MEFs appeared to be spherical and aggregated ( Figure 5C ) . This could reflect Dnm1l dysfunction alone though it may reflect a general dysfunction of these cells . As mentioned earlier , these cells fail to proliferate . Over several weeks in culture they slowly die . The nuclei staining with Hoechst 33342 demonstrated evidence of chromatin condensation in homozygous Python cells ( Figure 5C ) , suggesting the cells could be dying by necrosis . The observation that Python fibroblasts exhibit abnormal mitochondria coupled with the well-recognized role of DNM1L in mitochondrial fission [25] and the critical role of mitochondria in both the normal function and the death of cardiomyocytes [26] , led us to examine the mitochondrial and energetic phenotype of Python heterozygous mice in greater detail . Aside from the development of CHF , the Python mice did not exhibit any features prominently recognized in mitochondrial cytopathies , such as metabolic , neurological and skeletal muscle defects . They exhibited a normal general behavioral and functional profile as defined by the SHIRPA series of tests ( Table S2 ) [27] , which would reveal any major neurological abnormalities . Grip strength ( a reflection of muscle strength ) was normal , as was muscle histology as assessed from H&E-stained sections ( data not shown ) . Plasma lactate levels , an indicator of general metabolic dysfunction , were not elevated in Python mice aged 5–9 weeks compared to wild type littermate controls ( Figure 6A ) . Given that the Python phenotype at a gross pathological level appeared to be restricted to the heart , we examined the mitochondrial phenotype of Python cardiomyocytes . There was little evidence of morphological change in Python cardiomyocyte mitochondria . For example , we examined the nuclear:mitochondrial DNA ratio in Python hearts as mtDNA is lost when DNM1L is down-regulated and mitochondrial fission impaired [28] , However , there was no alteration in the nuclear:mitochondrial DNA ratio in Python hearts at any stage before the development of overt CHF and even at this stage only some Python hearts showed an increased ratio ( Figure 6B ) . This suggests that a major derangement of nuclear:mitochondrial DNA ratios was not a generalized effect of the Python mutation . There was evidence of mitochondrial function changes in hearts of Python mice suffering from overt CHF . Examination by enzyme immunohistochemistry for succinate dehydrogenase ( SDH ) and cytochrome c oxidase ( Complex IV ) activities revealed a reduction for both enzymes in Python hearts ( Figure 6C ) . In the case of SDH there is evidence of diminished myocellular enzyme activity as indicated by less-intense staining . However for Complex IV , the enzyme immunohistochemistry suggests that in the failing heart , the change in Complex IV enzyme levels may be due to the substantial fibrosis that occurs late in the disease process rather than a change in enzyme activity . In vitro measurement of Complex IV activity and quantity showed that both were proportionately reduced by similar amounts in ailing Python hearts ( Figure 6D ) , indicating that the enzyme remains fully active but is less abundant at 12 weeks of age . This reduction in overall levels at this age most likely reflects the considerable fibrosis and loss of cells that occurs in late-stage Python hearts . Defects in mitochondrial enzyme activity are recognized as a general phenomenon in CHF [29] and , therefore , the differences observed in failing Python hearts might be secondary to the primary cause of heart failure . To determine if the Python mutation was affecting mitochondrial function prior to major changes in heart structure , we examined heart samples from Python and wild type littermates at 10 weeks of age . This is before there are any overt signs of CHF though the cardiovascular data above ( Table 1 ) indicates that heart function is abnormal at this stage . Electron micrographs of heart samples were examined . Morphometric analysis of the proportional area occupied by mitochondria ( a reflection of overall volume per cell ) revealed no difference between Python hearts and controls ( Figure 6E ) . This in agreement with the flow cytometry findings in Python skin fibroblasts ( Figure 4E ) . Nor were myofibre widths significantly different between Python hearts and controls ( data not shown ) . There was no evidence of the membrane pinching reported in the Dnm1l-null mouse fibroblasts [Ishihara2009] , nor was there evidence of large aggregates as has been reported for in vitro cultured DNM1L mutants [30] . However , the average size of a mitochondrion was slightly smaller in Python hearts than wild type hearts ( Figure 6E ) . Smaller cardiomyocyte mitochondria have been previously reported in some cases of heart failure [31]–[33] . In the case of Python , if mitochondrial volume is not altered but mitochondrial tubules are extended in length , then a smaller cross-sectional transverse area of mitochondria would be consistent with this . If Dnm1l affects mitochondrial dynamics , we predicted that the end point of this would be impairment of respiratory chain function . Examination of mitochondrial enzyme complex activities normalized to citrate synthase activity in heart samples from 10-week-old mice revealed no differences ( Figure 6F and 6G ) . There was a slight reduction in the overall level of mitochondrial citrate synthase activity in Python hearts at this age but it was not statistically significant ( Student's t test , P = 0 . 11 ) ( Figure 6G ) also suggesting that the total mitochondrial volume in Python hearts was not reduced . As the end-point of respiratory chain function is ATP synthesis , and given that down-regulation of DNM1L results in a reduced rate of ATP synthesis [28] , [34] , myocardial ATP and total adenine nucleotide ( TAN ) levels were measured using HPLC . Python hearts exhibited a dramatic , approximately 50% , reduction in ATP and TAN levels ( Figure 6H and 6I ) compared to hearts from littermate controls . In liver and brain at the same age , ATP and TAN levels were similar in Python mice and controls ( Figure 6H and 6I ) , indicating that the defect in ATP generation was not a general one . These results were confirmed by using complementary quantitative bioluminescence assays ( Figure S2 ) . A range of metabolites in the heart was examined using high-resolution 1H NMR spectroscopy and Gas Chromatography Mass Spectrometry ( GC-MS ) -based metabolomic approaches [35] on Python and control mice . The metabolite changes identified are summarized in Table 2 . Significant reductions in Python hearts were noted in mitochondrial metabolic intermediates or accessory molecules e . g . succinate , malate , fumarate , pyruvate ( all associated with the citric acid cycle ) , creatine , glucose , AMP and adenosine . Two notable increases were in the amino acids glycine and proline . These account for approximately 50% of the amino acids in collagen , possibly reflecting the fibrosis in Python hearts .
We report the identification , through ENU mutagenesis , of a novel genetic cause of cardiomyopathy . Our principal finding is that a missense mutation in the middle domain of Dnm1l , whose product is critically involved in mitochondrial fission , results in DCM . The resulting defect in mitochondrial remodelling renders the Python hearts progressively energy deficient potentially contributing to the phenotype [36] . The fundamental importance of mitochondrial remodelling in mammalian pathophysiology has been underlined by in utero lethality and cerebellar degeneration in mice with homozygous mutations in Dnm1l itself , as well as Mfn1 , Mfn2 , or Opa1 [23] , [37] , [38] . Given the heart's manifest dependency on mitochondria [39] as evidenced by its frequent involvement in mitochondrial disorders [40] , mitochondrial remodelling defects might be expected to occur in some forms of myocardial disease . Recently it has been reported that mitochondria are smaller in failing hearts and DNM1L protein levels were increased in DCM heart samples [33] but until now , there has been no direct evidence that genes involved in regulating mitochondrial dynamics might be involved in heart failure . Python is the first such example . DNM1L is a member of the dynamin superfamily . In the higher order spirals formed by dynamin , the basic repeating unit appears to be a dimer [41] . The association between the GED and M domains forms a ‘stalk’ conformation through which strong inter-molecular interactions of the dimers occur [41] . Mutations in the M domain have accordingly been shown to adversely affect self-assembly into a higher order oligomeric structures that are critical to Dnm1l function [22] and conformational changes within this region are associated with the constriction of dynamin tubes that facilitates fission [42] . It is not difficult to envisage how the substitution of a bulky hydrophobic phenylalanine residue into a helix-rich region of the M domain ( Figure 3A ) has the potential to modify interactions necessary for Dnm1l's effective function . As a corollary , our yeast two hybrid analysis showed Python's capacity to abrogate Dnm1l's N-terminal region interacting with the C-terminal region ( and GED ) , while leaving the interaction between Dnm1l monomers unaffected . A similar effect was reported when the S637D mutation was introduced into Dnm1l [43] . This effect of the mutation on Dnm1l higher order structure is consistent with a dominant-negative mode of action . Since there appears to be no change in overall Dnm1l protein levels , we propose that the Python monomer is readily incorporated into dimers with the wild type protein but fails to function effectively within that dimer due to defective intramolecular interactions . If this model is accurate , only 1 in 4 Dnm1l dimers might be expected to be fully functional . This study cannot exclude a systemic ( i . e . non-cardiac ) impact of the C452F mutation , though from a clinical perspective , most tissues and organ functions appeared to be grossly spared . The liver was the one organ that did show evidence of extra-cardiac involvement but this might reflect congestive cardiac hepatopathy caused by heart disease . The predominant cardiac phenotype in the Python heterozygotes contrasts with the marked skeletal muscle and metabolic abnormalities in the infant with a dominant mutation in DNM1L [15] . It is unclear why the Python mutation manifests an overt phenotype only in heart . The reported human mutation in domain M , a dominantly acting alanine to aspartic acid mutation , appears to be much more severe than the Python mutation appeared to have a widespread metabolic defect . This may be related to the location of the amino acid within the structure and/or the chemical nature of the amino acid substitutions involved . An inter-species difference in DNM1l functionality is possible but unlikely given the extremely high degree of inter-species conservation . All 7 mutations identified in the M domain that affect mitochondrial dynamics are conserved not only in the M domain of DNM1L orthologues across multiple species ( Figure S1 ) , but are also conserved across the M domain of DNM1 , DNM2 , DNM3 and DNM1L ( Figure 2E ) . The human alanine to aspartate mutation occurs in a region of the protein that might interact with the membrane ( Figure 3A ) . When overall compatibility of the amino acid replacements are compared the Python amino acid replacement scores significantly higher in compatibility with its original amino acid in terms of hydrophobicity , size and charge compatibility ( 15 . 8 , 9 . 4 , 18 . 17 for the Python mutation compared to 12 . 8 , 8 . 6 and 12 . 82 for the A400D human DNM1L mutation ) [44] . Given that there is no evidence that this cysteine is involved in disulphide bonding in DNM1L , and its effect in the yeast two-hybrid system on intramolecular but not intermolecular Dnm1l interactions , its effect on Dnm1l function may be relative mild , still enabling the formation of dimers but with reduced functionality . Although the gross impact of the heterozygous C452F Dnm1l mutation on cardiac morphology was relatively subtle at ∼70 days , the corresponding functional and , even more so , the biochemical phenotypes were apparent . This was reflected in uniform lethality shortly thereafter . The degree of ATP deficiency in the Python hearts at this age is , to the best of our knowledge , unprecedented . The apparent fall of ATP levels to 44% of wild type levels in hearts from 10-week-old mice ( 15 . 1 vs . 6 . 6 nmol ATP/mg/protein in +/+ and Py/+ hearts at 10 weeks of age confirmed by two independent means ) may have been exaggerated by increased fibrosis , yet still seems beyond the range by which it is thought that cardiac [ATP] is allowed to diminish ( ∼25% to 30% ) even in CHF [39] . We used an exacting method involving HPLC to measure these levels to ensure they were as accurate as possible . However a major caveat to these observations , is that the degree of difference in ATP levels observed herein will inevitably be exaggerated by the technical limitations associated with measuring ATP levels in tissue samples after excision from the body . The resulting rapid depletion of ATP pools may have precluded accurate measurements . However , a difference in ATP levels between Python and control hearts ( that is not found in liver and brains ) may indicate that the Python mutation does espouse an aberrant and progressive impact on myocardial energetics . One possible explanation for the Dnm1l-mediated cardiomyopathy is cardiac energy deficiency . It is recognized that CHF is associated with , and in many cases exacerbated by , cardiac energy deficiency [36] , [39] , [45] , [46] . Indeed , the commonality of CHF to numerous primary mitochondrial diseases represents an excellent source of evidence that primary energy deficiency can and does cause CHF [36] , [47] . As reduction of Dnm1l function is known to impair cellular energetics [28] , [34] and as this pattern of cellular energetic impairment is profoundly and progressively manifested in Python hearts even in advance of gross cardiac dysfunction , cardiac energy deficiency may contribute to the phenotype of Python hearts and may be the proximate cause . Detailed questions remain regarding the impact of the C452F substitution . Further biochemical and cellular studies are needed to investigate the hypothesis that such M domain mutations alter the intra/inter-molecular interactions and alter the homo-oligomerisation properties of Dnm1l [48] . Such studies will have to explain why in the context of the subtle reticular mitochondrial changes in Python , there is a discrepancy between the lack of an overall decrease in mitochondrial volume or function and the degree of ATP depletion . There are a number of other metabolic and cellular processes that could be altered by the Python mutation . Calcium cycling , for example , is altered in cells lacking the mitochondrial fission protein Mitofusin 2 , reflecting its role in endoplasmic reticulum ( ER ) -mitochondria tethering [49] . Inhibition of Dnm1l alters ER structure [50] , though we could find no alteration in the morphological appearance of the ER in Python fibroblasts ( data not shown ) . Nevertheless , further investigation of Ca2+ uptake into mitochondria and release from ER is warranted . An energy defect not reflected in altered respiratory complex enzyme activity might also result from other disturbances such as uncoupling of electron transport and ATP production , perturbation of supercomplexes [51] , cell cycling [52] organelle quality control through autophagy , or generation of reactive oxygen species [28] . It is possible that Python has effects on Dnm1l function , unrelated to mitochondrial dynamics . The tissue specificity of the Python defect warrants further investigation . This may reflect unique properties of cardiomyocytes , their mitochondria , or a unique role for Dnm1l in cardiomyocytes . For example , a role for DNM1L in the heart has been inferred by Ong et al . who observed that mitochondrial fission protects the heart against ischemia/reperfusion [53] . One strategy to address this question would be to effect conditional inactivation of Dnm1l in cardiomyocytes , in a similar manner to mitochondrial fusion factor elimination in skeletal muscle [54] . In conclusion , we report the first model of mitochondrial remodeling to be associated with cardiomyopathy . It is likely that the C452F substitution in the M domain of Dnm1l alters the balance of mitochondrial fission and fusion . The impairments in mitochondrial remodeling and function result in a relatively tissue specific disease as manifested by rapidly progressive cardiomyopathy . It is plausible that mutations in Dnm1l that are similarly subtle and hence do not represent a barrier to viability , will be identified and prove to be of importance in human disease .
Mice were maintained in high health status facilities with access to food and water ad libidum . All work was approved by the Animal Ethical Review Committees of MRC Harwell , University of Sheffield and University of Leeds , and the UK Home Office , and conducted with the highest quality of animal care and in accordance with the 3Rs . Adult BALB/cAnNCrl mice aged 10 weeks were mutagenized by intraperitoneal injection of two weekly doses of 100 mg/kg ENU , mated with C3H/HeH and F1 offspring screened for abnormalities . Python mice were further backcrossed to C3H/HeN and C57BL/6J . All tissue samples used for metabolic and cellular analysis were derived from a line generated by inbreeding of an N1l backcross to C3H/HeN . Genome-wide low-resolution mapping was performed using DNA samples from 15 N2 C3H/HeH backcross animals that were identified as carriers based on the development of congestive heart failure . Genomic DNA samples isolated from tail biopsies of these animals were screened by PCR amplification and gel electrophoresis with 53 microsatellite markers spaced at regular intervals across the genome . Samples were genotyped as either homozygous C3H or heterozygous BALB/c-C3H for each marker . For finer mapping , crosses of Python mice with both C3H and C57BL/6J were used , and further microsatellite markers polymorphic between either BALB/c and C3H or BALB/c and C57BL/6J were used to identify Python mice that were recombinant in the critical region . Single nucleotide polymorphisms ( SNPs ) were genotyped by sequencing of PCR products amplified with primers flanking the SNP site . Sequencing of candidate genes involved designing primer pairs to amplify individual exons as well as flanking splice donor/acceptor sequences . All exons and splice sites in the critical region were sequenced using homozygous as well as heterozygous Python DNA , ensuring that no base changes were missed . A small drop of blood was obtained from the lateral tail vein and processed using a Lactate Pro analyzer ( HabDirect , Southam , UK ) . Tissues for light microscopy were emersion fixed in 10% neutral buffered formalin and wax-embedded 3 µm sections were stained with haematoxylin and eosin or Mauritius Scarlet Blue ( MSB ) . Mice were perfusion fixed for TEM . In situ staining for succinate dehydrogenase and Complex IV activities were a previously described [47] , [55] , [56] . Conscious ECG measurements were obtained in unrestrained male Python mice and littermate controls ( n = 6 of each ) at 10 weeks of age using the non-invasive AnonyMOUSE ECG screening tool ( Mouse Specifics Inc ) . One week after , at 75±2 days of age , the same mice were anaesthetised with isoflurane and placed on a homeothermic blanket . Parasternal short- and long- axis views were obtained under 1 . 25% isoflurane anaesthesia using an Agilent Sonos 5500 with 15 MHz transducer . The LV was cannulated via the right carotid artery with a 1 . 4F Mikro-tip conductance cannula ( SPR-839 , Millar Instruments ) . The superior vena cava was cannulated with a second 1 . 4F Millar cannula ( SPR-671 ) to measure central venous pressure . Mice were allowed at least 15 minutes equilibration before baseline aortic and ventricular pressure measurements were obtained . Dobutamine was given by intraperitoneal injection ( 1 . 5 µg/g body weight ) and pressure measurements obtained under maximal b-adrenergic stimulation . Mice were then killed by cervical dislocation and organs washed in heparinised saline , blotted and weighed . Two experiments were performed daily , alternating between genotypes for morning and afternoon experiments . All activities were determined at 30°C . Prior to analysis cells were subjected to three cycles of freezing and thawing to lyse membranes . Enzyme activities were assessed were assessed using a Uvikon 940 spectrophotometer ( Kontron Instruments Ltd , Watford , UK ) . Complex I activity was measured according to the method of Ragan et al . [57] . Complex II-III activity was measured according to the method of King [58] . Complex IV activity was measured according to the method of Wharton and Tzagoloff [59] . Citrate synthase ( CS; EC 1 . 1 . 1 . 27 ) activity was determined by the method of Shepherd and Garland [60] . Enzyme activities were expressed as a ratio to citrate synthase to compensate for mitochondrial enrichment in cell samples [61] . Measurement of Complex IV activity and quantity in terminal heart samples used a Complex IV Mouse Duplexing ( Activity + Quantity ) Microplate Assay Kit ( Mitosciences , Eugene , OR ) according to the manufacturer's instructions . Tissues were extracted using a methanol: chloroform: water extraction procedure to separate aqueous soluble metabolites from lipids as previously described [62] . Briefly , ∼100 mg tissue were pulverised with dry ice . 600 µl methanol: chloroform ( 2∶1 ) was added and the samples were sonicated for 15 min . Water and chloroform were added ( 200 µl of each ) . The resulting aqueous and organic layers were separated from the protein pellet . The organic layer was dried overnight in a fume hood whilst the aqueous extracts were evaporated to dryness using an evacuated centrifuge ( Eppendorf , Hamburg , Germany ) . 100 , 000 cells were resuspended in 100 µl extraction solution ( 0 . 2 mg/ml proteinase K , 0 . 2% SDS and 5 mM EDTA in PBS ) and incubated at 50°C for 3 h . Total DNA was then precipitated by addition of 10 µl of 3 M sodium acetate ( pH 5 . 2 ) , 110 µl isopropanol and incubation for 20 minutes on ice before centrifugation at 12 , 000 rpm at 4°C . The DNA-pellet was washed once with cold 70% ethanol , air dried for 15 min and resuspended in 100 µl TE buffer at 4°C overnight . Realtime PCR amplification was performed on 10 ng of total DNA using a iCycler ( Bio Rad ) and iQ SYBR Green Supermix ( BioRad ) following the manufacturer's instructions . A 211 bp fragment of the mtDNA 12S RNA gene was amplified between nucleotide 1095 and nucleotide 1305 ( Forward primer: 5′ GCTCGCCAGAACACTACGAG 3′ , reverse primer: 5′ CAGGGTTTGCTGAAGATGGCG 3′ ) . Elongation translation factor 1 gene ( EEF1A1 ) was used as an endogenous reference across all experimental conditions ( Forward primer: 5′ GGATTGCCACACGGCTCACATT 3′ , reverse primer: 5′ GGTGGATAGTCTGAGAAGCTCTC 3′ ) . Regions of Dnm1l were amplified from a mouse IMAGE cDNA clone plasmid and cloned into the vectors pDEST22 ( prey ) and pDEST32 ( bait ) ( Invitrogen Ltd . , Paisley , UK ) according to the manufacturer's instructions . The two hybrid tests were performed in the yeast strain MaV203 , which contains a HIS3 promoter driving expression of HIS3 and a GAL1 promoter to drive expression of LACZ as chromosomally integrated reporter genes . Interactions were initially tested by plating on yeast dropout medium agar without leucine , tryptophan and histidine and with increasing amounts of 3-amino-1 , 2 , 4-triazole ( 10 , 25 , 50 and 100 mM ) . Interactions were then further tested using a semi-quantitative b-glactosidase filter lift assay followed by a quantitative b-galactosidase liquid culture assay according to the Pro-Quest instruction manual ( Invitrogen Ltd . , Paisley , UK ) . For skin fibroblasts , 2–4-day-old pups were humanely culled , and a portion of the skin removed , washed in PBS and finely minced using a razor blade . A small piece of tissue was retained for genotyping purposes . Numerous small segments of tissue approximately 1 mm3 were placed well spaced on 10 cm Petri dishes and allowed to air dry for 10 minutes , DMEM containing Glutamax and 10% FCS ( Invitrogen Ltd . , Paisley , UK ) was then carefully added to the dish so as to avoid dislodging the tissue pieces . Plates were cultured for 7 days at 37°C , 5% CO2 , then outgrowing cells were harvested with trypsin and passaged in the same medium . For embryonic fibroblasts , embryos were dissected from the uterus and the yolk sacs were removed for use in genotyping the embryos . The embryos were placed in 50 µl 0 . 05% trypsin and macerated using a 20 µl disposable pipette tip . After incubation for 15 minutes at 37°C , the cells were counted and plated in one well of a 6-well plate in DMEM containing Glutamax and 10% FCS and cultured at 37°C , 5% CO2 . For microscopy , fibroblasts were cultured on glass coverslips in 50–200 nM Mitotracker Orange CM-H2TMRos in DMEM containing Glutamax and 10% FCS for 45 minutes at 37°C . Cells were washed in PBS , then fixed in 4% paraformaldehyde for 20 minutes at room temperature . Cover slips were mounted on glass slides in a drop of ProLong Gold mounting medium ( Invitrogen Ltd . , Paisley , UK ) containing 10 µg/mL Hoechst 33258 . For FACS analysis , cells were harvested by trypsinization , washed with PBS , then fixed for 20 minutes at 4°C in 4% paraformaldehyde . Cells were subsequently centrifuged and resuspended in PBS . Brain and heart samples from 5-week-old mice were snap frozen in liquid nitrogen then homogenized in RIPA ( 50 mM Tris-HCl pH 8 , 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS ) containing 1× protease inhibitor cocktail ( P8340; Sigma-Aldrich , Dorset , UK ) . Equal amounts of protein were separated by Laemmli stacking/separating SDS PAGE and blotted to Hybond-P ( GE Healthcare , Little Chalfont , UK ) , then individual proteins were detected using antibodies to Drp1 ( Clone 8; BD Biosciences , Oxford , UK ) , Tim23 ( Clone 32; BD Biosciences , Oxford , UK ) , and a-tubulin ( T6074; Sigma-Aldrich , Dorset , UK ) . An anti-mouse HRP-conjugated antibody ( 7076; Cell Signaling , NEB , Hitchin , UK ) , was used for detection in combination with ECL Western Blotting Detection Reagents ( GE Healthcare , Little Chalfont , UK ) . For peroxisome labeling , a primary rabbit anti-catalase IgG ( ab16731 , Abcam ) was used with a goat anti-rabbit FITC-conjugated IgG ( ab6717 , Abcam ) . For examining Dnm1l distribution in fibroblasts , an anti-Drp1 primary antibody ( Clone 8; BD Biosciences , Oxford , UK ) with a Daylight549-conjugated anti-mouse Ig was used . All images were taken on a Zeiss AxioCam MRc5 microscope using Axiovision Release 4 . 7 . 2 software . For imaging mitochondria with Mitotracker Orange and Dnm1l using a Daylight549-labelled secondary antibody , a filter with excitation 530–585 nm , emission 600–660 nm was used . For Imaging peroxisomes with a FITC-labeled secondary antibody , a filter with an excitation of 450–490 nm , emission 510–560 nm was used . For FACS analysis , cells were incubated in 200 nM Mitotracker Orange CM-H2TMRos in DMEM containing Glutamax and 10% FCS for 45 minutes at 37°C , harvested using trypsin , washed with PBS and fixed by incubation in 4% paraformaldehyde for 20 minutes at 4oC . Cells were washed in PBS and resuspended in PBS containing 1% FCS and analyzed for fluorescence on a CyAnADP O2 flow cytometer ( Dako ) . The gate for Mitotracker Orange-positive cells was set using control cells that were not labelled . Hearts were excised , washed in heparinised normal saline , blotted and weighed , before being snap frozen in liquid nitrogen and stored at −80°C . Total adenine nucleotide ( TAN ) content and myocardial adenosine triphosphate ( ATP ) were measured by High Performance Liquid Chromatography ( HPLC ) as previously described [65] . For the purpose of calculating activity per mg of protein , the Lowry method was used . The structure of the human DNMlL protein ( Uniprot [66] accession number O00429 ) was predicted by homology modelling as follows . The structure of the bacterial dynamin-like protein BDLP [13] ( PDB [67] identification code 2w6d ) was used as template . Given the low sequence identity ( around 12% ) , the alignment between target sequence and template was performed using a profile-to-profile alignment method implemented in the FFAS03 server [68] ( http://ffas . ljcrf . edu ) . Profile-to-profile alignments are superior in terms of sensitivity and alignment quality compared to traditional pair wise alignments ( and in particular at low sequence identity ) . The FFAS03 score for the alignment was −33 . 5 implying significant similarity ( FFAS03 scores below −9 . 5 reported less than 3% false positive under benchmarking conditions [68] ) . The alignment was manually inspected and the structural model was derived using MODELLER [69] as previously described [70] . Structure representations were prepared using the molecular visualization program PyMOL ( http://www . pymol . org ) . DNA sequence alignments for detecting mutations were performed using DNASTAR ( DNASTAR Inc . Madison , WI ) . Protein alignments were performed using CLUSTALW2 and ALIGN software available online from the European Bioinformatics Institute ( www . ebi . ac . uk ) . Prism software ( GraphPad Software , Inc . , La Jolla , CA ) was used throughout for statistical analysis . Morphometric analysis used ImageJ [71] . For measurement of mitochondrial area and size , contrast of micrographs was enhanced by 0 . 5% . After setting the scale , the threshold was altered such that only mitochondria were marked . The thresholded image was then analyzed for area , and area fraction for particles greater than 50 pixels . For measurement of area stained in MSB sections , the image type was set to RGB stack and the threshold set to detect areas positive for collagen . The thresholded image was then analyzed for area above threshold . | Heart disease is very common . Some cases of heart disease are strongly influenced by lifestyle and diet , whereas others have a strong genetic component . A certain form of heart failure , known as dilated cardiomyopathy ( DCM ) quite often runs in families suggesting that a defective gene or genes underlie this disease . We describe a new mouse mutant called “Python” which suffers from a heart disease similar to DCM . We were able to pinpoint the defective gene responsible for the disease . This gene is normally involved in the division of mitochondria , the “power plants” of the cell that generate one of the main energy supplies for the cell . This is a unique model that implicates a new gene and mechanism of disease for further investigation . | [
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] | 2010 | A Mutation in the Mitochondrial Fission Gene Dnm1l Leads to Cardiomyopathy |
Adaptive divergence at the microgeographic scale has been generally disregarded because high gene flow is expected to disrupt local adaptation . Yet , growing number of studies reporting adaptive divergence at a small spatial scale highlight the importance of this process in evolutionary biology . To investigate the genetic basis of microgeographic local adaptation , we conducted a genome-wide scan among sets of continuously distributed populations of Arabidopsis halleri subsp . gemmifera that show altitudinal phenotypic divergence despite gene flow . Genomic comparisons were independently conducted in two distinct mountains where similar highland ecotypes are observed , presumably as a result of convergent evolution . Here , we established a de novo reference genome and employed an individual-based resequencing for a total of 56 individuals . Among 527 , 225 reliable SNP loci , we focused on those showing a unidirectional allele frequency shift across altitudes . Statistical tests on the screened genes showed that our microgeographic population genomic approach successfully retrieve genes with functional annotations that are in line with the known phenotypic and environmental differences between altitudes . Furthermore , comparison between the two distinct mountains enabled us to screen out those genes that are neutral or adaptive only in either mountain , and identify the genes involved in the convergent evolution . Our study demonstrates that the genomic comparison among a set of genetically connected populations , instead of the commonly-performed comparison between two isolated populations , can also offer an effective screening for the genetic basis of local adaptation .
Recent advances in next-generation sequencing ( NGS ) technologies have enabled a genome-scale analysis to infer the phylogenetic history , demography , and selection of natural populations . One of the intriguing challenges in ecological genomics is to identify the genes underlying local adaptation [1] . Although ecological genomics has been applied to various study systems , screening methods to detect the selected loci can be represented by two approaches: those that focus on the adaptive differentiation , and those that focus on the genotype-environment correlations . The former differentiation-based approach assumes neutral genetic drift to affect the entire genome , so that unusual differentiation at a particular locus should indicate a presence of selection . FST-based outlier tests are among the earliest and most common method to detect the selected loci [2] . The latter correlation-based approach compares a set of subpopulations at heterogeneous environments to detect the loci with correlation between allele frequency and environmental variables [3] . Availability of the genome-scale datasets have facilitated improvements in these two approaches , along with the development of other methods that employ indicators such as reduced heterozygosity , skews in site frequency spectrum , and extended linkage disequilibrium ( reviewed in [4] ) . Although ecological genomics have provided important insights into the genetic basis of local adaptation , each of the above mentioned approaches has drawbacks to its practical implementation , which includes false positive and false negative detection of the selected loci . For instance , FST-based outlier tests generally face problems in identifying the significant departure from neutral expectation . Without taking account the actual demographic history , outlier tests may suffer from false positives due to high variance in FST values among the neutral loci [5] . Within- and between-population structures can also increase the false positive rate of correlation-based approaches by creating spurious correlation between allele frequency and environmental variable [6] . In any case , complex demographic histories and entailing genetic structures are the major issues that challenge the genome-wide screening for adaptive genes , and a combination of different approaches is preferred to avoid false detections [6] . Because gene flow will erode and prevent a genetic divergence , adaptive differentiation is more likely to occur between populations that are reproductively isolated . Geographical distance can provide a strong reproductive barrier and also shape environmental differences ( e . g . , temperature along the latitudes ) , both of which may facilitate the adaptive divergence between populations . Indeed , most ecological genomic studies compare populations that are tens of hundreds of kilometers apart ( e . g . representative study cases reviewed in [7] ) . The problem of comparing distantly isolated populations is that the periods since population divergence are usually long enough to allow the intervention of various demographic processes . As a consequence , complicated population structure seems as an intrinsic difficulty to conduct the genome-wide scan for adaptive genes . Recently , growing number of works reporting microgeographic-scale adaptation [8–12] have corroborated the theory that adaptive population divergence can take place even under high gene flow if selective pressure is sufficient [13] . Microgeographic-scale adaptation may in fact be a suitable system for ecological genomics because the evolutionary split between nearby populations should be relatively recent compared to that of distantly isolated populations . Furthermore , gene flow may benefit the screening procedure because most of the genome is expected to be undifferentiated between populations , leaving the genetic footprints of a natural selection more pronounced [14] . In plant species , NGS-based restriction-site associated DNA ( RAD ) sequencing has been used to study the distinct ecotypes that occur within few kilometers from each other in Senecio [15] , and Helianthus [16] . Although these studies have provided insights into the phylogenetic history , population demography , and genomic structure dynamics during microgeographic-scale divergence , candidate genes that underlie the phenotypic differentiation were not identified . An example of microgeographic-scale divergence has been reported from a self-incompatible perennial plant , Arabidopsis halleri subsp . gemmifera . In Mt . Ibuki , a mountain located in central Japan , populations of this plant are continuously distributed along the top to bottom of a hiking trail . Although the linear distance between the lowest and highest populations is smaller than 3 km , highland ecotypes characterized by dense trichomes on the leaves and stems [17] are found on the peaks ( S1 Fig ) . A previous AFLP-based study on Mt . Ibuki demonstrated little genetic differentiation between normal and highland ecotypes collected from low and high altitudes [18] . Thus , it has been suggested that these two ecotypes share a similar genomic structure and the evolutionary split has occurred relatively recently . Interestingly , similar phenotypic divergence is also found along the altitudes of Mt . Fujiwara , which situate approximately 30 km from Mt . Ibuki . Highland ecotypes of the two mountains are regarded as a convergent evolution , however , no empirical evidences have yet been reported . In addition to denser trichomes , growth chamber measurements have confirmed other genetically based convergent characteristics of the highland ecotypes , such as shorter but thicker stems and leaves , increased resource investment to photosynthetic components , and increased accumulation of ultraviolet ( UV ) absorbing compounds [19] . Overall , these altitudinal differentiations are considered as a consequence of high altitude adaptation . Although trichomes in plants often serve in the defense against herbivores [20] , a study in A . halleri subsp . gemmifera revealed no clear correlation with leaf beetle damage [21] . Interestingly , the hyperaccumulator plant A . halleri accumulates zinc and cadmium inside its trichome bases [22] . This finding suggests that denser trichomes in the highland ecotypes might indicate higher tolerance to heavy metals . Alternative trichome functions in other plant species , including the prevention of external ice formation [23] , avoidance of excess transpiration under strong wind [24] , and protection against UV radiation [25] , are also considered to be related to the adaptive significance of dense trichomes at high altitudes . Other characteristics of the highland ecotypes are also associated with the common selective pressures in the two mountains , such as dwarf phenotype to resist strong wind , investment to photosynthetic component to compensate the reduced enzyme activity due to suboptimal conditions , and accumulation of UV absorbing compound to tolerate increased UV radiation [19] . However , mountain-specific altitudinal differentiations are also reported . For instance , freezing resistance [19] and rapid seed germination ( Shin-Ichi Morinaga , personal communications ) are found only in the highland ecotypes from Mt . Ibuki . Nevertheless , the two mountains share similar environmental characteristics in terms of altitudinal cline . Although both mountains are relatively low ( 1 , 377 and 1 , 144 m for Mt . Ibuki and Mt . Fujiwara , respectively ) , areas above approximately 1 , 000 m are host to open subalpine grasslands with calcareous scree and heavy snow cover in winter . In contrast , areas lower than approximately 400m occupy the understory of temperate forests . Annual temperature , snow depth , and canopy openness have been quantified to show gradient variation along the altitude in both mountains [19] . As in this case , mountain populations may be an excellent model for the analysis of microgeographic adaptation because steep environmental gradients can shape selective barriers on a small geographic scale . Thanks to the genetic information accumulated in A . thaliana , ecological genomics has become a powerful approach to screen adaptive genes from wild Arabidopsis species [26–29] . However , while these studies have provided fruitful insights into the genetic basis of local adaptations , genomic comparisons have so far been conducted at the macrogeographic-scale , using distantly isolated populations . Here , we test the prediction that genomic comparison at the microgeographic-scale can also offer an effective screening for the genetic basis of local adaptation . If the screening procedure works as expected , we should be able to find some correlation between the candidate genes and the observable phenotypic or environmental differentiation . In addition , a replicated analysis in two independent but synchronizing environmental transects will have a good chance of finding the genes involved in a convergent evolution . Our study system take advantage of the above mentioned populations of A . halleri subsp . gemmifera on Mt . Ibuki and Mt . Fujiwara , where populations continuously distribute along a steep environmental cline and the populations at each extreme ( the lowest and highest populations on each mountain ) are locally adapted to their habitats . Within each mountain , the loci governing altitudinal adaptation should be highly differentiated between the lowest and highest populations . More importantly , theoretical models predict that , if a set of populations is distributed along an environmental continuum and neighboring populations are exchange their genes , clines of allele frequencies at the adaptive loci can be observed [30 , 31] . Because neighboring populations of A . halleri subsp . gemmifera in both mountains are close enough to allow gene flow , we placed an emphasis on detecting correlations between allele frequencies and altitudinal clines . Thus , we employed both differentiation-based and correlation-based approaches to screen the selected loci from a genome-wide SNP dataset . Credibility of the screening procedure was evaluated by comparing the proportion of a certain Gene Ontology ( GO ) term between screened and unscreened set of genes . Here , we selected 30 GO terms that cover the representative phenotypic and environmental entries within the database . If we successfully retrieve the genes under natural selection , then we should be able to see coincidence between the enriched GO terms and the known phenotypic or environmental differentiation across the altitudes . Furthermore , the screened loci were narrowed based on the presence of genetic hitchhiking . The screening procedure was independently applied to each mountain , and we obtained two lists of candidate genes that are potentially involved in altitudinal adaptation . By comparing these gene lists , we distinguished between genes that are adaptive only in either mountain , and those involved in the convergent evolution .
To perform a genome-wide screen for loci associated with local altitudinal adaptation , we began by establishing a draft de novo reference genome for A . halleri subsp . gemmifera . The whole-genome shotgun method via next-generation sequencing ( NGS ) was applied to a single individual sampled from the base of Mt . Ibuki . Using 190× coverage sequence data ( haploid genome size of A . halleri = 255 Mbp [32] ) , genome assembly resulted in 149 , 013 scaffolds , with an N50 of 4 , 825 bp and a total of 252 Mbp , which corresponds to 98 . 8% of the entire genome . The resulting reference genome was evaluated by mapping A . thaliana exon sequences from 33 , 602 genes deposited in the TAIR10 database ( The Arabidopsis Information Resource; http://www . arabidopsis . org ) . For comparison , we mapped the same A . thaliana exon sequences to the high-quality reference genome of A . lyrata ( 695 scaffolds , with an N50 of 24 . 5 Mbp , totaling 207 Mbp [33] ) . As a result , 92 . 9% and 90 . 7% of the A . thaliana exons were mapped to the reference genomes of A . halleri subsp . gemmifera and A . lyrata , respectively . Although the number of scaffolds remains excessive compared with the actual chromosome number in A . halleri ( 2n = 16; [32] ) , our draft de novo reference genome sequence covers the entire genome well and will facilitate genomic studies in this species . On both Mt . Ibuki and Mt . Fujiwara , four distinct populations associated with different altitudes were situated along hiking trails from the bottom to the top of the mountains . The four populations are found at the altitudes of 380 , 600 , 1 , 000 , and 1 , 250 m on Mt . Ibuki and at 200 , 400 , 700 , and 1 , 100 m on Mt . Fujiwara ( Fig 1B and S1 Table ) . The linear distance between the lowest and highest populations is approximately 2 . 7 km on Mt . Ibuki and 1 . 9 km on Mt . Fujiwara . In addition to the main study sites , four reference populations were set apart from the mountains ( Fig 1A and S1 Table ) . These populations were situated at low altitudes ( 220 , 230 , 370 , and 520 m ) with environments similar to the lowest populations from the main study sites . On the two mountains , five individuals from each altitude-specific population were collected for analysis , whereas four individuals were collected from the reference populations . Through genome-wide resequencing of each of these 56 individuals , we obtained a set of 527 , 225 reliable SNPs with a minimum read count of five per individual ( S1 Table ) . The average inter-SNP spacing across the entire genome was 484 bp . The mapped A . thaliana exon information was used to examine the proximity of each SNP to a functional gene . Among the 527 , 225 SNPs , 327 , 980 overlapped with or were within 5 kbp of an exon for 22 , 395 genes . These SNPs and the associated functional gene information were used for the following analyses . Genetic diversity ( He ) was significantly different ( bonferroni-corrected p-value from pairwise Wilcoxon test < 0 . 01 ) among all paired populations within each mountain , except for IB0380 vs . IB0600 in Mt . Ibuki , and FJ0400 vs . FJ1100 in Mt . Fujiwara ( Table 1 ) . Although the statistical significance is somewhat overestimated , lower populations of Mt . Ibuki tended to have smaller genetic diversity compared to higher populations . To examine the population structure within and between the two mountains , we conducted a structure [34 , 35] analysis of all 56 individuals ( including the reference populations ) using a set of 10 , 000 randomly selected SNPs . Based on 20 independent runs for each value of K ( the number of subpopulations ) from 1 to 12 , both the log likelihood value and Evanno’s ΔK method [36] indicated the optimum K to be six ( Fig 2B ) . Under K = 6 , each cluster clearly corresponded to the two mountains and the four reference populations ( Fig 2A ) . It is notable that the four altitude-specific populations on each mountain were not genetically subdivided . However , subdivision within each mountain were indicated with higher K values . Further structure analysis within each mountain supported the split in Mt . Ibuki , but not in Mt . Fujiwara ( S2 Fig ) . Previous study has demonstrated that although snow depth and canopy openness increased with increasing altitude in both mountain , Mt . Ibuki showed steeper gradients for both environmental components [19] . Thus , the genetic split in Mt . Ibuki may indicate a restricted gene flow among the altitudes due to stronger environmental barrier . Nevertheless , interleaving populations of Mt . Ibuki ( IB0600 and IB1000 ) seem to be comprised of some admixed individuals . These individuals indicate the presence of gene flow between the neighboring altitude-specific populations . In fact , although highland ecotypes from the top of the mountain are easily distinguished based on their appearance , plants with intermediate phenotypes are found at intervening altitudes . Because highland and normal ecotypes are highly cross-compatible ( Shin-Ichi Morinaga , personal communications ) , these intermediate plants are likely to have originated from natural hybridization due to frequent gene flow between neighboring populations . In addition , pairwise G′ST values showed a pattern of genetic differentiation by distance in both mountains ( Table 1 ) . Thus , the population structure in each mountain can be regarded as a simple linear stepping-stone model proposed by Kimura and Weiss ( 1964 [37] ) . We also examined the historical relationship among populations with TreeMix [38] , a statistical model used to infer patterns of population splits and mixtures from genome-wide allele frequency data . The maximum likelihood tree based on 518 , 706 bi-allelic SNPs clearly demonstrated that the evolutionary split between the two mountains predated the differentiation of the altitude-specific populations ( Fig 2C ) . In addition , the tree explained most ( 99 . 1% ) of the variance in relatedness between the populations , which indicates that the tree captures the historical relationship without adopting migration events from distantly related populations . These results indicate that although the two mountains share a common ancestry , the differentiation of the altitude-specific populations took place independently on each mountain . Therefore , the morphologically similar highland ecotypes found on the two mountains may be considered to be a consequence of convergent evolution . Together with the results from structure analysis , these findings suggest that these populations are a suitable model for exploration of the genetic basis of microgeographic adaptation . To identify the SNPs associated with altitudinal adaption , we conducted a screening based on the following assumptions: first , and most importantly , we anticipated a cline in the allele frequency as a result of natural selection across environmental gradients . Therefore , we focused on those loci that undergo a unidirectional change in allele frequency along the altitudinal cline . To further reduce the number of candidate loci , we adopted the following two selection criteria: 1 ) the SNP loci should be highly divergent between the lowest and highest populations; and 2 ) the frequency of the derived allele should be higher in the highest-altitude populations . We developed an index U to measure the unidirectional change in allele frequency , used G′ST proposed by Hedrick ( 2005 [39] ) to measure the divergent between lowest and highest populations , and also developed an index ΔD′ to measure the frequency of the derived allele at the highest populations ( see Materials and Methods section for details ) . Indices at each loci were averaged across a 4kbp window size and the upper 1 . 5 times the IQR ( interquartile range ) of a genome-wide frequency distribution ( Fig 3 ) was determined as a screening threshold . Screening was conducted independently for the populations from each mountain , and only those SNP loci that fulfilled all three criteria were further considered . The number of SNPs that fulfilled the criteria was 5 , 523 for Mt . Ibuki and 5 , 407 for Mt . Fujiwara ( Fig 4 ) . The total number of identified SNPs in common between the two mountains were 358 . Among the screened SNPs , 3 , 869 from Mt . Ibuki and 3 , 527 from Mt . Fujiwara were linked ( overlapping or within 5 kbp of an exon ) to a gene . The number of genes linked to the screened SNPs was 923 and 924 on Mt . Ibuki and Mt . Fujiwara , respectively . To gain perspective into the biological process in which the screened SNPs are involved , we conducted a Gene Ontology ( GO ) enrichment analysis for each mountain . We tested for enrichment in 30 GO terms that cover the representative phenotypic and environmental entries within the database . To adjust for multiple comparisons , significant enrichment was accepted if the corresponding false discovery rate ( FDR ) q-value [40] was below 0 . 05 . Here , we tested for enrichment using two approaches: one is an SNP-based method , where the ratio of SNPs that are associated and unassociated with a given GO term is compared between the lists of screened ( SNP loci that fulfilled all three criteria mentioned above ) and unscreened ( all SNP loci ) datasets . Another is a gene-based method , where the ratio of genes that are associated and unassociated with a given GO term is compared between the lists of screened and unscreened SNPs . Because the SNP-based method assumes that every screened SNP represents an independent observation , linkage between SNPs will cause bias , and the significance of enrichment will be overestimated [41] . However , the gene-based method ignores the joint effect of multiple SNPs within a gene , which may underestimate the significance of enrichment [41 , 42] . As previously recommended for gene set enrichment analysis [43] , we declare that our enrichment analysis is an exploratory procedure rather than a pure statistical solution . Not surprisingly , the SNP-based method detected more significant enrichment in GO terms compared with the gene-based method ( Fig 5 and S2 Table ) . Here , we discuss the SNP-based enriched GO terms that were significant in both mountains . The four common GO terms were ‘response to red or far red light , ’ ‘cellular response to DNA damage stimulus , ’ ‘meristem development , ’ and ‘trichome differentiation . ’ It is noteworthy that the GO term related to trichomes , which constitute the most distinguishing characteristic of the highland ecotype [17] , was detected in both mountains . In addition , enrichment for ‘trichome differentiation’ was also indicated by the gene-based method in both mountains . Detection of a major defining characteristic of the highland ecotype supports the validity of our screening procedure . Although the adaptive significance of the denser trichomes in the highland ecotypes remains unknown , our result strongly suggests that the trait has evolved under an common selective pressure between the two mountains . Another common GO term related to morphogenesis was ‘meristem development . ’ This GO term can be related to the morphological differentiation where plants at the lower altitude are characterized by their tall and spindly appearance , and highland ecotypes by their dwarf-like appearance ( S1 Fig ) . Another common GO term ‘response to red or far red light’ is also interesting since previous observation has detected a positive correlation between canopy openness and altitude in both mountains [19] . Although we could not observe an enrichment in the term ‘photosynthesis , ’ the increased investment to photosynthetic components in the higher altitudes in both mountains could be related to an adaptation against light environment variance . In this context , measurement based on cyclobutane pyrimidine dimer has demonstrated that opened canopy at higher altitudes induce increased UV induced DNA damage . At the same time , a correlation between altitude and UV tolerance via accumulation of UV absorbing compound was also detected in both mountains [19] . Although enrichment in the term ‘response to UV’ was not detected , we succeeded to find a significant enrichment in the term ‘cellular response to DNA damage stimulus’ in both mountains . These coincidence point out a possibility that light environment is an important selective pressure for the convergent evolution between the two mountains . On the other hand , although tolerance against freezing seems as an indispensable ability for high-altitude adaptation , previous observation detected an increased tolerance only from the highland ecotypes of Mt . Ibuki [19] . GO enrichment analysis were consistent with this result , where significant enrichment of the term ‘response to freezing’ was detected in Mt . Ibuki , but not in Mt . Fujiwara . Overall , consistency between the enriched GO terms and known features of the highland ecotypes suggests that our screening procedure provided a good estimate for the SNP loci associated with altitudinal adaptation . Here , we also tested other popular approaches to find the loci under selection . We used BayeScan [44–46] to find the FST outliers between the lowest and highest populations , and LFMM ( Latent Factor Mixed Models [47] ) to find the loci that correlate with the altitude . As shown in S3 Fig , these typical outlier tests did not fit very well with our dataset , especially in terms of detecting statistically significant outliers . More specific , at the significance level of a FDR q-value = 0 . 01 , none of the loci from both mountains were detected by the BayeScan analysis . In Mt . Ibuki , –log10 ( q-value ) of even those with the most highly differentiated loci ( loci that are fixed for one allele in the lowest , and fixed for another in the highest population ) reached a ceiling around 1 . 0 . The problem seems to be caused by our sampling design , where small number of individuals were collected from limited geographical points . According to the manual for BayeScan , statistical power to detect the outliers will be limited when small sample size is used . On the other hand , LFMM analysis detected 1 , 530 outliers ( FDR q-value < 0 . 01 ) in Mt . Ibuki , however , none were detected in Mt . Fujiwara . In LFMM , the background population structure is modelled from a chosen number of latent factors ( K ) , which corresponds to the number of neutral genetic structure of the data . Underestimated value of K leads to liberal tests with false positives , whereas overestimated K leads to conservative tests with false negatives . Here , we used K = 2 as a number of latent factor in both mountains . From the structure analysis , a genetic split was detected in Mt . Ibuki and K = 2 was statistically supported ( S2A and S2C Fig ) . However , in Mt . Fujiwara , clear differentiation ( K = 2 ) was not supported ( S2B and S2D Fig ) . Thus , K = 2 for Mt . Fujiwara may have been an overestimate , leading to a conservative test with false negatives . Although we can run the LFMM with K = 1 , such run will not account for background population structures and will produce a plethora of false positives because a large set of loci is correlated with the altitude . Overall , because typical outlier analyses expect a set of numerous individuals from variable locations ( environment ) as an input , our dataset would not be suitable for these tests . Another problem may be the linear stepping-stone population structure detected in our study sites ( Table 1 ) , where not only the adaptive loci but also a large set of neutral loci can be correlated with the altitude . Under this condition , it would be difficult to determine the cutoffs to correct for the underlying population structure . Based on the screened SNPs linked to genes , we attempted to narrow down and sort the candidate genes according to the likelihood of having undergone natural selection . Here , we assumed that the presence of genetic hitchhiking represented a footprint of a selective sweep [48] . However , we acknowledge that variation in mutation rates , non-uniform recombination rates , and chromosomal rearrangements can also lead to differentiated genomic regions and clusters of genes that contribute to local adaptation are more likely to diverge together regardless of selective sweeps [49] . To detect local signatures of genetic hitchhiking , we scanned for continuous allele frequency clines ( the primary criterion for screening the SNPs ) around the screened gene-linked SNPs . Through an independent scanning procedure within each mountain , we identified 474 and 629 continuous hitchhiking regions , or ‘genomic islands , ’ which included 573 and 721 genes in the populations from Mt . Ibuki and Mt . Fujiwara , respectively ( see S3 Table for the genes within top 100 genomic islands ) . To reduce the false positive detection from a single SNP locus , genomic islands that contained only one screened SNPs were rejected and total of 350 and 203 genes from Mt . Ibuki and Mt . Fujiwara , respectively , were excluded . Based on the length of the continuous hitchhiking region ( i . e . , the length of linkage disequilibrium ) and the steepness of the allele frequency clines ( i . e . , the difference in allele frequencies between lowest and highest populations ) , the genomic islands were ranked according to how likely they were to have undergone a selective sweep ( see S4 Fig for workflow ) . Linkage disequilibrium can be disrupted by recurrent mutations and recombination events during the evolutionary time course; a higher ranking indicates that the genomic region experienced stronger and/or more recent natural selection . Here , we considered the top 20 genomic islands as promising candidates that were recently subject to natural selection ( Table 2 ) . For example , we detected a steep allele frequency cline spanning approximately 10 kbp on Mt . Ibuki , with a peak near the 5’ UTR of EDA8 ( AT4G00310; Fig 6A ) . EDA8 includes GO terms such as ‘regulation of flower development’ , ‘response to freezing’ , and ‘seed dormancy process’ [50] . Because freezing tolerance [19] , flowering period , and seed dormancy ( Shin-Ichi Morinaga , personal communications ) differ between the lowest and highest populations from Mt . Ibuki , the functional annotations of EDA8 are in line with the known phenotypic and environmental differences between altitudes . However , an allele frequency cline was not detected in the same genomic region on Mt . Fujiwara ( Fig 6B ) . Mountain-specific candidate genes , such as EDA8 , may indicate the underlying differences in natural selection between the mountains or that each mountain utilizes distinct genes to overcome a common natural selective pressure . Other genes from Mt . Ibuki with notable GO terms included the following: FNR1 ( AT5G66190 ) , with ‘response to cold , ’ and ‘photosynthesis’ [50]; LIS ( AT2G41500 ) , with ‘seed dormancy process , ’ and ‘response to freezing’ [50]; EMB2788 ( AT4G27010 ) with ‘regulation of flower development’ [50]; SAR1 ( AT1G33410 ) , with ‘regulation of flower development’ [50]; FTSH12 ( AT1G79560 ) with ‘embryo development ending in seed dormancy’ [51]; and AT5G16280 with ‘vegetative to reproductive phase transition of meristem’ [50] . Specific genes from Mt . Fujiwara included the following: AT2G40270 with ‘response to bacterium , ’ and ‘response to insect’ [50]; BAM7 ( AT2G45880 ) with ‘vernalization response’ [50]; STO ( AT1G06040 ) , with ‘response to temperature stimulus , ’ and ‘response to light stimulus’ [50 , 52]; AVP1 ( AT1G15690 ) , with ‘response to water deprivation , ’ and ‘response to salt stress’ [53]; and FWA ( AT4G25530 ) , with ‘photoperiodism , flowering , ’ and ‘trichome morphogenesis’ [50] ( see Table 2 ) . Detailed analysis of the adaptive roles of these mountain-specific genes in A . halleri subsp . gemmifera would highlight unique characteristics of natural selection in the superficially similar habitats between the two mountains . We also found that some genes within the list shared a common function . For instance , four genes from Mt . Ibuki ( EDA8 , PBA1 , FNR1 , and LIS ) and three genes from Mt . Fujiwara ( PBA1 , BAM7 , and STO ) had GO terms under ‘response to temperature stimulus . ’ Among the 22 , 395 SNP-tagged genes , only 863 were associated with this GO term , and an empirical p-value for the observed result was 0 . 007 . Although increased freezing tolerance was detected only in highland ecotypes of Mt . Ibuki [19] , our results suggest that temperature variation can be an important selective pressure for altitudinal adaptation in both mountains . Inferring environments and ecological traits from genomic information , the so-called ‘reverse ecology’ approach [54] , may give rise to a new era in ecological genomics on wild plant species . The most novel findings of this study are candidate genes that are shared between the two mountains . In total , two genes were ranked within the top 20 genomic islands on both mountains . An empirical p-value to find two common genes between two independent gene lists from a set of 22 , 395 SNP-tagged genes was 0 . 001 , which supports the presence of convergent evolution involving the same genes . Interestingly , both genes had functional annotations relevant to altitudinal adaptation . One of these ‘shared’ genes is GSL8 ( AT2G36850 ) , which is annotated with the GO terms ‘meristem initiation , ’ ‘trichome morphogenesis , ’ and ‘telomere maintenance in response to DNA damage’ [50] . On both mountains , the genomic region around GSL8 underwent a continuous unidirectional allele frequency shift that spanned at least approximately 15 kbp and most likely involved a longer region ( Fig 6C and 6D ) . The long linkage distance observed in this case may be evidence of recent selection acting on this genomic region . In addition , anatomical observation of transposon-induced gsl8 A . thaliana mutant lines detected dwarfed growth , revealing the wild-type gene function in normal morphological development [55] . These results indicate that GSL8 is an ideal candidate gene for explaining the morphological convergence found between the highland ecotypes on the two mountains . Another candidate is PBA1 ( AT4G31300 ) , which presents the GO terms ‘response to temperature stimulus , ’ ‘response to salt stress , ’ and ‘response to cadmium ion’ [50 , 56] . PBA1 shows an altered expression level in response to various stresses , such as NaCl [56] , zinc [57] , genotoxic agents [58] , oxidants [59] , and viral infection [60] . Furthermore , an RNAi knockdown lineage showed defects in plant immunity against bacterial pathogens [61] . Considering the variety of functions related to abiotic and biotic stresses , PBA1 appears to be a promising candidate for playing a role in altitudinal adaptation . Overall , these ‘shared’ genes may be a result of common natural selection acting on genetic variation that preceded the divergence of the two mountain populations , and they highlight the genetic basis of convergent evolution . Needless to say , other highly ranked genes without notable GO terms are also worth examining because they might retain unknown adaptive functions . To validate our result , the screened candidate genes must be further investigated by functional analyses of the genes , detecting loci that alter fitness , and field measurements including transplantation experiments . Another ecological genomic study in A . halleri has been conducted at the Swiss Alps , where genome-wide SNP analyses were performed to search for the imprints from natural selection related to environmental variation [29] . By focusing on the highly differentiated genomic regions associated with environmental factors such as precipitation , slope , radiation , site water balance , and temperature , a list of 175 genes were obtained . Although the study case in the Swiss Alps was conducted in a wider geographical scale compared to the present study , the populations were situated at various altitudes ranging from 790 m to 2 , 308 m . Thus , we may have a chance to find common genes related to altitudinal adaptation between the mountains in central Japan ( Mt . Ibuki and Mt . Fujiwara ) and Swiss Alps . Unfortunately , none of the genes within the top 20 genomic islands from our study were found in the 175 genes from the Swiss Alps . However , three genes within each of the top 100 genomic islands from Mt . Ibuki and Mt . Fujiwara were also listed in the Alps ( S3 Table ) . Although the coincidence is not surprising considering the large number of genes within each list ( empirical p-value for the observed result was 0 . 09 for Mt . Ibuki and 0 . 06 for Mt . Fujiwara ) , we noticed that a single gene , CMT1 ( AT1G80740 ) , was detected in all three locations ( empirical p-value = 0 . 006 ) . This gene was ranked as the 51st and 40th in the gene list from Mt . Ibuki and Mt . Fujiwara , respectively , and was associated with site water balance in the Swiss Alps . Although we must further compare the selected loci and haplotypes between central Japan and Swiss Alps , the gene may be an evidence of convergent evolution to altitude in different continents . Although theories for local adaptation have supported the development of population genomics , several central predictions remain untested , especially for predictions involving gene flow ( reviewed in [1] ) . Under gene flow , adaptive differentiation requires an allele with high fitness in one environment to show lower fitness in the other environment [62] . Thus , fitness trade-offs of the adaptive traits are expected to be associated with trade-off at the loci level . Otherwise the allele with the highest fitness will invade the other population thereby causing the locus to become monomorphic [63] . In addition , the loci involved in local adaptation are expected to cluster together on the chromosomes [14 , 49 , 64] . Further investigations on our candidate genes should provide an opportunity to empirically evaluate the untested predictions , and help understand the evolutionary dynamics of adaptive genes during local adaptation . In this context , an improved reference genome with longer scaffolds would not only enhance accuracy of detecting the selected genes , but also would assist in clarifying the positional relationship among the adaptive loci . The Joint Genome Institute ( JGI ) has recently assembled another reference genome for A . halleri which is available at: http://phytozome . jgi . doe . gov/pz/portal . html# ! info ? alias=Org_Ahalleri_er . Although dataset usage is restricted prior to publication , the reference genome from JGI has shorter total genome size ( 145 . 5 Mbp versus 252 . 2 Mbp ) but longer N50 value ( 24 . 4 Kbp versus 4 . 8 Kbp ) , compared to our present reference genome . However , we are also developing an improved version of the A . halleri subsp . gemmifera reference genome , which should be comparable to the A . halleri genome from JGI . Our study demonstrates that typical outlier-based approaches ( BayeScan [44–46] and LFMM [47] ) have limitation in screening for the selected loci at a microgeographic-scale . Due to recent colonization event , not only the selected loci , but also a large set of neutral loci can show patterns of variation where allele frequencies change along the environmental gradient . In such cases , the selected loci may not differ from the genomic mean sufficiently to be considered as an outlier . We therefore suggest that a genomic region-based approach ( genomic islands in the present study ) which aims to detect the genetic hitchhiking regions may be more successful , rather than approaches that treat each locus as independent . Another promising approach would be a comparison between parallel environmental gradients . A study in sessile oak investigated whether SNP variation of candidate genes reflect the clinal pattern of bud burst along altitudinal and latitudinal gradients [65] . By comparing the results in the two parallel gradients , a set of genes showing imprints of selection in both gradients were obtained , which can be considered as evidence for convergent evolution . In the present study , we also utilized two independent but parallel altitudinal clines , where phenotypic observations indicate the presence of a convergent evolution . Because the probability of occasionally detecting the same gene from parallel environmental gradients is very low , the common genes appear intuitively promising . We anticipate that the number of ecological genomic studies on convergent evolutions will grow , as it provides an excellent opportunity to efficiently screen the candidate genes responding to natural selection .
Arabidopsis halleri subsp . gemmifera is a perennial , self-incompatible , clonal herb distributed in the Russian Far East , northeastern China , Korea , Taiwan , and Japan [66] . The highland ecotype , characterized by denser trichomes , was formerly treated as the variant Arabis gemmifera var . alpicola [17] and is found only in the higher altitudes of Mt . Ibuki and Mt . Fujiwara in central Japan . On both mountains , continuous variation in morphological characters is found along altitudes ( Shin-Ichi Morinaga , personal communications ) . Our main study populations were located on Mt . Ibuki ( IB0380 , IB0600 , IB1000 , and IB1250 ) and Mt . Fujiwara ( FJ0200 , FJ0400 , FJ0700 , and FJ1100 ) . The low-altitude reference populations were situated at Minoo ( MN0220 ) , Inotani ( IN0230 ) , Itamuro ( IT0520 ) , and Okunikkawa ( OK0370 ) . See Fig 1A and 1B and S1 Table for the location and coordinates . Leaf samples were collected from each of the 12 populations and silica-dried for subsequent DNA extraction . To avoid sampling of clones , the sampled individuals were at least 4 m apart from each other . Genomic DNA was extracted from the dried leaf of a single individual using the DNeasy Plant Kit ( QIAGEN ) . This individual was collected from population IB0380 and was not included in the resequencing analysis . DNA libraries were prepared using the Illumina TruSeq DNA Sample Preparation Kit for paired-end reads , the Roche GS Titanium Rapid Library Preparation Kit for 454 single reads , and the SOLiD Mate-Paired Library Construction Kit for mate-pair reads . Instead of SOLiD adapters , Illumina adapters were used in the final step of mate-pair library construction . Reads were generated using the Illumina GAIIx , HiSeq2000 ( 300 bp paired-end reads , 3 kbp and 5 kbp mate-pair reads ) , and Roche 454 GS FLX Plus Titanium ( single reads ) systems . Subsequent data processing was performed with CLC Genomics Workbench version 6 ( CLC bio ) . Raw reads were trimmed based on quality scores of 0 . 05 and a maximum allowance of two ambiguous nucleotides . Reads shorter than 60 bp for the Illumina platform and 100 bp for the Roche 454 platform were discarded . De novo assembly was carried out using the “De Novo Assembly” function with the following parameters: Mismatch cost 3 , Insertion cost 3 , Deletion cost 3 , Length fraction 1 , Similarity 1 , Minimum contig length 200 . Single reads from the Roche 454 platform were used as guidance-only reads . The number of reads used to construct the reference genome was as follows: 74 , 102 , 134 ( 7 , 034 , 411 , 911 nt ) Illumina 300 bp paired-end reads , 150 , 099 , 682 ( 13 , 756 , 599 , 514 nt ) Illumina 3 kbp mate-pair reads , 127 , 910 , 808 ( 11 , 644 , 031 , 026 nt ) Illumina 5 kbp mate-pair reads , 66 , 195 , 930 ( 6 , 338 , 573 , 278 nt ) single reads from the broken pairs of Illumina 3 kbp mate-pair reads , 73 , 840 , 719 ( 7 , 058 , 674 , 210 nt ) single reads from the broken pairs of Illumina 5 kbp mate-pair reads , and 3 , 534 , 305 ( 2 , 579 , 555 , 709 nt ) Roche 454 single reads . The established de novo A . halleri subsp . gemmifera reference genome sequences is uploaded online and freely available . The quality of the assembled reference genome was validated by mapping the exon sequence of A . thaliana at the TAIR10 database ( The Arabidopsis Information Resource; http://www . arabidopsis . org ) . A total of 217 , 183 A . thaliana exon sequences were mapped using the “Map Reads to Reference” function with the following parameters: Mismatch cost 2 , Insertion cost 2 , Deletion cost 2 , Length fraction 0 . 3 , Similarity 0 . 9 . Using the same parameter settings , the A . thaliana exon sequences were mapped to the reference genome of A . lyrata [33] downloaded from the JGI’s PHYTOZOME portal ( US Department of Energy Joint Genome Institute; http://www . phytozome . net/alyrata ) . Genomic DNA from each of the 56 individuals was isolated with the DNeasy Plant Kit ( QIAGEN ) . DNA libraries were constructed according to the Low-Throughput Protocol of the TruSeq DNA Sample Preparation Kit ( Illumina ) . After quantification , 76 and 93 bp paired-end reads were obtained from the Illumina GAIIx platform and 101 bp paired-end reads from the HiSeq2000 platform . Raw short read sequences have been deposited at DDBJ and are freely available . Subsequent mapping and SNP calling procedures were performed using CLC Genomics Workbench version 6 ( CLC bio ) . Prior to mapping , all sequences were trimmed based on a quality score of 0 . 05 and a maximum allowance of two ambiguous nucleotides . Broken pairs and reads shorter than 65 bp were discarded . For each individual , the reads were mapped to the reference genome with the following parameters: Mismatch cost 3 , Insertion cost 3 , Deletion cost 3 , Length Fraction 0 . 97 , and Similarity fraction 0 . 97 . The reads from each individual were mapped to satisfy 9- to 15-fold coverage of the reference genome ( S1 Table ) . We used 101 bp reads for mapping , but shorter reads were employed when the input was insufficient to meet the coverage demands . The short reads used for each individual are now undergoing the registration process and will be made freely available . SNPs were accepted if the locus had at least five reads per individual and the frequency of the antagonistic allele exceeded 20% . A total of 2 million provisional SNP loci were detected from the 56 individuals . Among these loci , those with a total read count over 10 , 000 were excluded because excessive read coverage may indicate nucleotide mismatches from paralogous copies of duplicated sequences . In addition , only those loci that had at least five reads in each individual were retained . Accordingly , a set of 527 , 225 SNP loci with an average read coverage per individual of 20 was obtained . Among these reliable SNP loci , 518 , 706 were bi-allelic , while 8 , 442 were tri-allelic , and 77 were tetra-allelic . A Bayesian clustering analysis of population structure was performed with structure version 2 . 3 . 4 [34 , 35] . All 56 individuals from the 12 populations were subjected to analysis , and 10 , 000 SNP loci were randomly selected for the input dataset . Twenty independent runs for each value of K ( the number of subpopulations ) ranging from 1 to 12 were performed . For the optional setting for each run , we chose 400 , 000 iterations , with the first 200 , 000 iterations discarded as burn-in , and we applied the admixture model with correlated allele frequencies . To decide the best number of genetic clusters for the 56 individuals , we plotted the values of LnP ( D ) ( log likelihood of the observed genotype distribution ) and estimated Evanno’s ΔK [36] for each K . Based on the largest value of LnP ( D ) and a clear peak of ΔK , we selected 6 as the best K ( Fig 2B ) . As we found further subdivisions within the mountains in runs with K above 6 , we conducted additional analysis within each mountain . Using the same SNP loci and settings mentioned above , 20 individuals from each mountain were subjected to a set of analysis with K from 1 to 4 . Although LnP ( D ) and ΔK supported K = 2 for Mt . Ibuki , genetic subdivision was not supported in Mt . Fujiwara ( S2 Fig ) . Graphical representations of the results were generated using the program Distruct [67] . A maximum likelihood tree of the 12 populations was constructed with TreeMix version 1 . 12 [38] . This program uses a set of genome-wide allele frequency data from populations to construct the maximum-likelihood tree . Population splits are represented as nodes , and branch lengths are proportional to the amount of genetic drift experienced by the population . Migration events are inferred for populations that fit the tree poorly . Input allele frequency data for the 12 populations were generated based on 518 , 706 bi-allelic SNP loci . We first inferred the maximum likelihood tree without adopting a migration event , using OK0370 as an outgroup . To judge the confidence of the topology , 100 bootstrap replicates were performed . We then calculated the fraction of the variance in relatedness between populations that was explained by the tree ( f of Equation 30 in [38] ) . Screening of the 527 , 225 SNP loci was carried out according to the following three distinct criteria . For the first criterion , we defined an index ( U ) to evaluate the level of unidirectional change in allele frequencies across altitudes . For each locus , the following index , ranging from −1 to 1 , was calculated for each mountain: U=|FL−FH|+|FL−FH|−|FL−FM1|−|FM1−FM2|−|FM2−FH|2 where F indicates the allele frequency of a specific nucleotide in the lowest ( L ) , lower-middle ( M1 ) , higher-middle ( M2 ) , and highest ( H ) altitude-specific populations . The nucleotide showing the largest allele frequency difference between the lowest and highest populations was used to calculate each F . The index yields greater values if the difference in the allele frequency between the lowest and highest populations is larger and if the allele frequency of the intervening population falls between that in the lower and higher populations . In other words , for a given allele frequency difference between the lowest and highest population , U value is highest when the frequency increases or decreases monotonically along the altitude . For each SNP locus , we calculated U¯ , which is an average of the U values 2 kbp down- and upstream ( 4 kbp window size ) from its genomic position to minimize the spurious noise from single SNP locus . The second criterion was used to evaluate the genetic difference of a given SNP locus between the lowest and highest populations within each mountain . For each SNP locus , Hedrick’s G′ST [39] were calculated and averaged across 2 kbp down- and upstream from its genomic position to obtain G′ST¯ . Because the preceding two criteria basically filter those genes that are highly differentiated between lowest and highest populations , genes adaptive in the lower altitude can also be detected . While those genes are also interesting , our study system focus on high-altitude convergent evolution in two distinct mountains , and thus needed a third criteria to spot the genes that are related to high-altitude adaptation . Thus the third criterion was adopted to select those loci that show increased derived allele frequency ( DAF ) in the highest population compared with the low-altitude reference populations . Allele frequency data from the four reference populations ( 16 individuals in total ) were combined and the allele with minor frequency was regarded as the derived allele . DAF of the reference populations ranges from 0 to 0 . 5 , whereas DAF of the highest population ranges from 0 to 1 . 0 . For tri- and tetra-allelic locus , we subtracted the major allele frequency from one and used it to calculate the DAF . An index to measure the increment of DAF in the highest populations ( ΔD′ ) was calculated by: ΔD′= ( |DH−DR| ) ( 1−DR ) where DH is the DAF in the highest population , and DR the DAF in the reference populations . As we are not sure whether the allele is really ‘derived , ’ especially for locus with high minor allele frequency in the reference populations , absolute value for the DAF difference is used . In addition , a probability of the allele being derivative ( 1 − DR ) was used to correct the absolute DAF difference between the highest and reference populations . As well as other indices , ΔD′ values were also averaged 2 kbp down- and upstream ( 4 kbp window size ) from its genomic position to obtain ΔD′¯ . For all three indices ( U¯ , G′ST¯ , and ΔD′¯ ) , we analyzed the genome-wide frequency distribution and the upper 1 . 5 times the IQR of a genome-wide frequency distribution ( Fig 3 ) was determined as a screening threshold . Screening was conducted independently for each mountain , and only those SNP loci that fulfilled all three criteria were considered further . Note that the three criteria are not completely independent . For instance , a steep monotonic allele shift along the altitude is likely to be found among loci that are highly differentiated between the lowest and highest populations . See Fig 4 for the overlaps between the sets of loci screened by different criteria . To test for enrichment of a specific gene function among the screened SNPs , we conducted a Gene Ontology ( GO ) enrichment analysis with 30 GO terms that cover the representative phenotypic and environmental entries within the database . ( See S2 Table for the complete list of the selected GO terms ) . Here , only those SNP loci that were linked ( overlapping or within 5 kbp of an exon ) to a mapped gene in the A . halleri subsp . gemmifera reference genome were used . The ratio between ‘the number of SNP loci ( or genes ) associated with a given GO term within the screened dataset’ and ‘the number of SNP loci ( or genes ) unassociated with a given GO term within the screened dataset’ was compared with the same ratio obtained from the unscreened dataset . Significant enrichment for each GO term was computed with a one-tailed Fisher’s exact test for a 2 × 2 table [68 , 69] , and p-values from multiple comparisons were adjusted using a 0 . 05 threshold of the FDR q-value [40] . We also applied our datasets to two popular outlier detection methods that take account of the underlying population structures . Both analysis were independently conducted in each mountain . BayeScan uses a hierarchal Bayesian approach to detect outliers from the locus-specific FST distribution [44–46] . The program is based on a multinomial Dirichlet model that covers a wide range of realistic demographic scenarios . In addition , the program can be used with small number of samples with the risk of a low power , but with no particular risk of bias . We run our dataset with BayeScan 2 . 1 using the default parameter settings ( 20 pilot runs for 5 , 000 length , 50 , 000 burn in followed by additional 50 , 000 iteration with a thinning interval of 10 ) . Posterior probabilities for each locus were calculated and corrected by the FDR method implemented in the program . Outliers were identified at the 1% significant levels of the FDR q-value . Another method LFMM ( Latent Factor Mixed Models ) uses a hierarchal Bayesian mixed model to detect outliers from correlations between environmental and genetic variation [47] . At the same time , the program infers the background levels of population structure based on principal component analysis . Population structure is modelled from a chosen number of latent factors ( K ) , which corresponds to the number of principal components to describe the neutral structure of the data . Underestimated value of K leads to liberal tests with false positives , whereas overestimated K leads to conservative tests with false negatives . Here , based on the results from structure analysis , we used K = 2 as a number of latent factor in each mountain . Population altitudes shown in S1 Table were used as the environmental data for each individual . Using the program lfmm in the LEA package version 1 . 0 ( LEA: an R package for Landscape and Ecological Association studies; http://membres-timc . imag . fr/Olivier . Francois/LEA/index . htm ) , we conducted 20 runs with a burn in number of 5 , 000 and a total of 10 , 000 iterations . FDR q-value [40] was calculated for each locus based on the outputted p-values . The sorting process of the candidate genes was based on the level of unidirectional change in allele frequencies across altitudes ( U¯ described above ) and the effect of genetic hitchhiking . First , the U¯ values of all SNP loci were plotted and connected with a line across genomic regions . Continuous regions with positive U¯ values , starting and ending at the x-intercept , were considered to be hitchhiking regions ( genomic island ) . In addition to the x-intercept , the genomic islands were terminated if the neighboring SNP loci were more than 4 kbp apart . We then defined the x-axis as the base and computed the area inside each genomic island . Genomic islands that contained at least two screened SNP loci were sorted from those with the largest area . The top 20 genomic islands contained 38 and 32 genes in the Mt . Ibuki and Mt . Fujiwara populations , respectively ( see S3 Table ) . Finally , to visualize the unidirectional change in allele frequency , the difference in allele frequencies between the lowest and higher populations was plotted using a sliding window approach with window size of 4 kbp and a step size of 1 kbp ( see S4 Fig for workflow ) . We also carried out a simulation-based analysis to confirm the statistical significance of our results . To calculate the empirical p-value for obtaining two common genes from the two independent gene lists , we performed one million trials of randomly selecting 38 ( number of candidate genes within the list for Mt . Ibuki ) and 32 ( number of candidate genes within the list for Mt . Fujiwara ) genes from a set of 23 , 395 genes ( total number of analyzed SNP-tagged genes ) . For each trial , we examined the number of shared genes between the two lists . Similarly , we calculated the empirical p-value for detecting three and four genes with the GO term ‘response to temperature stimulus’ in two gene lists . Again , we performed one million trials of randomly selecting 38 and 32 genes from a set of 23 , 395 genes . This time , however , 863 of the 23 , 395 genes were tagged with the GO term ‘response to temperature stimulus’ and we counted the number of genes with the GO term in the two derived gene lists . | Where does a local adaptation take place ? In general , an adaptive divergence is predicted to occur between isolated populations because gene flow will erode and prevent the divergence . Therefore , previous genome-wide studies that aim to find the adaptive genes have compared populations that are usually tens of hundreds of kilometers apart . However , because nearby populations are likely to be genetically connected or connected until recently , most of the genome should be undifferentiated , leaving the genetic footprints of natural selections more pronounced . Thus , if an adaptive divergence is to be found within a small spatial scale , such case may favor the screening for the adaptive genes . Here , we took advantage of a unique small-scale local adaptation in Arabidopsis halleri subsp . gemmifera , where similar phenotypic differentiation is found across an altitudinal cline on two distinct mountains . By scanning the genome with a focus on the presence of unidirectional allele frequency shift along the altitudes , we successfully obtained genes with functions that were in line with the known phenotypic and environmental difference between altitudes . Our approach is applicable to any species that show microgeographic divergence and should help understand the genetic basis of small-scale evolution . | [
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] | [] | 2015 | A Genome Scan for Genes Underlying Microgeographic-Scale Local Adaptation in a Wild Arabidopsis Species |
Recent studies indicate a mutual epidemiological relationship between coronary heart disease ( CHD ) and periodontitis . Both diseases are associated with similar risk factors and are characterized by a chronic inflammatory process . In a candidate-gene association study , we identify an association of a genetic susceptibility locus shared by both diseases . We confirm the known association of two neighboring linkage disequilibrium regions on human chromosome 9p21 . 3 with CHD and show the additional strong association of these loci with the risk of aggressive periodontitis . For the lead SNP of the main associated linkage disequilibrium region , rs1333048 , the odds ratio of the autosomal-recessive mode of inheritance is 1 . 99 ( 95% confidence interval 1 . 33–2 . 94; P = 6 . 9×10−4 ) for generalized aggressive periodontitis , and 1 . 72 ( 1 . 06–2 . 76; P = 2 . 6×10−2 ) for localized aggressive periodontitis . The two associated linkage disequilibrium regions map to the sequence of the large antisense noncoding RNA ANRIL , which partly overlaps regulatory and coding sequences of CDKN2A/CDKN2B . A closely located diabetes-associated variant was independent of the CHD and periodontitis risk haplotypes . Our study demonstrates that CHD and periodontitis are genetically related by at least one susceptibility locus , which is possibly involved in ANRIL activity and independent of diabetes associated risk variants within this region . Elucidation of the interplay of ANRIL transcript variants and their involvement in increased susceptibility to the interactive diseases CHD and periodontitis promises new insight into the underlying shared pathogenic mechanisms of these complex common diseases .
Coronary Heart Disease ( CHD ) is the leading cause of death worldwide [1] , [2] . It is a systemic disease which is propagated by several environmental and behavioural risk factors [3]–[7] with inflammation as an important additional risk [8] . It has a strong genetic basis [9] , but despite decades of intensive genetic research , until recently , the sequence variants that confer cardiovascular risk had remained largely unknown . Periodontitis is a complex chronic inflammatory disease , resulting in a loss of connective tissue and bone support of the teeth [10] . It is the major cause of tooth loss in adults above 40 years , and according to the WHO , affects human populations worldwide at prevalence rates of up to 10 to 20% for the most severe forms [11] . Moreover , it is associated with an increase in premature death in adults [12] . Formal genetic studies have demonstrated the genetic basis of periodontitis and indicated that about half of the population variance in chronic periodontitis can be attributed to genetic factors , with a concordance rate of 0 . 23–0 . 38 for monozygotic twins [13] , and a heritability of 50% for dizygotic twins [14] . But hitherto , association studies with periodontitis have led to controversial results and its inherited components have remained unexplained [15] . Epidemiological studies demonstrated an association between the presence of CHD and periodontitis [16] , [17] , which is dependent on the severity of periodontal disease [18] , [19] . CHD is also propagated by risk factors similar to periodontitis . Both diseases share smoking as a major environmental risk factor and relate to diabetes mellitus , obesity , and gender [6] , [7] , [20]–[22] . Recent studies also demonstrated similarities in the spectrum of bacteria in the oral cavity and in coronary plaques [23] , [24] , and both diseases are characterized by an imbalanced immune reaction and a chronic inflammatory process [25] , [26] . Periodontitis is also associated with elevated C-reactive protein levels [27] , a phenotype for which common genetic risk factors for periodontitis and atherosclerosis have been discussed [28] . These findings indicate a possible mutual genetic basis underlying both diseases . Recently , four independent genome-wide association studies ( GWAS ) reported a strong association of a region of elevated linkage disequilibrium ( LD ) on human chromosome 9p21 . 3 , located upstream of the CDKN2A and CDKN2B genes [29]–[32] . The first two studies identified three individual SNPs ( rs238206 [29] , P = 6 . 7×10−6; rs10757274 [29] , P = 3 . 7×10−6 , and rs10757278 [30] , P = 1 . 5×10−7 ) , which were in high LD in HapMap [33] CEU ( r2≥0 . 86 ) . Two subsequent GWA studies [31] , [32] identified an association of rs1333049 ( P = 1 . 79×10−14 and P = 3 . 4×10−6 , respectively ) which is in strong LD with rs10757278 in HapMap CEU and which is flanking the proximate border of the LD region ( Figure 1 ) . A subsequent meta-analysis in seven different populations confirmed the association of this region with CHD [34] , making it the best replicated genetic risk locus of CHD to date . To investigate whether this locus may also relate to the risk for periodontitis , we investigated a possible association of this LD region with aggressive periodontitis ( AgP ) , the most extreme form of periodontal diseases for which it is assumed that genetic factors play a greater role in the susceptibility than for chronic periodontitis [15] .
After verification of the CHD associations , we genotyped these SNPs in 159 German periodontitis patients with the most extreme phenotype of AgP , generalized AgP ( ≥50% bone loss at ≥7 teeth below the age of 35 ) , and 736 independent ethnically matched healthy controls ( Table 1 ) . All SNPs were associated with periodontitis , with a multiplicative OR of 1 . 42 ( 95% confidence interval 1 . 11–1 . 81 ) , P = 4 . 8×10−3 and 1 . 42 ( 1 . 11–1 . 81 ) , P = 5 . 2×10−3 for the two most significant SNPs rs1333042 and rs1333048 , respectively ( Table S2 , Table S3 ) . Next , we adjusted for the established AgP risk factors smoking , type 2 diabetes , and the potential confounder gender . Upon adjustment , the effect of the rare alleles of the analyzed markers remained highly significant and was similar to that seen in the unadjusted analysis ( Table 3 ) . We considered a full genotypic model as well as a dominant , a multiplicative , and a recessive model for the rare marker allele in a logistic regression analysis . Recessive and multiplicative models yielded P-values of similar magnitude for all three markers , with SNP rs1333048 showing the strongest association under the recessive model ( P = 6 . 9×10−4 , OR = 1 . 99 [95% confidence interval 1 . 33–2 . 94] ) . In line with this , genotypic ORs of SNP rs1333048 equalled 1 . 12 ( 0 . 70–1 . 80 ) for heterozygotes and 2 . 13 ( 1 . 31–3 . 50 ) for homozygotes , suggesting an underlying genetic model somewhere between the recessive and the multiplicative one . This is supported by the Akaike's Information Criterion ( AIC; Table S4 ) , which do not differ between the recessive and the multiplicative model by more than 3 . 0 for any of the SNPs . We replicated the associations in an independent population of 146 German periodontitis patients with the less severe AgP phenotype localized AgP ( ≥50% bone loss at 2–6 teeth below the age of 35 ) , and further 368 ethnically matched healthy controls . All SNPs were found to be significantly associated at both the multiplicative and the genotypic level . SNP rs1333048 was again most significant , with a recessive OR of 1 . 70 ( 95% confidence interval 1 . 06–2 . 68 ) , P = 2 . 5×10−2 ( Table S2 ) . After adjustment for the covariates smoking , type 2 diabetes and gender , all SNPs remained significant in the multiplicative genetic model , but not so under the recessive model . The smallest P-value was again observed for SNP rs1333048 ( Pmultiplicative = 5 . 9×10−3 ) ( Table 3 ) . However , genotypic ORs were similar , albeit slightly higher than those observed for generalized AgP ( ORhet = 1 . 56 [0 . 94–2 . 64] , ORhom = 2 . 30 [1 . 28–4 . 20] ) , and AIC values between the two models differed by less than 3 . 0 ( Table S4 ) , again suggesting a not completely autosomal-recessive model for the genetic effect of this SNP . The minor allele of rs1333048 has a frequency of 54% in the combined AgP case samples and of 45% in the combined controls ( Table S3 ) . These frequencies correspond to those found in this LD region by the WTCCC study ( 55% in cases , 47% in controls ) [31] and by the German myocard infarct study ( 54% in cases , 48% in controls , [32] ) . The analyzed LD region is defined by a region of moderate LD ( average D′ approximately 0 . 60 ) stretching at the 5′ site [34] ( Figure 1 ) . There , a second region of elevated LD , moderately in LD with the main CHD-associated LD region , harbours further CHD associated SNPs [32] , [34] ( Figure 1 ) . Three tagging SNPs ( rs7044859 , rs1292136 [recently renamed rs496892] , and rs7865618 ) were identified to be sufficient to characterize the main haplotypes of this region [34] . To test these SNPs for possible associations with AgP , we performed a two step case-control analysis . If a shared genetic cause for both diseases is assumed , association with the primary causal polymorphism will be consistent in clinical cohorts of related diseases , while SNP associations secondary to the main associated LD structure are much less likely to be consistently observed , due to different LD patterns in different cohorts . As with the GWA studies on CHD , in our study , the 3′ LD region gave stronger association signals than the second LD region . Among the three tagging SNPs , only SNP rs496892 was significant in both AgP populations after adjustment . Here , the OR was 1 . 36 ( 95% confidence interval 1 . 04–1 . 79 ) , P = 2 . 4×10−2 for generalized AgP , and 1 . 38 ( 95% confidence interval 1 . 02–1 . 86 ) , P = 3 . 6×10−2 for localized AgP ( Table 4 , Table S3 ) in the multiplicative model . Statistical evidence of the CHD association signals suggested that the main associated LD region alone did not fully explain the association with CHD [34] . To test this , we performed a haplotype analysis [35] and pooled the two AgP populations to increase statistical power . The analysis indicated that rs1333048 accounted for most of the association with periodontitis ( data not shown ) , similar to the genetic effect observed for the same haplotypic backgrounds in the CHD populations [34] . The analyzed LD region is further defined by a recombination hot spot 3′ to rs1333049 . ( Figure 1 ) . To our knowledge , there is no evidence in the literature for an association with CHD distal to rs1333049 [29]–[32] , [34] , [36] . Three studies demonstrated an association of rs10811661 with type 2 diabetes [37]–[39] ( T2D; 8 . 6 kb downstream of rs1333049; Figure 1 ) . However , no association of this variant was observed with coronary heart disease [36] . Because T2D is an established risk factor for periodontitis we tested the T2D associated SNP rs10811661 for an additional association with AgP . No association with neither AgP status , prior or after adjustment for smoking , diabetes and gender was detected ( Table 4 ) .
In this candidate gene association study we provide evidence for a shared association of the CHD high-risk locus on chromosome 9p21 . 3 with AgP . We selected SNPs which were located on the same LD region as the lead SNPs of previous GWA studies on CHD , by which they were flanked . We verified the selected SNPs for their association with CHD prior to the replication in the AgP populations . For the initial verification panel we used CHD cases with an age of disease onset <55 years , a particular young age for disease onset , for which it is expected that genetic factors make a particularly high contribution to disease development . The homozygote OR for the three analyzed SNPs ranged from 1 . 92 to 2 . 02 and were , thus , in a range similar to the homozygote ORs observed in the WTCCC CHD study ( OR = 2 . 07 ) . The replication panel of CHD patients was on average nine years older than the verification panel . Patient collections of older age at disease onset are generally considered to have a higher proportion of individuals who were more strongly exposed to environmental and/or behavioral risk factors than to genetic ones . In our study , the decreased homozygote ORs in the replication panel , which ranged from 1 . 59 to 1 . 74 for the three analyzed SNPs , could be due to this effect , although a simple overestimation of the effect size in the screening panel is an equally likely cause . The controls were ethnically matched with the CHD cases but younger in age . This problem was also encountered earlier by the WTCCC CHD study ( cases <66 years vs . two controls panels of 49 years and an age range of 18 to 69 years , respectively ) . As with the WTCCC study , we also could not adjust for the CHD specific risk factors as the necessary information was not available from the blood donors used as controls . However , the CHD association of this risk LD region appeared to be remarkably uniform in other replication studies and showed no evidence of gene-environmental interactions in that the associations did not appear to be modified by the common CHD risk factors: age gender , smoking history , hypertension , diabetes , obesity , or variations in LDL- , and HDL-cholesterol levels [40] . After the verification of the association of the selected SNPs with CHD , we showed that these variants were also associated with AgP in two independent populations . The robustness of these significant findings was supported by the fact that the inclusion of covariates such as smoking and gender did not notably alter P-values or effect sizes . In the explorative analysis we used a population with the most severe phenotype of periodontitis , generalized AgP . Similar to the early-onset CHD phenotype , this most severe form is suspected to be determined by genetic risk factors in particular . In this case panel , we matched the controls for an age older than the general age of onset of mild forms of periodontitis ( ∼60 years ) . We considered this matching as appropriate to minimize a possible stratification by individuals with undiagnosed periodontal diseases because less severe forms of periodontitis are not easily diagnosed at a young age and the information of the periodontal status of the controls was self-reported . Because the locus on chromosome 9p21 . 3 is associated with CHD , it could have an effect on survival . Hence , older controls may represent “survivors” and do not comprise the complete gene pool of the population . This could cause stratification . To assess this problem , we matched the controls in the replication panel for the age of the AgP cases to better mirror the gene pool of the average population . The independence of the association signals from the age of the controls in the replication further confirmed the robustness of the shared association of this locus in CHD and AgP . This robustness of the findings which indicate a shared susceptibility locus for CHD and AgP was also reflected in the similar frequency difference of the rare alleles in both the WTCCC study on CHD and the presented study on AgP , which both differed by approximately 10% between cases and controls . The observed association of a shared susceptibility locus for AgP and CHD suggests a shared genetic cause for these diseases which implies a partly overlapping genetic mechanism of disease development . However , it is likely that by hitherto unknown mechanisms , one disease may also have a direct causal influence on the susceptibility of the other . The mechanisms of these interactions await further elucidation . It can also be argued that some of the epidemiological associations between periodontitis and CHD can be explained by shared socioeconomic risk factors which promote the development of both diseases . If so , AgP cases would be likely to develop future CHD . Likewise , CHD case populations would comprise significant proportions of unphenotyped periodontitis patients . Whereas this might be true for the common forms of chronic periodontitis and late onset CHD , it is unlikely for the severe early onset forms of these diseases . To further limit the potential cause of stratification , we employed cases with a particularly young age and a strong phenotype for both diseases , where the effect of environmental or behavioural factors are limited , and who are considered to carry a strong genetic burden . To add evidence to a genetic rather than a socioeconomical association , the statistical data of the risk LD regions did not appear to be modified by the common environmental and behavioural risk factors known to increase the susceptibility for CHD and/or periodontitis . Although small compared to other association studies on complex diseases , the presented AgP population represents , to date and to our knowledge , the largest population of this phenotype worldwide . It could be argued that studies with such small sample sizes are prone to increased type I and type II errors . However , for the explorative study we had a statistical power of >90% to detect the observed genetic effects but a confirmation of the described association in further and larger AgP populations would be desirable . The nearest described protein coding genes within the region of our associated SNPs are the genes CDKN2A/2B , which are located >104 kb in 5′ direction and are not in LD with the analyzed haplotypes ( r2<0 . 15 ) . They encode inhibitors of the genes CDK4/CDK6 and are believed to play critical roles in cell proliferation and tumor suppression . A third gene , MTAP , is located further upstream in close proximity . Many studies also reported MTAP function as a tumor suppressor gene and described an inverse correlation between MTAP protein levels and the progression of various tumors [41]–[45] . Interestingly , the association of MTAP activity and interferon sensitivity has been reported [41] , [44] , [46] , indicating a possible role in immune response . Both analyzed risk LD regions do not map to a sequence of these genes , but are embedded in the large antisense noncoding RNA ( ncRNA ) ANRIL [47] , belonging to a class of genes thought to be part of the regulatory repertoire of the transcriptome [48] . This gene overlaps CDKN2B but does not share any coding nucleotide sequences . Interestingly , mRNA AF109294 , which is thought to encode a hypothetical MTAP fusion protein mRNA , also partially overlaps ANRIL , sharing two exons . Expression data suggested a coordinated transcriptional regulation of ANRIL , CDKN2A and CDKN2B [47] , and expression in tissues involved in atherosclerosis , like vascular endothelial cells , macrophages , and coronary smooth muscle cells , had been shown [40] . The function and mechanism of action for this ncRNA is yet unknown . Full length deletion of ANRIL was associated with cutaneous melanoma [47] and the CHD and AgP high-risk haplotype , overlapping exons 13–19 of ANRIL , is associated with diseases that also share a phenotype of abnormal cell proliferation . A shared function of the neighbouring genes CDKN2A/2B and MTAP is the regulation of cell proliferation/tumor suppression . It is tentative to speculate a mechanistic link between the overlapping disease phenotypes of impaired cell proliferation , and a potential transcriptional regulatory role of the ncRNA ANRIL . The interplay of these genes in the tissue specific regulation of cell proliferation could be mediated by this antisense RNA . It is intriguing that the CHD and AgP phenotypes share high-risk LD regions , whereas T2D has apparently independent risk variants within this region . Elucidation of the interplay of ANRIL transcript variants and their involvement in increased susceptibility to interactive diseases like CHD and periodontitis promises new insight into the underlying partially shared pathogenic mechanisms , and will open up new avenues in the understanding of the development of these complex common diseases .
The CHD patients comprised a population-representative collection of unrelated Germans ( Table 1 ) . They were recruited from Schleswig-Holstein , the northernmost region in Germany , through the population-based PopGen biobank [49] . In the recruitment area , all coronary angiograms of any of the five cardiac catheterization laboratories were screened . Study subjects were required to have coronary catheterization demonstrating significant CHD ( at least a 70% stenosis in one major epicardial coronary vessel ) . 1 , 104 cases had a diagnosed disease onset <55 years , of whom a subset of 596 individuals had suffered a myocardial infarction . The majority ( 90 . 3% ) had a history of severe CHD and had undergone a coronary revascularization procedure ( percutaneous coronary intervention or coronary artery bypass grafting ) . The controls were obtained from the Blood Service of the University Hospital Schleswig-Holstein . Information about the age and gender was available . Written informed consent was obtained from all participants and the recruitment and the experimental protocols were approved by the institutional ethics review board and data protection authorities . The AgP patients were recruited from througout Germany . Only patients of German ethnicity were included , determined by the location of both parental birthplaces . Prior to the study , the genetic sub-structure of the German population had been assessed [50] , indicating only negligible sub-structures and therefore allowing a joint analysis of all individuals . A further inclusion criterion was age at diagnosis ≤35 years . A set of full-mouth dental radiographs was available for confirmative periodontal bone scoring , ≥2 teeth with ≥50% periodontal bone loss was defined as inclusion criterion . The sub-phenotype of localized AgP was characterized by ≥50% bone loss at 2–6 teeth; the sub-phenotpye generalized AgP was characterized by ≥50% bone loss at ≥7 teeth . The ethnically matched controls used for generalized AgP association study were randomly identified on the basis of the local population registry and the controls used for localized AgP were obtained from the Blood Service of the University Hospital Schleswig-Holstein . All controls underwent an additional physical examination at the PopGen facilities to obtain information on general health status . Information on the oral health status and physical risk factors ( e . g . smoking , diabetes ) was obtained from questionnaires completed during medical consultation . Additionally , a clinical checkup was subsequently performed . All controls self reported to be free of periodontitis . Genomic DNA was extracted from blood samples ( Invisorb Blood Universal Kit , Invitek , Berlin , Germany ) and amplified by whole genome amplification ( GenomiPhi , Amersham , Uppsala , Sweden ) . Genotyping was performed using the SNPlex and TaqMan GenotypingSystem ( Applied Biosystems , Foster City , CA , USA ) on an automated platform , employing TECAN Freedom EVO and 96-well and 384-well TEMO liquid handling robots ( TECAN , Männedorf , Switzerland ) . Genotypes were generated by automatic calling using the Genemapper 4 . 0 software ( Applied Biosystems ) with the following settings: sigma separation >6 , angle separation for 2 cluster SNPs <1 . 2 radians , median cluster intensity >2 . 2 logs . Genotypes were additionally reviewed manually and call rates >95% in each sample set were required . Power calculations were performed using PS Power and Sample Size Calculations [51] . Markers were tested for deviations from Hardy-Weinberg equilibrium in controls before inclusion in the analysis ( http://ihg2 . helmholtz-muenchen . de/cgi-bin/hw/hwa2 . pl , α = 0 . 05 ) . Single-marker case-control analysis was performed using Haploview v4 . 0 [52] , PLINK v2 . 049 [53] , and FamHap [35] . LD measures were plotted with the GOLD program [54] . We assessed the significance of associations with or between single-locus genotypes using χ2 and Fisher's exact tests for 2×2 and 2×3 contingency tables where applicable . Logistic regression analysis was performed in R v2 . 7 . 2 [55] . Significance was assessed by a Wald test and by a likelihood-ratio test . | Coronary heart disease ( CHD ) and periodontitis are the most widespread diseases in the Western industrialized world and pose a substantial health threat to populations worldwide . CHD is a leading cause for premature death , and periodontitis is the major cause for tooth loss in adults over 40 years . Both diseases are associated with similar risk factors such as smoking , diabetes , and gender , and both diseases are further characterized by a chronic inflammatory process . In the last year , several genome studies have identified a region of the human genome near the CDKN2A and CDKN2B genes as having an influence on CHD . We show that this genetic region , being the most important susceptibility locus for CHD to date , is also associated with a substantial risk increase of aggressive periodontitis . The associated genetic region maps to a genomic region that codes for an “antisense RNA , ” which partly overlaps regulatory and coding sequences of genes CDKN2A/CDKN2B . The interplay between these common inflammatory complex diseases could be partially due to the shared genetic risk variants of this antisense RNA . | [
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] | 2009 | Identification of a Shared Genetic Susceptibility Locus for Coronary Heart Disease and Periodontitis |
We propose an integrative , mechanistic model that integrates in vitro virology data , pharmacokinetics , and viral response to a combination regimen of a direct-acting antiviral ( telaprevir , an HCV NS3-4A protease inhibitor ) and peginterferon alfa-2a/ribavirin ( PR ) in patients with genotype 1 chronic hepatitis C ( CHC ) . This model , which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients , prospectively predicted sustained virologic response ( SVR ) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations . The model explains the clinically-observed responses , taking into account the IC50 , fitness , and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses , which result in different eradication times of each variant . The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents .
Chronic hepatitis C ( CHC ) affects approximately 180 million people worldwide and is a frequent cause of increased risk for hepatic fibrosis , cirrhosis , hepatic failure , and hepatocellular carcinoma [1] , [2] , [3] , [4] . The treatment objective for CHC is SVR , or viral eradication , which is considered to be a virologic cure of the infection . The previous treatment for patients with genotype 1 CHC , 48 weeks of therapy with PR ( PR48 ) ; results in SVR for 42%–50% of treatment-naïve patients [5] , [6] . In clinical trials , a combination therapy of telaprevir and PR treatment ( TPR ) achieved potent antiviral activity and higher SVR rates compared to treatment with PR alone [7] , [8] , [9] , [10] , [11] , [12] , [13] . As a consequence of its high replication rate and its error-prone polymerase , the HCV population in a patient exists as quasispecies . At the start of treatment with direct-acting antivirals such as telaprevir , the HCV population must be considered as a mixed population , consisting predominantly of wild-type HCV ( WT ) and a small population of HCV variants with varying levels of resistance to telaprevir . The resistant variants generally exist at a lower frequency than WT prior to the start of treatment [14] because they are less fit ( have lower replicative capacity ) [15] , [16] , [17] , [18] , [19] . Variants with lower-level resistance ( 3 to 25-fold increase in telaprevir IC50 in vitro: V36A , V36M , R155K , R155T , T54A , A156S ) have higher fitness than variants with higher-level resistance ( 25-fold increase in telaprevir IC50 in vitro: A156T , A156V , V36M/R155K ) [18] . These variants retain sensitivity to PR treatment in vitro [20] and in patients [16] , [21] , [22] . WT virus was eliminated more rapidly in the presence of telaprevir than with interferon-based regimens alone in clinical trials [23] , [24] . The treatment duration required to achieve SVR is based on the time to eradicate all HCV , including WT and all variants . For PR treatment , models of viral dynamics have successfully predicted SVR rates by calculating the percentage of patients whose on-treatment HCV RNA levels reach the viral eradication limit [25] , [26] , [27] . For TPR treatment , because of the presence of multiple variants in the quasispecies , the time when the level of each variant within a patient reaches the viral eradication limit may vary depending on the variant's fitness and resistance , and individual patient responses to treatment . The importance of these different eradication times to treatment strategies has not been elucidated . Here , we describe a viral dynamic model of response to TPR treatment . The model incorporates the presence of viral variants of differing degrees of resistance and fitness , and the diversity in patient responses to treatment . The viral dynamic model was improved from the previously published model [18] , with 2 novel features: 1 ) the model integrated TPR pharmacokinetics into viral dynamics , and 2 ) viral dynamic parameters were estimated using a population-approach method . The model was developed using in vitro and clinical data in early studies obtained from 28 patients treated with 2 weeks of telaprevir monotherapy and 478 treatment-naïve patients treated with PR and TPR regimens . Model predictions were evaluated from the outcome data of 2380 patients . Eradication of each viral variant was simulated as a discrete event occurring at variable times during treatment: when eradicated , variants were assumed to stop replicating . If eradication of all viral variants within a simulated patient was achieved , the patient was deemed to have reached SVR .
Model parameters were estimated from HCV RNA and drug concentration data from 478 patients who participated in early phase clinical studies ( study regimens are described in Supplementary Table S1 ) . The goodness-of-fit plot was provided in Supplementary Figure S1 and examples of fits in representative patients were provided in Supplementary Figure S2 . The estimated parameters were provided in Supplementary Table S2 . The estimated replicative fitness among all the variants ( Figure 1 ) showed that the R155K variant has the highest fitness ( with estimated fitness of about 50% of WT fitness ) , and the A156T variant has the lowest fitness ( with estimated fitness of about 10% of WT fitness ) . Some lower-level telaprevir resistant variants ( R155K , V36M , and V36A ) had higher fitness than the higher-level telaprevir resistant variants ( V36M/R155K , A156T ) . The other lower-level telaprevir resistant variants ( A156S , R155T , and T54A ) had lower fitness than the higher-level telaprevir resistant variants . The individual contributions of telaprevir and PR to antiviral blockage and infected-cell clearance rates estimated from treatment-naïve population are provided in Table 1 . Telaprevir contribution to production blockage ranged from −2 . 51−log10 to −2 . 27−log10 for WT and lower-level telaprevir resistant variants and −0 . 01−log10 to 0 . 00−log10 for higher-level telaprevir resistant variants , while PR treatment contributed −1 . 09−log10 for all variants . Compared to WT , lower-level telaprevir resistant variants have similar median blockages but reduced blockage in the extreme ( 95th percentile ) , which occurred in patients with lower telaprevir concentrations . Infected-cell elimination rates were higher for WT and lower-level telaprevir resistant variants ( 0 . 62 d−1 ) than for higher-level telaprevir resistant variants ( 0 . 29 d−1 ) . The higher elimination rates were mainly driven by higher antiviral blockage against WT and lower-level telaprevir resistant variants by telaprevir than by PR . These results suggest that the primary role of telaprevir is to block viral replication of WT and lower-level telaprevir resistant variants , and the primary role of PR is to block viral replication of higher-level telaprevir resistant variants . The model prediction capability was validated by comparing predicted and observed SVR rates in study regimens in which on-treatment data were used to estimate the model parameters ( 478 patients ) and in which the model was used strictly in prediction mode ( 2380 patients , Supplementary Table S1 ) . Predicted SVR rates were generated based on these inputs: ( a ) simulated drug concentrations and HCV RNA dynamics using parameter values re-sampled from the estimates; ( b ) the actual number of patients treated; ( c ) the number of patients who prematurely discontinued treatment; ( d ) the number of patients who failed to reach SVR because of other reasons ( lost to follow-up , noncompliance , and withdrawal of consent ) ; ( e ) the timing of treatment discontinuations; and ( f ) the distribution of HCV genotype ( 1a and 1b ) for each regimen/patient population . Figure 2 shows the correspondence between observed and predicted SVR rates . In the early studies in which the on-treatment data were used to develop the model , all observed SVR rates were within the 90% confidence intervals ( CIs ) of the predicted rates . In subsequent studies , observed SVR rates were also consistent with predicted rates . In the subsequent Phase 2 studies , the majority of the observed SVR rates ( 13/14 treatment groups ) were within the 90% CI bounds of the predicted rates; the other group had a rate within 3% of the nearest 90% CI bounds . In the Phase 3 treatment-naïve Studies ADVANCE and ILLUMINATE , the observed rates were within the 90% CI bounds in 4/5 groups; the other group had an observed rate that was 1% of the nearest CI bounds . In the Phase 3 treatment-experienced Study REALIZE , the observed SVR rates were all lower ( by up to 7% ) than the 90% CI lower bounds of the predicted rates . The discrepancy was greatest in the prior PR48-nonresponder population . In all regimens in this study , observed SVR rates were lower than predicted rates; therefore , the comparison of rates among regimens within the study is comparable between observed and predicted rates . Despite being trained only for the treatment-naïve population , the model produced consistently predictive results even for different patient populations such as prior PR48-nonresponders and prior PR48-relapsers . The predicted SVR rates by prior PR48 responses were calculated from a subset of simulated treatment-naïve patients by classifying these patients based on their simulated HCV RNA dynamics in response to PR48 treatment , using the standard definition of PR responsiveness: prior PR48-SVR , if patients would reach SVR with PR48 treatment; prior PR48-relapser , if patients have undetectable HCV RNA at the end of PR48 treatment but not reached SVR; prior PR48-partial responder , if patients have more than 2-log10 HCV RNA decline at week 12 but detectable HCV RNA throughout PR48 treatment , prior PR48-null responder , if patients have less than 2-log10 HCV RNA decline at week 12 during PR treatment . Using the assumption that each subgroup of prior PR responses was a narrower subset of the diverse PR responsiveness of treatment-naïve population , the model was able to predict the observed higher SVR rates in prior PR48-relapser and lower SVR rates in prior PR48-nonresponders compared to rates in treatment-naïve patients . To examine how viral eradication is affected by variant fitness , resistance , antiviral inhibition of each drug in the combination regimen , and patients' diversity in responses to treatment , simulations were performed for patients with 3 levels of PR responsiveness treated with 12 weeks of telaprevir in combination with 48 weeks of PR ( T12PR48 , Figure 3 ) : 1 ) typical patient who would achieve SVR if treated with PR48 ( left panel ) , 2 ) typical prior PR48-relapser ( middle panel ) , and 3 ) typical prior PR48-null-responder ( right panel ) . Simulated patients were assumed to have subtype 1a or 1b infection to provide a representative illustration . These simulations illustrate only representative examples with median responses , as there is variable PR responsiveness even within each respective group of prior PR response ( the predicted SVR rates by groups of prior PR responses are provided elsewhere [28] ) . Patients in each HCV subtype were assumed to have the same set of major variants: for subtype 1a: WT , R155K , V36M/R155K , and A156T; for subtype 1b: WT , V36A , A156T; variants with intermediate fitness or resistance were not included ( see methods ) . The PR responsiveness of the first 2 simulated patients with subtype 1a succeeded in eliminating all variants , but that of the last patient failed to eliminate the higher-level telaprevir resistant variant V36M/R155K . Both WT and the lower-level variant R155K were eliminated by about 6 weeks of telaprevir treatment in these 3 patients; however , the higher-level telaprevir resistant variant V36M/R155K was eliminated only in patients with better PR responsiveness ( the first 2 simulated patients ) . In contrast , the 3 simulated patients with subtype 1b were able to reach eradication because the PR responsiveness of these patients overcame the relatively poor fitness of A156 variants ( V36M/R155K variants were not present at baseline in the subtype 1b patients ) . The simulation above illustrates that the variability in PR responsiveness affects the time needed to eradicate higher-level telaprevir resistant variants . For these 3 simulated patients , the time to eradicate was similar for WT and lower-level telaprevir resistant variant R155K . However , the time to eradicate higher-level telaprevir resistant variants differed by PR responsiveness: for variant A156 , eradication times were 8 , 11 , and 13 weeks for the 3 patients; for variant V36M/R155K , the eradication time was 5 and 8 weeks for the first 2 patients , and was undefined in the last patient ( because this variant was never eradicated ) . For the simulated null-responder patient ( which as noted above , represents a median response for the null responder population ) , the increase in the level of V36M/R155K resulted in re-generation of R155K variant after completion of 12-week telaprevir treatment , resulting in a viral population with R155K-dominant quasispecies at week 48 ( because of the higher fitness of R155K compared to V36M/R155K ) . However , a telaprevir duration longer than 12 weeks would also result in virologic failure but with different predominant variant in the quasispecies when failure is detected ( V36M/R155K variant predominant instead of R155K variant predominant ) . To examine the contribution of the eradication assumption—that a variant stops replicating when its level is below the eradication limit—a simulation was performed with and without the eradication assumption . In the simulation without eradication , all variants were allowed to continue replicating even when their levels were below the eradication limit . The simulations were performed for simulated patients with 2 levels of PR responsiveness treated with T12PR: 1 ) typical treatment-naïve patient ( Figure 4 left panels ) , and 2 ) typical patient who would not reach SVR with PR48 treatment ( Figure 4 right panels ) . In the typical treatment-naïve patient , the predicted outcomes were the same with and without the eradication assumption: Week 48 HCV RNA levels were below the eradication limit . However , for the patient who failed to reach SVR with PR48 treatment , the outcomes differed . The dynamics in the first 12 weeks were the same: WT and lower-level telaprevir resistant variant levels reached the eradication limit by week 6 . With the eradication assumption , the quasispecies left were residual higher-level telaprevir resistant variants with reduced fitness that continued to be eliminated by PR treatment , resulting in a Week 48 HCV RNA level below the eradiation limit . Without the eradication assumption , the WT HCV RNA level returned back to the baseline value around week 24 after the level reached the eradiation limit during the first 12 weeks of TPR treatment ( telaprevir was only administered in the first 12 weeks ) . The return of HCV RNA levels after the completion of 12 weeks of telaprevir treatment with quasispecies predominately WT is rarely observed in clinical trials [8] , [10] , [29] , supporting the eradication assumption . The predicted treatment outcomes with and without the eradication assumption for a population of simulated treatment-naïve patients completing a T12PR24 regimen are shown in Figure 5 . Virologic outcomes were categorized as virologic failure at weeks 1–12 when TPR treatment was administered; virologic failure at Weeks 13–24 when PR treatment was administered; virologic failure after Week 24 when no treatment was administered ( relapse ) ; and SVR . Comparing simulations with and without the eradication assumption , the largest difference was observed for virologic failure between Weeks 13–24: 4 . 4% with eradication and 16 . 5% without eradication . The virologic failure rate with the eradication assumption is more consistent with rates observed in clinical trials ( see discussion ) , supporting the eradication assumption .
An integrated model of viral dynamic responses to treatment with telaprevir and PR has been developed and validated by comparing predictions against observed outcomes in late-phase clinical trials . It provides a framework to integrate multi-faceted information related to this novel CHC regimen , including in vitro resistance and fitness , pharmacokinetics , viral sequencing , and viral dynamics . The framework supported decisions pertaining to treatment strategies and optimizing regimens during clinical development . The model that was based on data from early-phase trials was predictive of observed SVR rates in subsequent studies that were not used in model building . The model also aided understanding of a novel CHC treatment regimen consisting of telaprevir and PR . It provided a consolidated picture of the interplay between the fitness and resistance of variant populations , antiviral inhibition by telaprevir and by PR treatment , and patient diversity in PR responsiveness , and connected these factors to the ultimate treatment outcome of SVR . The model suggested that the primary role of telaprevir in a TPR regimen is to eradicate WT and lower-level telaprevir resistant variants , and the complementary role of PR is to eradicate higher-level telaprevir resistant variants . Accordingly , virologic failure during the telaprevir-treatment phase has been associated predominately with higher-level telaprevir resistant variants , indicating a failure of PR to inhibit higher-level telaprevir resistant variants in some patients [9] , [29] . Modeling results and analysis of viral populations derived from patients who stopped treatment prior to viral eradication [28] have led to the working hypothesis that a successful regimen should have ( 1 ) a telaprevir treatment duration sufficient to eradicate WT and most lower-level telaprevir resistant variants , and ( 2 ) a PR treatment duration sufficient to eradicate any remaining variants , particularly higher-level telaprevir resistant variants . Once WT and lower-level telaprevir resistant variants have been eradicated and higher-level telaprevir resistant variants are the dominant residual viral population , telaprevir adds no additional antiviral effect . The PR duration required to eradicate higher-level telaprevir resistant variants depends greatly on the PR responsiveness of a given patient and likely the number of residual higher-level telaprevir resistant variants . Because higher-level telaprevir resistant variants pre-exist at lower frequency than WT and have reduced fitness , a greater percentage of patients can be treated with a shorter duration of PR treatment in the TPR regimen than in the PR regimen . The personalization of PR durations for patients treated with T12PR treatment has been demonstrated in those who achieved early virologic response in clinical trials [11] , [12] . Data and modeling analyses suggest different eradication times for variants with varying fitness and resistance , leading to different optimal treatment durations of telaprevir and PR treatment . Modeling analysis showed that a higher percentage of patients would be expected to have virologic failure during PR treatment after the completion of 12 weeks of telaprevir treatment if simulated without viral eradication , a phenomenon that has rarely been observed in clinical trials: the virologic failure rates after 12-week of telaprevir treatment in treatment-naïve patients were 1% for the T12PR24 arm of Study PROVE2 [8] and 4 . 4% in the T12PR24-48 arms of ADVANCE [28] , [29] . Moreover , the shorter eradication times of sensitive variants as compared to resistant variants are also consistent with the observed more rapid elimination of WT HCV in patients dosed with telaprevir as compared to those typically observed in PR treatment [23] , [24] . The model produced consistently predictive results for different prior PR48-treatment-failure populations despite being trained only for the treatment-naïve population . This finding supports the hypothesis that a treatment-naive population contains several types of patients with differing PR responsiveness , and suggests that a model estimated from the treatment-naive population can be used to predict results for populations with different PR responsiveness . In the 2 studies in the treatment-experienced population ( Studies PROVE3 and REALIZE ) , the predicted and observed SVR rates were generally consistent: comparable SVR rates in PROVE3 and slightly higher predicted SVR rates compared to those rates observed in REALIZE . The discrepancy is greatest in the prior nonresponder population . The discrepancy in the REALIZE study may arise from a limitation of the model: that the underlying parameters constituting PR responsiveness were assumed to be continuously distributed in treatment-naïve population , while the actual parameters may be more discrete and based on other factors such as the IL28B genotypes [30] , which has been reported to produce different viral dynamics in response to PR treatment [31] , [32] . Alternatively , the discrepancy may be attributed to a higher proportion of patients with adverse prognostic factors for achieving SVR ( e . g . , advanced liver disease ) enrolled in REALIZE , whereas the predictions were generated from the dataset that contained treatment-naïve patients with fewer of these adverse factors . In the modeling described here , adverse factors were not formally examined as covariates because of the limited data available from the early studies . In summary , the proposed model served as a framework in integrating information from multiple sources and was useful in supporting decision-making for the optimization of treatment strategies during clinical development . The model provided insights to help design novel treatment regimens of combination therapy with telaprevir , peginterferon alfa-2a and ribavirin for CHC treatment , and may be useful for evaluating future CHC treatment regimens that include direct-acting antiviral agents .
The study protocols and informed consent forms were reviewed and approved by ethics committees or institutional review boards for each clinical research site before initiation of studies at that site . Written informed consent was obtained in accordance with the Declaration of Helsinski . The model was developed from HCV RNA and drug concentration from a total of 478 patients treated with PR and TPR regimens in early studies of telaprevir . The model was validated using outcomes from 2380 patients in later studies . The list of studies is provided in Supplementary Table S1 . The study design , enrollment criteria , and primary results have been published elsewhere [7] , [8] , [9] , [10] , [11] , [12] , [13] , [33] . Only quantifiable HCV RNA data were used in the estimation . Additional limitations were implemented: 1 ) for PR regimens , only HCV RNA data up to time when the first dose modifications of either peginterferon or ribavirin were used ( or end of the treatment ) to evaluate the PR responses with one dose level; and 2 ) for TPR regimens , only patients with WT-dominant quasispecies ( 98% of patients ) were included because few patients ( 2% ) had resistant-variant dominant quasispecies . While the model can be applied to the patients with resistant-variant dominant quasispecies , the small number of patients in this dataset prevented us from making accurate conclusion regarding the comparability of the fitness of resistant variants in these patients to those in patients with WT-dominant quasispecies . The model structure is given in Equations 1–8 , and the descriptions of symbols are given in Table 2 . Drug pharmacokinetics were estimated from time-concentration data in early studies . Telaprevir and peginterferon alfa-2a pharmacokinetics were described by one-compartmental models and provided in Equation 8 . Ribavirin pharmacokinetics were described by a 3-compartmental model , with parameters estimated using empirical Bayesian feedback from published distributions of parameter estimates [34] . Model-predicted drug concentrations were simulated based on the dosing records and pharmacokinetic model parameters and were entered into the viral dynamic model . ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) A schematic of the viral dynamic model is provided in Figure 6 . Viral populations were represented as a mixture of quasispecies with varying fitness and sensitivities to telaprevir . Variant V represents a virion with characterized amino-acid substitution ( s ) in the NS3/4A protease . Variant Vi infects target cells ( T ) to form V-infected cells ( I ) at rate βTV . Each variant competes for the same target cells T . Target cells T also represent limited “replication space” shared by all variants; target cell T has a synthesis rate s and a first-order elimination d . In [18] , a model with different representation of T ( which maintain T+I ) resulted in comparable estimates . The maximum target cells were assumed to be 1011 [35] . Each infected cell ( I ) produces a population of variants at production rate pf , with a m-fraction of this production mutating to produce variant j ( V ) . The mutation rate was assumed to be 1 . 2 10−4 base−1 cycle−1 [36] . The production rate ratio ( f ) quantifies variant replicative fitness in the absence of any drug . Different production rates ( pf ) , but the same infection ( β ) and clearance ( c ) rates , are assumed for different variants . This assumption is consistent with the function of the NS3/4A protease to cleave a precursor polyprotein as a crucial step in the HCV replication cycle [37] . Each drug ( telaprevir , peginterferon alfa-2a , ribavirin ) assumes a dual role in clearing HCV . First , each drug blocks viral production by a factor ( 1- ε ) . Telaprevir antiviral blockage εi , T is constrained to be consistent with in vitro sensitivity assay of variant i to telaprevir [38] , [39] , [40] . Blockage by peginterferon alfa-2a and ribavirin are assumed to be equal among variants , consistent with in vitro sensitivity assay . While the antiviral mechanism of ribavirin ( of whether ribavirin blocked viral production or changed infectious into noninfectious viral strains ) remained controversial , our data were unable to distinguish a model with a simple production blockage from a model with infectious and noninfectious viral strains [25] , and therefore , a simpler model with production blockage was chosen instead of the alternative model because the alternative model would need twice as many number of variants . The blockage factors were calculated as a function of plasma concentrations of each drug ( multiplied by a factor κ to convert plasma to effective concentrations ) , and the sensitivities of each variant as measured in HCV replicon cells ( represented by parameters IC50 , and hill-power values h ) . Overall blockage in the combination regimen assumed additive ( in logarithmic scale ) blockages of each drug . The second role of each drug is to enhance the infected-cell clearance δ . WT δWT values were up to 10-times higher in patients dosed with telaprevir than in patients treated with interferon-based regimen alone [41] , [42] . These observations were represented into the model by assuming that δ increased proportionally with log10 ( 1-ε ) [18] . The enhanced δ may be attributed to increases in infected-cell clearance or uncovering of intracellular viral RNA [27] . Consistently , as these mechanisms may not be specific to direct-acting antivirals , the enhancement may also be observed for interferon if its effectiveness is high enough . The HCV variants used in this model was based on the major variants detected in clinical studies: one major variant with the highest fitness for each of the resistant groups ( lower-level and higher-level resistance ) and the nucleotide changes from WT . Subtypes 1a and 1b were modeled separately because when telaprevir was administered in monotherapy , different sets of resistant variants emerged [15] , [16] . All patients with the same subtype were assumed to have the same set of major variants: for subtype 1a: WT , R155K , V36M/R155K , and A156T/V; for subtype 1b: WT , V36A , A156T/V . The frequency of these variants prior to treatment was calculated by assuming a steady-state condition . The intermediate-resistant variants R155T/I and other minority variants observed in a few patients were not included in the model used to generate predictions because of lack of data to estimate their fitness . Including these variants in the model was expected to result in only small changes in the SVR rates , because these variants appeared to be less fit than the variants used in the model [18] . The parameters related to the antiviral activity of peginterferon alfa and ribavirin were correlated because the current training dataset contained data from regimens where peginterferon and ribavirin were administered simultaneously . Because of the data limitation , the proportionality constant related to the enhanced infected-cell clearance for ribavirin is assumed to be equal to the constant for peginterferon . SVR rates were predicted by evaluating simulated HCV RNA dynamics and entering the observed patient disposition into the model . The predicted HCV RNA dynamics for treatment-naïve patients were generated by simulations , with parameters re-sampled from the distributions of estimates in Supplementary Table S2 , truncated by lower and upper bounds ( bounds were obtained from the extreme values of the observed individual estimates ) . Dosing compliance was assumed to be 100% . Ribavirin dose modification followed the observed modification in the training dataset . A simulated patient was considered to achieve eradication ( or SVR ) if the overall HCV RNA level by the end of treatment was below 1 copy in the body [25] ( or reached a 12-log decline from baseline in HCV RNA , assuming a baseline value of 107 IU/mL ) . Predicted SVR rates for different categories of PR responsiveness ( SVR with PR48 , prior PR48-non-SVR , prior PR48-relapser , prior PR48-nonresponder , prior PR48-null responder ) were generated by simulating HCV RNA dynamics to PR48 treatment , and by filtering the responses with the respective PR responsiveness criteria . The categories of PR responsiveness followed these criteria: SVR with PR48 , if patient's viral load reached eradication by the end treatment; prior PR48-non-SVR , if patient's viral load did not reach eradication by the end of treatment; prior PR48-relapser , if patient's viral load was undetectable by the end of treatment but did not reach eradication; prior PR48-nonresponder , if patient's viral load was always detectable during treatment; prior PR48-null-responder , if patient's viral load at week 12 declined <2−log10 . Drug concentrations were estimated or simulated using a Bayesian approach implemented in NONMEM version 6 . Viral dynamic model was implemented in Jacobian® software version 4 . 0 ( RES group , Inc . , Cambridge , MA ) . | Hepatitis C virus chronically infects approximately 180 million people worldwide . The treatment aim for patients chronically infected with hepatitis C is viral eradication or sustained viral response ( SVR ) . Historical standard of care for HCV treatment was peginterferon-alfa and ribavirin . Recently , approved HCV protease inhibitors , in combination with peginterferon-alfa and ribavirin , have demonstrated higher SVR rates compared to peginterferon-alfa and ribavirin alone . As members of a novel class of compounds directly targeting hepatitis C virus , HCV protease inhibitors have different mechanisms of actions and are affected by resistance and fitness of HCV variants . The significance of these different mechanisms of action , and the interplays between resistance and viral fitness to the treatment outcome has not been elucidated . Here , we developed and validated an integrative , mechanistic model of viral dynamics in response to a combination regimen including telaprevir , peginterferon-alfa , and ribavirin . The model was developed from early studies in 478 treatment-naïve patients and its SVR rate predictions were verified in 2380 patients in subsequent studies . These results provide an example of the use of mechanistic information to the development of viral dynamic model that has been useful in the design of optimal treatment regimens . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"bioengineering",
"medicine",
"hepatitis",
"c",
"biological",
"systems",
"engineering",
"infectious",
"diseases",
"biotechnology",
"hepatitis",
"viral",
"diseases",
"engineering"
] | 2012 | A Viral Dynamic Model for Treatment Regimens with Direct-acting Antivirals for Chronic Hepatitis C Infection |
Recent epidemics of Zika , dengue , and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae . albopictus mosquitoes . We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika , chikungunya , and dengue change with mean temperature , and we show that these predictions are well matched by human case data . Across all three viruses , models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C . Controlling for population size and two socioeconomic factors , temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence . Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission , but transmission in temperate areas is limited to at most three months per year even if vectors are present . Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones .
Epidemics of dengue , chikungunya , and Zika are sweeping through the Americas , and are part of a global public health crisis that places an estimated 3 . 9 billion people in 120 countries at risk [1] . Dengue virus ( DENV ) distribution and intensity in the Americas has increased over the last three decades , infecting an estimated 390 million people ( 96 million clinical ) per year [2] . Chikungunya virus ( CHIKV ) emerged in the Americas in 2013 , causing 1 . 8 million suspected cases from 44 countries and territories ( www . paho . org ) . In the last two years , Zika virus ( ZIKV ) has spread throughout the Americas , causing 764 , 414 suspected and confirmed cases , with many more unreported ( http://ais . paho . org/phip/viz/ed_zika_cases . asp , as of April 13 , 2017 ) . The growing burden of these diseases ( including links between Zika infection and both microcephaly and Guillain-Barré syndrome [3] ) and potential for spread into new areas creates an urgent need for predictive models that can inform risk assessment and guide interventions such as mosquito control , community outreach , and education . Predicting transmission of DENV , CHIKV , and ZIKV requires understanding the ecology of the vector species . For these viruses the main vector is Aedes aegypti , a mosquito that prefers and is closely affiliated with humans , while Ae . albopictus , a peri-urban mosquito , is an important secondary vector [4 , 5] . We expect one of the main drivers of the vector ecology to be the climate , particularly temperature . For that reason , mathematical and geostatistical models that incorporate climate information have been valuable for predicting and responding to Aedes spp . spread and DENV , CHIKV , and ZIKV outbreaks [5–10] . The effects of temperature on ectotherms are largely predictable from fundamental metabolic and ecological processes . Survival , feeding , development , and reproductive rates predictably respond to temperature across a variety of ectotherms , including mosquitoes [11 , 12] . Because these traits help to determine transmission rates , the effects of temperature on transmission should also be broadly predictable from mechanistic models that incorporate temperature-dependent traits . Here , we introduce a model based on this framework that overcomes several major gaps that currently limit our understanding of climate suitability for transmission . Specifically , we develop models of temperature-dependent transmission for Ae . aegypti and Ae . albopictus that are ( a ) mechanistic , facilitating extrapolation beyond the current disease distribution , ( b ) parameterized with biologically accurate unimodal thermal responses for all mosquito and virus traits that drive transmission , and ( c ) validated against human dengue , chikungunya , and Zika case data across the Americas . We synthesize available data to characterize the temperature-dependent traits of the mosquitoes and viruses that determine transmission intensity . With these thermal responses , we develop mechanistic temperature-dependent virus transmission models for Ae . aegypti and Ae . albopictus . We then ask whether the predicted effect of temperature on transmission is consistent with patterns of actual human cases over space and time . To do this , we validate the models with DENV , CHIKV , and ZIKV human incidence data at the country scale in the Americas from 2014–2016 . To isolate temperature dependence , we also statistically control for population size and two socioeconomic factors that may influence transmission . If temperature fundamentally limits transmission potential , transmission should only occur at actual environmental temperatures that are predicted to be suitable , and conversely , areas with low predicted suitability should have low or zero transmission ( i . e . , false negative rates should be low ) . By contrast , low transmission may occur even when temperature suitability is high because other factors like vector control can limit transmission ( i . e . , the false positive rate should be higher than the false negative rate ) . Finally , if the simple mechanistic model accurately predicts climate suitability for transmission , then we can use it to map climate-based transmission risk of DENV , CHIKV , ZIKV , and other emerging pathogens transmitted by Ae . aegypti and Ae . albopictus seasonally and geographically .
Data gathered from the literature [9 , 13–30] revealed that all mosquito traits relevant to transmission—biting rate , egg-to-adult survival and development rate , adult lifespan , and fecundity—respond strongly to temperature and peak between 23°C and 34°C for the two mosquito species ( Ae . aegypti in Fig 1 and Ae . albopictus in Fig A in S1 Text ) . DENV extrinsic incubation and vector competence peak at 35°C [31–37] and 31–32°C [31 , 32 , 34 , 38] , respectively , in both mosquitoes—temperatures at which mosquito survival is low , limiting transmission potential ( Fig 1 , Fig A in S1 Text ) . Appropriate thermal response data were not available for CHIKV and ZIKV extrinsic incubation and vector competence . We estimated the posterior distribution of R0 ( T ) and used it to calculate key temperature values that indicate suitability for transmission: the mean and 95% credible intervals ( 95% CI ) on the critical thermal minimum , maximum , and optimum temperature for transmission by the two mosquito species . At constant temperatures , Ae . aegypti transmission peaked at 29 . 1°C ( 95% CI: 28 . 4–29 . 8°C ) , and declined to zero below 17 . 8°C ( 95% CI: 14 . 6–21 . 2°C ) and above 34 . 6°C ( 95% CI: 34 . 1–35 . 6°C ) ( Fig 2 ) . Ae . albopictus transmission peaked at 26 . 4°C ( 95% CI: 25 . 2–27 . 4°C ) and declined to zero below 16 . 2°C ( 95% CI: 13 . 2–19 . 9°C ) and above 31 . 6°C ( 95% CI: 29 . 4–33 . 7°C ) ( Fig 2 ) . Overall , the thermal response curve for Ae . albopictus is shifted towards lower temperatures than Ae . aegypti , so Ae . albopictus transmission is better suited to cooler environments . For a more realistic scenario in which daily temperature ranged over 8°C , the transmission peak , minimum , and maximum were slightly lower for both Ae . aegypti ( 28 . 5°C , 13 . 5°C , 34 . 2°C , respectively ) and Ae . albopictus ( 26 . 1°C , 11 . 9°C , and 28 . 3°C , respectively ) . The lower thermal maximum under fluctuating temperatures occurs because we incorporated empirically supported irreversible lethal effects of temperatures that exceed thermal maxima for survival ( see Materials and Methods ) . The posterior distribution of R0 ( T ) allows us to evaluate uncertainty in key temperature values that define the transmission range , including critical thermal minimum , maximum , and optimum . Uncertainty was higher for the critical thermal minimum for transmission than for the maximum or optimum , and the two mosquito species overlapped most for this outcome ( Fig 2 , bottom panels ) . This result occurred because several trait thermal responses increase gradually from low to mid temperatures but decline more steeply at high temperatures ( Fig 1 ) , so uncertainty is greatest at low temperatures . Ae . aegypti has a substantially higher optimum and maximum temperature than Ae . albopictus ( Fig 2 ) due to its greater rates of adult survival at high temperatures ( see Supplementary Materials for sensitivity analyses ) . We used generalized linear models ( GLM ) to ask whether the predicted relationship between temperature and transmission , R0 ( T ) , was consistent with observed human cases of DENV , CHIKV , and ZIKV . Specifically , we assessed whether R0 ( T ) was an important predictor of the probability of autochthonous transmission occurring and of the incidence given that transmission occurred . We also controlled for human population size , virus species , and two socioeconomic factors . ( Note that we focused on testing the R0 ( T ) model , rather than on constructing the best possible statistical model of human case data . ) To do this , we used the version of the Ae . aegypti R0 ( T ) model that includes 8°C daily temperature range , along with country-scale weekly case reports of DENV , CHIKV , and ZIKV in the Americas and the Caribbean between 2014–2016 . We first addressed the fact that countries with larger populations have greater opportunities for ( large ) epidemics by creating two predictors that incorporate scaled R0 ( T ) and population size . In the models of the probability of autochthonous transmission occurring we used the product of the posterior probability that R0 ( T ) > 0 ( which we notate as GR0 ) and the log of population size ( p ) to give log ( p ) *GR0 . ( Here , and throughout , log denotes the natural logarithm . ) In the models of incidence , given that transmission does occur , we used the log of the product of the posterior mean of R0 ( T ) and population size , log ( p*R0 ( T ) ) . To control for several socioeconomic factors that might obscure the impact of temperature , we also included log of gross domestic product ( GDP ) and log of percent of GDP in tourism ( using logs because the predictors were highly skewed , to stabilize variance ) . These are potential indicators of investment in and/or success of vector control and infrastructure improvements that prevent transmission . By comparing models that included the R0 ( T ) metric alone , socioeconomic factors alone , or both , we tested whether R0 ( T ) was an important predictor of observed transmission occurrence and incidence ( see Table D in S2 Text ) . Note that R0 ( T ) is out of sample for all validation analyses because it is derived and calculated strictly from laboratory data on mosquitoes , and we perform a validation analyses for R0 ( T ) using independent case incidence reports . For this validation step we assessed model adequacy for the transmission data in two ways . First we used the full dataset for case incidence reports to select the best model ( Table D in S2 Text ) and to determine whether or not our predicted value of relative R0 ( T ) based on laboratory data was included in the model ( “within sample” analysis ) . Second we used a bootstrapping approach where models were fit on subsets of the case incidence data that were randomly sampled and then predictive accuracy of the competing models ( Table D in S2 Text ) was assessed on left-out data ( “out of sample” analysis ) . For the probability of autochthonous transmission occurring , the model that included both the R0 ( T ) predictor and socioeconomic predictors had overwhelming support based on Bayesian Information Criterion ( BIC; model PA5 relative probability = 1 , Table D in S2 Text ) . Based on deviance explained , the models that included R0 ( T ) , with or without the socioeconomic predictors out-performed the model that did not include R0 ( T ) ( Table D in S2 Text; Fig 3A , Fig B in S1 Text ) . In analyses of out-of-sample accuracy , models that included the R0 ( T ) metric ( with or without the socioeconomic factors ) were surprisingly accurate . They predicted the probability of transmission with 86–91% out-of-sample accuracy for DENV ( Table D in S2 Text ) . For CHIKV and ZIKV , models that included the R0 ( T ) metric or population alone had 66–69% out-of-sample accuracy ( Table D in S2 Text ) . There were no significant differences in out-of-sample accuracy between the top four models , but for both DENV and CHIKV/ZIKV the best model was significantly better than the worst model [see supplementary code in 39 for full results] . The lower out-of-sample accuracy for CHIKV and ZIKV likely reflects the much lower frequency of positive values and the lower total sample size of this dataset . All results were similar for a set of models that separated GR0 from population size , so for simplicity we show the model predictors that combines GR0 and population size here ( see Table D in S2 Text and [39] for results of other models ) . Further , from a biological perspective , the combined model better describes what we know about disease systems: if either the probability of R0 ( T ) being greater than zero is small or population size is very small , transmission is unlikely to occur . Together , these analyses suggest that R0 ( T ) is an important predictor of transmission occurrence , but that CHIKV and ZIKV need further data to better explain the probability of transmission occurrence ( Fig 3A , Fig B in S1 Text ) . R0 ( T ) was also an important predictor of incidence , given that autochthonous transmission did occur . Within-sample , incidence was best predicted by the model that included both R0 ( T ) and the socioeconomic predictors ( model IM5 in Table D in S2 Text ) based on BIC ( relative probability = 1 ) . The models that included R0 ( T ) out-performed those that did not based on deviance explained ( Table D in S2 Text ) . In out-of-sample validation , the models that included R0 ( T ) explained the magnitude of incidence based on mean absolute percentage error ( 85–86% accuracy versus 83% accuracy for models that did not include R0 ( T ) ; Table D in S2 Text ) , but this difference was not statistically significant . For illustration , we show the simpler model that only contains the R0 ( T ) predictor in the main text ( Fig 3B; model IM1 in Table D in S2 Text ) . Notably , the models that contained R0 ( T ) predicted incidence well for all three viruses , despite the lower incidence of CHIKV and ZIKV . Although predicted R0 ( T ) correlated with the observed occurrence and magnitude of human incidence for all three viruses , these observed incidence metrics were higher for DENV than for CHIKV and ZIKV . While the reason for this difference is unclear , the most likely explanation is that DENV is much more established in the Americas , so it is more likely to be detected , diagnosed , and reported . Because ZIKV and CHIKV are newly emerging , they may not have fully saturated the region at this early stage . The ability of the model to explain the probability and magnitude of transmission is notable given the coarse scale of the human incidence versus mean temperature data ( i . e . , country-scale means ) , the lack of CHIKV- and ZIKV-specific trait thermal response data to inform the model , the nonlinear relationship between transmission and incidence , and all the well-documented factors other than temperature that influence transmission . Together , these analyses show simple mechanistic models parameterized with laboratory data on mosquitoes and dengue virus are consistent with observed temperature suitability for transmission . Moreover , the similar responses of human incidence of ZIKV , CHIKV , and DENV to temperature suggest that the thermal ecology of their shared mosquito vectors is a key determinant of outbreak location , timing , and intensity . The validated model can be used to predict where transmission is not excluded ( posterior probability that R0 ( T ) > 0 , a conservative estimate of transmission risk ) . Considering the number of months per year at which mean temperatures do not prevent transmission , large areas of tropical and subtropical regions , including Puerto Rico and parts of Florida and Texas , are currently suitable year-round or seasonally ( Fig 4 ) . These regions are fundamentally at risk for DENV , CHIKV , ZIKV , and other Aedes arbovirus transmission during a substantial part of the year ( Fig 4 ) . Indeed , DENV , CHIKV , and/or ZIKV local transmission has occurred in Texas , Florida , Hawaii , and Puerto Rico ( www . cdc . gov ) . On the other hand , many temperate regions experience temperatures suitable for transmission three months or less per year ( Fig 4 ) . Temperature thus limits the potential for the viruses to generate extensive epidemics in temperate areas even where the vectors are present . Moreover , many temperate regions with seasonally suitable temperatures currently lack Ae . aegypti and Ae . albopictus mosquitoes , making vector transmission impossible ( Fig 4 , black line ) . The posterior distribution of R0 ( T ) also allows us to map months of risk with different degrees of uncertainty ( e . g . , 97 . 5% , 50% , and 2 . 5% posterior probability that that R0 > 0 ) , ranging from the most to least conservative ( Fig D in S1 Text ) .
Temperature is an important driver of—and limitation on—vector transmission , so accurately describing the temperature range and optimum for transmission of DENV , CHIKV , and ZIKV is critical for predicting their geographic and seasonal patterns of spread [12 , 41] . We directly estimated the temperature–transmission relationship using mechanistic transmission models for each mosquito species ( Fig 2 ) . These models are built using empirical estimates of the ( unimodal ) effects of temperature on mosquito and pathogen traits that drive transmission , including survival , development , reproduction , and biting rates ( Fig 1 , Fig A in S1 Text ) . Because these trait thermal responses are unimodal across the majority of ectotherm taxa and traits , and because the traits combine nonlinearly to drive transmission , the emergent relationship between temperature and transmission is difficult to infer directly from field data or from individual trait responses . Here , we present a model of temperature-dependent DENV , CHIKV , and ZIKV transmission that advances on previous models because it is mechanistic , fitted from experimental trait data ( Fig 1 , Fig A in S1 Text ) , and validated against independent human case data at a broad geographic scale ( Fig 3 ) . Mechanistic understanding is valuable for extrapolating beyond the current spatial and temporal range of transmission ( Fig 4 ) , as compared to environmental niche models , for example [5 , 42 , 43] . Of the six previous mechanistic temperature-dependent models of DENV , CHIKV , or ZIKV transmission by Ae . aegypti and Ae . albopictus that we were able to reproduce , three had similar thermal optima [7 , 44 , 45] while the other three had dramatically higher optima ( 3–6°C ) [9 , 46] ( Fig E in S1 Text ) . Two of the models were very similar to ours [44 , 45]; of the remaining four models , two predicted much greater suitability for transmission at low temperatures [46] and all four predicted greater suitability at high temperatures [7 , 9 , 46] ( Fig E in S1 Text ) . Only one of these previous models was ( like ours ) statistically validated against independent data not used to estimate model parameters , and its predictions were very similar to those of our model [44] . Other mechanistic and environmental niche models could not be directly compared with ours [5 , 10 , 41–43] , either because fully reproducible equations , parameters , and/or code were not provided or because their predicted marginal effects of temperature were not displayed . Visually , our maps are similar to maps based on a previous model of Ae . aegypti and Ae . albopictus persistence suitability indices [41] . Recent environmental niche models of Zika distribution have shown similar but more constrained predicted distributions of environmental suitability , in part because these models include not just temperature suitability but also further environmental , socioeconomic , and demographic constraints [5 , 42 , 43 , 47] . Even though the thermal response data are imperfect—for example , CHIKV and ZIKV thermal response data are missing—and the human case data are reported at a coarse spatial scale , the validation analyses suggest that R0 ( T ) is an important predictor of both the probability of transmission occurring and the magnitude of incidence for DENV , CHIKV , and ZIKV . This has several key implications . First , temperature-dependent transmission is pervasive enough to be detected at a coarse spatial scale . Second , dynamics of the mosquito predict transmission for a suite of Ae . aegypti-transmitted viruses , without additional virus-specific information . Third , climate and socio-economic factors combine to shape variation in incidence across countries . Finally , these simple predictors explain a substantial proportion of the variance in both the probability and intensity of transmission . Predicting arbovirus transmission at a higher spatial resolution and precision will require more detailed information on factors like the exposure and susceptibility of human populations , environmental variation ( e . g . , oviposition habitat availability , seasonal and daily temperature variation ) , and socioeconomic factors . However , as a first step our mechanistic model provides valuable insight because it makes broad predictions about suitable environmental conditions for transmission , it is mechanistic and grounded in experimental trait data , it is validated against independent human case data , and its predictions are applicable across three different viruses . Using these thermal response models as a scaffold , additional drivers could be incorporated to obtain more precise and specific predictions about transmission dynamics , which could in turn be used for public health and vector control applications . For this purpose , all code and data used in the models are available on Figshare [39] . The socio-ecological conditions that enabled CHIKV , ZIKV , and DENV to become the three most important emerging vector-borne diseases in the Americas make the emergence of additional Aedes-transmitted viruses likely ( potentially including Mayaro , Rift Valley fever , yellow fever , Uganda S , or Ross River viruses ) . Efforts to extrapolate and to map temperature suitability ( Fig 4 ) will be critical for improving management of both ongoing and future emerging epidemics . Mechanistic models like the one presented here are useful for extrapolating the potential geographic range of transmission beyond the current envelope of environmental conditions in which transmission occurs ( e . g . , under climate change and for newly invading pathogens ) . Accurately estimating temperature-driven transmission risk in both highly suitable and marginal regions is critical for predicting and responding to future outbreaks of these and other Aedes-transmitted viruses .
We constructed temperature-dependent models of transmission using a previously developed R0 framework . We modeled transmission rate as the basic reproduction rate , R0—the number of secondary infections that would originate from a single infected individual introduced to a fully susceptible population . In previous work on malaria , we adapted a commonly used expression for R0 for vector transmission to include the temperature-sensitive traits that drive mosquito population density [12]: R0 ( T ) = ( a ( T ) 2b ( T ) c ( T ) e−μ ( T ) /PDR ( T ) EFD ( T ) pEA ( T ) MDR ( T ) Nrμ ( T ) 3 ) 1/2 ( 1 ) Here , ( T ) indicates that the trait is a function of temperature , T; a is the per-mosquito biting rate , b is the proportion of infectious bites that infect susceptible humans , c is the proportion of bites on infected humans that infect previously uninfected mosquitoes ( i . e . , b*c = vector competence ) , μ is the adult mosquito mortality rate ( lifespan , lf = 1/μ ) , PDR is the parasite development rate ( i . e . , the inverse of the extrinsic incubation period , the time required between a mosquito biting an infected host and becoming infectious ) , EFD is the number of eggs produced per female mosquito per day , pEA is the mosquito egg-to-adult survival probability , MDR is the mosquito immature development rate ( i . e . , the inverse of the egg-to-adult development time ) , N is the density of humans , and r is the human recovery rate . For each temperature-sensitive trait in each mosquito species , we fit either symmetric ( Quadratic , -c ( T–T0 ) ( T–Tm ) ) or asymmetric ( Brière , cT ( T–T0 ) ( Tm−T ) 1/2 ) unimodal thermal response models to the available empirical data [48] . In both functions , T0 and Tm are respectively the minimum and maximum temperature for transmission , and c is a positive rate constant . We consider a normalized version of the R0 equation such that it is rescaled to range from zero to one with the value of one occurring at the unimodal peak . Although absolute values of R0 that are used to determine when transmission is stable depend on additional factors not captured in our model , the minimum and maximum temperatures for which R0 > 0 map exactly onto our normalized equations , allowing us to accurately calculate whether or not transmission should be possible at all . Empirical estimates of absolute values of R0 are difficult to obtain in any case , but it is much easier to determine whether transmission is occurring and for how long . While different model formulations for predicting R0 versus temperature can produce results with different magnitudes and potentially different overall shapes [49] , the temperatures for which R0 is above or below zero ( or one ) are mostly model independent . For instance , two competing models differ only by whether or not the formula in Eq ( 1 ) is squared , but the square of a number ( e . g . , an absolute R0 value ) greater than one is always greater than one , and the square of a number less than one is always less than one . Therefore , the threshold temperatures at which absolute R0 > 0 or absolute R0 > 1 will be exactly the same for either choice of formula ( Fig F in S1 Text ) . Similarly , because different expressions for R0 , including the square of Eq ( 1 ) , map monotonically onto our function , they will produce identical estimates for the temperatures at which transmission declines to zero and peaks ( Fig F in S1 Text ) . Consequently , our use of relative R0 adequately describes the nonlinear relationship between mosquito and virus traits and transmission . We fit the trait thermal responses in Eq ( 1 ) based on an exhaustive search of published laboratory studies that fulfilled the criterion of measuring a trait at three or more constant temperatures , ideally capturing both the rise and the fall of each unimodal curve ( Tables S1-S2 ) . Constant-temperature laboratory conditions are required to isolate the direct effect of temperature from confounding factors in the field and to provide a baseline for estimating the effects of temperature variation through rate summation [50] . We attempted to obtain raw data from each study , but if they were not available we collected data by hand from tables or digitized data from figures using WebPlotDigitizer [51] . We obtained raw data from Delatte [19] and Alto [21] for the Ae . albopictus egg-to-adult survival probability ( pEA ) , mosquito development rate ( MDR ) , gonotrophic cycle duration ( GCD , which we assumed was equal to the inverse of the biting rate ) and total fecundity ( TFD ) ( Table D in S2 Text ) . Data did not meet the inclusion criterion for CHIKV or ZIKV vector competence ( b , c ) or extrinsic incubation period ( EIP ) in either Ae . albopictus or Ae . aegypti . Instead , we used DENV EIP and vector competence data , combined with sensitivity analyses . Following Johnson et al . [52] , we fit a thermal response for each trait using Bayesian models . We first fit Bayesian models for each trait thermal response using uninformative priors ( T0 ~ Uniform ( 0 , 24 ) , Tm ~ Uniform ( 25 , 45 ) , c ~ Gamma ( 1 , 10 ) for Brière and c ~ Gamma ( 1 , 1 ) for Quadratic fits ) chosen to restrict each parameter to its biologically realistic range ( i . e . , T0 < Tm and we assumed that temperatures below 0°C and above 45°C were lethal ) . Any negative values for all thermal response functions were truncated at zero , and thermal responses for probabilities ( pEA , b , and c ) were also truncated at one . We modeled the observed data as arising from a normal distribution with the mean predicted by the thermal response function calculated at the observed temperature , and the precision τ , ( τ = 1/σ ) , distributed as τ ~ Gamma ( 0 . 0001 , 00001 ) . We fit the models using Markov Chain Monte Carlo ( MCMC ) sampling in JAGS , using the R [53] package rjags [54] . For each thermal response , we ran five MCMC chains with a 5000-step burn-in and saved the subsequent 5000 steps . We thinned the posterior samples by saving every fifth sample and used the samples to calculate R0 from 15–40°C , producing a posterior distribution of R0 versus temperature . We summarized the relationship between temperature and each trait or overall R0 by calculating the mean and 95% highest posterior density interval ( HPD interval; a type of credible interval that includes the smallest continuous range containing 95% of the probability , as implemented in the coda package [55] ) for each curve across temperatures . We fit a second set of models for each mosquito species that used informative priors to reduce uncertainty in R0 versus temperature and in the trait thermal responses . In these models , we used Gamma-distributed priors for each parameter T0 , Tm , c , and τ fit from an additional ‘prior’ dataset of Aedes spp . trait data that did not meet the inclusion criteria for the primary dataset ( Table C in S2 Text ) . We found that these initial informative priors could have an overly strong influence on the posteriors , in some cases drawing the posterior distributions well away from the primary dataset , which was better controlled and met the inclusion criteria . We accounted for our lower confidence in this data set by increasing the variance in the informative priors , by multiplying all hyperparameters ( i . e . , the parameters of the Gamma distributions of priors for T0 , Tm , and c ) by a constant k to produce a distribution with the same mean but 1/k times larger variance . We chose the value of k based on our relative confidence in the prior versus main data . Thus we chose k = 0 . 5 for b , c , and PDR and k = 0 . 01 for lf . This is the main model presented in the text ( Fig 2 ) . It is comparable to some but not all previous mechanistic models for Ae . aegypti and Ae . albopictus transmission ( Fig E in S1 Text ) . Results of our main model , fit with informative priors , did not vary substantially from the model fit with uninformative priors ( Figs G-H in S1 Text ) . Because organisms do not typically experience constant temperature environments in nature , we incorporated the effects of temperature variation on transmission by calculating a daily average R0 assuming a daily temperature range of 8°C , across the range of mean temperatures . This range is consistent with daily temperature variation in tropical and subtropical environments but lower than in most temperate environments . At each mean temperature , we used a Parton-Logan model to generate hourly temperatures and calculate each temperature-sensitive trait on an hourly basis [56] . We assumed an irreversible high-temperature threshold above which mosquitoes die and transmission is impossible [57 , 58] . We set this threshold based on hourly temperatures exceeding the critical thermal maximum ( Tm in Tables A-B in S1 Text ) for egg-to-adult survival or adult longevity by any amount for five hours or by 3°C for one hour . We averaged each trait over 24 hours to obtain a daily average trait value , which we used to calculate relative R0 across a range of mean temperatures . We used this model in the validation against human cases ( Fig 3 ) and the risk map ( Fig 4 ) . To validate the model , we used data on human cases of DENV , CHIKV , and ZIKV at the country scale and mean temperature during the transmission window . Using statistical models ( as described below ) , we estimated the effects of predicted R0 ( T ) on the probability of local transmission and the magnitude of incidence , controlling for population size and several socioeconomic factors . We downloaded and manually entered Pan American Health Organization ( PAHO ) weekly case reports for DENV and CHIKV for all countries in the Americas ( North , Central , and South America and the Caribbean Islands ) from week 1 of 2014 to week 8 of 2015 for CHIKV and from week 52 of 2013 to week 47 of 2015 for DENV ( www . paho . org ) . ZIKV weekly case reports for reporting districts ( e . g . , provinces ) within Colombia , Mexico , El Salvador , and the US Virgin Islands were available from the CDC Epidemic Prediction Initiative ( https://github . com/cdcepi/ ) from November 28 , 2015 to April 2 , 2016 . We aggregated the ZIKV data into country-level weekly case reports to match the spatial resolution of the DENV , CHIKV , and covariate data . We matched the DENV , CHIKV , and ZIKV incidence data with temperature using daily temperature data from METAR stations in each country , averaged at the country level by epidemic week . A previous study found a six-week lagged relationship between temperature and oviposition for Aedes aegypti in Ecuador [40] . Assuming that the subsequent transmission , disease development , medical care-seeking , and case reporting in humans takes an additional four weeks , we assumed a priori a ten-week lag between temperature and incidence ( i . e . , mean temperature for the week that is ten weeks prior to each case report ) . METAR stations are internationally standardized weather reporting stations that report hourly temperature and precipitation measures . Outlier weather stations were excluded if they reported a daily maximum temperature below 5°C or a daily minimum temperature above 40°C during the study period , extremes that would certainly eliminate the potential for transmission in a local area . Because case data are reported at the country level , we needed a collection of weather stations in each country that accurately represent weather conditions in the areas where transmission occurs , excluding extreme areas where transmission is unlikely . For the study period of October 1 , 2013 through April 30 , 2016 , we downloaded daily temperature data for each station from Weather Underground using the weatherData package in R [59] . We removed all data from Chile because it spans so much latitude and the terrain is so diverse that its country-level mean is unlikely to be very representative of the temperature where an outbreak occurred . We accessed available data on projected 2016 gross domestic product ( GDP ) for countries of interest via the International Monetary Fund’s World Economic Outlook Database ( http://www . imf . org/external/ns/cs . aspx ? id=28 ) . The direct and total contributions of tourism to GDP in 2016 were compiled from World Travel and Tourism Council economic impact reports ( http://www . wttc . org/research/economic-research/economic-impact-analysis/country-reports/#undefined ) . We retrieved population size data for 2013–2015 from the United Nations Population Division ( https://esa . un . org/unpd/wpp/Download/Standard/Population/ ) and averaged them across the three years for each country . Throughout the analyses below , unless otherwise specified , we used the natural log of the population size and of GDP as our predictors . We have two reasons for this choice . The first is that , intuitively , the relative order of magnitude of the population/GDP is more important in determining observed outbreak sizes or probabilities than their absolute sizes . Second , population sizes and GDPs across countries tend to exhibit clumped patterns with a few outliers that are much larger than the others . From a statistical perspective , using the un-transformed populations ( or GDPs ) results in those few large/rich countries having very high leverage in the analysis , and thus potentially skewing the results . Taking a log of the population better balances these predictors and is the standard accepted approach when using these kinds of predictors in regression models . To validate the R0 ( T ) model while controlling for population and socio-economic factors , we used generalized linear models ( GLMs ) on the weekly case count data . Importantly , we focused on testing whether the case counts were consistent with the transmission–temperature relationship predicted from our model , rather than on maximizing the variation explained in the statistical model . We are more specifically interested in understanding autochthonous transmission ( i . e . , locally acquired , not just imported cases ) . We set country-level thresholds for the number of cases defining autochthonous transmission for our three diseases separately , based on current transmission understanding: seven cases of CHIKV , 70 cases of DENV , and three cases of ZIKV . We derived these thresholds in the following way . First , we looked for data on outbreaks of travel related cases in countries that are not expected to experience any local transmission . For instance , in 2014 Canada experienced 320 confirmed , travel-related cases of chikungunya ( http://www . phac-aspc . gc . ca/publicat/ccdr-rmtc/15vol41/dr-rm41-01/rapid-eng . php ) , equivalent to an average of more than six cases per week . Thus , to be conservative in our estimates , we set the threshold of transmission as seven cases/week for CHIKV . The reported weekly cases of DENV transmission in our study sample are considerably higher than for CHIKV ( mean DENV incidence was nearly 100 times higher mean CHIKV incidence ) . We chose a moderately high threshold of 70 cases in a week ( i . e . , 10 times higher than the CHIKV threshold based on Canadian cases ) to reflect higher overall incidence and increased potential for travel related cases . We examined the sensitivity of the results to choice of threshold by varying it from 25 to 100 , and we found qualitatively similar results for all thresholds that we tested . As ZIKV is not as well established as either CHIKV or DENV at this time , smaller numbers of cases may indicate autochthonous transmission . Consequently , we chose a threshold of three cases for ZIKV ( approximately half the CHIKV threshold ) . Further , the results were fairly sensitive to the ZIKV threshold as many locations have small numbers of cases . Since higher thresholds exclude a very large proportion of available case data making analysis impossible , we used the slightly less conservative threshold of three cases for autochthonous transmission of ZIKV . The resulting data consisted of zeros for no transmission and positive case counts when transmission is presumed to be occurring . To model these data , we used a hurdle model that first uses logistic regression on the presence/absence of local transmission data to understand the factors correlated with local transmission occurring or not ( PA analysis ) . Then we modeled the log of incidence ( number of new cases per reporting week ) for positive values with a gamma generalized linear models ( incidence analysis ) . We were interested in understanding whether R0 ( T ) was an important predictor of human transmission occurrence and incidence , after controlling for potentially confounding factors like population size and socioeconomic conditions . To do this , we fit a series of models with different subsets of predictors that included R0 ( T ) and population size , the socioeconomic variables , or both ( see Table D in S2 Text for full models ) . To control for human population size , we created new metrics based on R0 ( T ) and population size to use for validation against the PAHO incidence data . We define GR0 , which is the posterior probability that R0 ( T ) > 0 . We use log ( p ) *GR0 , where p is the population size , as the relevant R0-based predictor for the PA analysis . For the incidence analysis , we instead use log ( p*R0 ( T ) ) as the predictor . In all cases log refers to the natural logarithm . For simplicity , we refer to these as the R0 ( T ) metrics hereafter and in the Results . In both the PA and incidence analyses , we first used the full data sets to examine which of the candidate models best described the data . Randomized quantile residuals indicated that the logistic and gamma GLM models were performing adequately . We compared the approximate model probabilities , calculated from the BIC scores , as well as the proportion of deviance explained ( D2 ) from each model . Next we examined the performance of the models in predicting out of sample , for both PA and incidence analyses . To do this we created 1000 random partitions , where 90% of the data were used to train the model and 10% were used for testing . In the PA analyses we classified each partition based on presence/absence , with separate classification thresholds for DENV versus CHIKV/ZIKV as these grouping had much different probabilities of occurrence . We assessed the performance of the model for the PA analysis based on the mean misclassification rate . In the incidence analyses we assessed the model performance based on the predictive mean absolute percentage error ( MAPE ) . Since differences in prediction success between the models in both the PA and incidence analyses were not statistically significant , we present the simpler models that only include the R0 ( T ) metrics in the main text ( Fig 3 ) and the models that additionally include socioeconomic covariates in the Supplementary Information ( Figs B-C in S1 Text ) . We plotted the model predictions as a function of the R0 ( T ) metrics together with the observed data for the PA and incidence analyses using the R package visreg [60] . The residuals of the incidence model exhibit “inverse trumpeting , ” in which residual variation is larger at low than high predicted incidence ( Fig I in S1 Text ) . This occurs in part because we forced the model to go through the origin , i . e . , no transmission when R0 ( T ) or the population size is equal to zero . However , the data did sometimes show transmission where we did not expect it , potentially because of imported cases , errors in reporting , or small pockets of transmission suitability in countries or times that are otherwise unsuitable on average . More local-scale case reporting that separates autochthonous from travel-associated cases would be needed to tease apart the source of this error . Using the validated model , we were interested in where the temperature was suitable for Ae . aegypti and/or Ae . albopictus transmission for some or all of the year to predict the potential geographic range of outbreaks in the Americas . We visualized the minimum , median , and maximum extent of transmission based on probability of occurrence thresholds from the R0 models for both mosquitoes . We calculated the number of consecutive months in which the posterior probability of R0 > 0 exceeds a threshold of 0 . 025 , 0 . 5 , or 0 . 975 for both mosquito species , representing the maximum , median , and minimum likely ranges , respectively . The minimum range is shown in Fig 4 and all three ranges are overlaid in Fig D in S1 Text . This analysis indicates the predicted seasonality of temperature suitability for transmission geographically , but does not indicate its magnitude . To generate the maps , we cropped monthly mean temperature rasters from 1950–2000 for all twelve months ( Worldclim; www . worldclim . org/ ) to the Americas ( R , raster package , crop function ) and assigned cells values of one or zero depending on whether the probability that R0 > 0 exceeded the threshold at the temperatures in those cells . We then synthesized the monthly grids into a single raster that reflected the maximum number of consecutive months where cell values equaled one . The resulting rasters were plotted in ArcGIS 10 . 3 , overlaying the three cutoffs ( Fig D in S1 Text ) . We employed this process for both mosquito species . | Understanding the drivers of recent Zika , dengue , and chikungunya epidemics is a major public health priority . Temperature may play an important role because it affects virus transmission by mosquitoes , through its effects on mosquito development , survival , reproduction , and biting rates as well as the rate at which mosquitoes acquire and transmit viruses . Here , we measure the impact of temperature on transmission by two of the most common mosquito vector species for these viruses , Aedes aegypti and Ae . albopictus . We integrate data from several laboratory experiments into a mathematical model of temperature-dependent transmission , and find that transmission peaks at 26–29°C and can occur between 18–34°C . Statistically comparing model predictions with recent observed human cases of dengue , chikungunya , and Zika across the Americas suggests an important role for temperature , and supports model predictions . Using the model , we predict that most of the tropics and subtropics are suitable for transmission in many or all months of the year , but that temperate areas like most of the United States are only suitable for transmission for a few months during the summer ( even if the mosquito vector is present ) . | [
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] | 2017 | Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models |
The “enhanced intracellular survival” ( eis ) gene of Mycobacterium tuberculosis ( Mtb ) is involved in the intracellular survival of M . smegmatis . However , its exact effects on host cell function remain elusive . We herein report that Mtb Eis plays essential roles in modulating macrophage autophagy , inflammatory responses , and cell death via a reactive oxygen species ( ROS ) -dependent pathway . Macrophages infected with an Mtb eis-deletion mutant H37Rv ( Mtb-Δeis ) displayed markedly increased accumulation of massive autophagic vacuoles and formation of autophagosomes in vitro and in vivo . Infection of macrophages with Mtb-Δeis increased the production of tumor necrosis factor-α and interleukin-6 over the levels produced by infection with wild-type or complemented strains . Elevated ROS generation in macrophages infected with Mtb-Δeis ( for which NADPH oxidase and mitochondria were largely responsible ) rendered the cells highly sensitive to autophagy activation and cytokine production . Despite considerable activation of autophagy and proinflammatory responses , macrophages infected with Mtb-Δeis underwent caspase-independent cell death . This cell death was significantly inhibited by blockade of autophagy and c-Jun N-terminal kinase-ROS signaling , suggesting that excessive autophagy and oxidative stress are detrimental to cell survival . Finally , artificial over-expression of Eis or pretreatment with recombinant Eis abrogated production of both ROS and proinflammatory cytokines , which depends on the N-acetyltransferase domain of the Eis protein . Collectively , these data indicate that Mtb Eis suppresses host innate immune defenses by modulating autophagy , inflammation , and cell death in a redox-dependent manner .
Mycobacterium tuberculosis ( Mtb ) is an intracellular pathogen that can survive and even multiply within host macrophages [1] , [2] . Mtb can persist within phagosomes by interfering with intracellular membrane trafficking and by arresting phagosome maturation in infected host cells [3] . Pathogenic mycobacteria have developed several strategies for surviving and growing under nutrient-limited conditions [4] . Autophagy , or the removal of aged organelles , plays a central role in regulating important cellular functions [5] , [6] and aids in innate and adaptive immune defense against Mtb and other intracellular pathogens [5] , [7]–[9] . Physiological or pharmacological induction of autophagy in macrophages results in increased co-localization of mycobacterial phagosomes and the autophagy effector LC3 , and the fusion of the former with lysosomes , which overcomes the blockade of membrane trafficking and increased bactericidal activity [7] . Although autophagy plays key roles in host innate and adaptive immune defenses , it can , under certain circumstances , result in type II programmed cell death [10] , [11] . Autophagic processes are activated in response to cellular stresses , such as oxidative stress , and can influence several types of cell death , including autophagy-related cell death [12] . Recently , we showed that the mycobacterial BCG cell wall triggers autophagy-induced cell death in radiosensitized colon cancer cells [13] . Additionally , several viral gene products may be involved in autophagy-induced cell death [14] . However , the genetic basis for mycobacterial induction of autophagy , and its implications for host cell viability , remain to be elucidated . The “enhanced intracellular survival” ( eis ) gene and its protein product , Eis , a unique protein of 42 kDa , of Mtb H37Rv enhance the survival of the saprophytic M . smegmatis during repeated passage through the human macrophage-like cell line U-937 [15] . Bioinformatic analyses showed that Eis is a member of the GCN5-related family of N-acetyltransferases [16] . Recent studies have revealed that kanamycin resistance is associated with eis promoter mutations that increase Eis transcript and protein levels [17] . Additionally , regulation of eis expression by SigA enhanced intracellular growth of the W-Beijing Mtb strain in monocytic cells [18] . Moreover , Eis inhibited the proliferation of mitogen-activated T cells and , by blocking the phosphorylation of extracellular signal-regulated kinase ( ERK ) , reduced the production of tumor necrosis factor ( TNF ) -α and interleukin ( IL ) -4 [19] . Despite being implicated in host-pathogen interactions during Mtb infection , the precise role of Eis in innate immune regulation remains to be determined . In an effort to gain further insight into the role of Eis in host responses , we examined autophagy , inflammatory cytokine production , and reactive oxygen species ( ROS ) generation in macrophages infected with wild-type ( Mtb-WT ) , eis-deletion ( Mtb-Δeis ) , or complemented ( Mtb-c-eis ) Mtb strains . Infection with Mtb-Δeis significantly increased autophagy , inflammatory responses , and ROS generation in macrophages . NADPH oxidase ( NOX ) and mitochondria were found to be the major sources of ROS , which contributed to the induction of autophagy and inflammatory responses in Mtb-Δeis-infected cells . Increased and excessive activation of autophagy in macrophages infected with Mtb-Δeis had no effect on antimicrobial responses , but stimulated caspase-independent cell death ( CICD ) . Mtb-Δeis-induced host cell death was regulated by autophagic pathways and influenced by c-Jun N-terminal kinase ( JNK ) -dependent ROS generation . Furthermore , we show that the N-acetyltransferase domain of Eis is responsible for its modulation of ROS generation and proinflammatory responses in macrophages .
Previous studies identified a role for the eis gene in enhancing the survival of mycobacteria in human monocytic cells [15] . However , the role of eis in autophagy activation in macrophages , which plays a key role in defense and cellular homeostasis [5] , is not fully understood . We first infected bone marrow-derived macrophages ( BMDMs ) with the Mtb-WT , Mtb-Δeis , and Mtb-c-eis strains of Mtb H37Rv and examined the kinetics of autophagosome formation by immunostaining for LC3 . As shown in Figure 1A , in BMDMs infected with Mtb-Δeis we observed the recruitment of endogenous LC3 in punctate structures the formation of which peaked 24 h after infection , before decreasing substantially by 48 h post-infection ( Fig . 1A , right ) . In contrast , autophagosome formation was not increased in BMDMs infected with Mtb-WT or Mtb-c-eis ( Fig . 1A ) . Additionally , RAW 264 . 7 macrophages transfected with green fluorescent protein ( GFP ) fused to the autophagosome protein LC3 ( GFP-LC3 ) [20] showed a significant increase in GFP-LC3 puncta formation when infected with Mtb-Δeis at a multiplicity of infection ( MOI ) of 10 ( over levels in cells infected with Mtb-WT or Mtb-c-eis at the same bacterial load; Fig . S1A ) . Moreover , Mtb-Δeis-induced formation of LC3 punctae in BMDMs ( Fig . 1B ) and RAW 264 . 7 cells ( Fig . S1B ) was abrogated by treatment for 4 h with 3-methyladenine ( 3-MA ) , a classical inhibitor of autophagy [21] . Cleavage of soluble LC3 ( LC3-I ) to form LC3-II , which correlates with the extent of autophagosome formation [20] , was further examined by Western blotting . As shown in Figure 1C , Mtb-Δeis significantly induced LC3-II formation , whereas Mtb-WT and Mtb-c-eis did not . We next monitored Mtb-Δeis-induced autophagy through detection of autophagic vacuoles or organelles by transmission electron microscopy ( TEM ) . Ultrastructural analysis of BMDMs treated with Mtb-Δeis for 24 h revealed the presence of multiple cytosolic autophagic vacuoles resembling autophagosomes ( Fig . 1D ) . Additionally , TEM analyses revealed the presence of bacilli within characteristic double-membrane autophagosomes and multiple membrane structures ( Fig . 1D ) , a pattern characteristic of the induction of autophagy and autophagic death [22]–[24] . From 12 h post-infection , we observed Mtb-Δeis within autophagic vacuoles ( Fig . 1D , middle ) , which fused with multivesicular structures [25] . At 24 h post-infection , multiple late or degradative autophagic vacuoles [25] were clearly visible , in which partially degraded cytoplasmic materials and bacteria were evident ( Fig . 1D , bottom ) . We also examined whether autophagic vacuoles formed in cells infected with Mtb-Δeis were able to mature to autolysosomes [25] . Confocal analysis showed that BMDMs infected with Mtb-Δeis exhibited co-localization of the autophagosomal marker LC3 and the lysosomes marker Lamp-1 ( Fig . S1C ) . We also observed that levels of LC3-II and LC3 puncta formation in Mtb-Δeis-infected BMDMs were increased by pretreatment with the vacuolar H+-ATPase inhibitor bafilomycin A ( Baf-A ) [20] , [26] ( Fig . 1E , LC3-II; Fig . S1D , LC3 puncta formation ) . These findings indicate that Mtb-Δeis induced both autophagy and autophagosome-lysosome fusion in macrophages . The interaction of Mtb with innate receptors in phagocytes triggers an oxidative burst and activates intracellular signaling cascades that induce proinflammatory responses [27] , [28] . We thus examined the production of proinflammatory cytokines and the generation of ROS in BMDMs infected with Mtb-WT , Mtb-Δeis , or Mtb-c-eis . As shown in Figure 2A , BMDMs infected with Mtb-Δeis at increasing bacterial loads ( MOI = 0 . 1 , 1 , 10 ) produced greater amounts of TNF-α and IL-6 than cells infected with Mtb-WT or Mtb-c-eis . Levels of TNF-α and IL-6 , which peaked at 18 h , were significantly higher in BMDMs infected with Mtb-Δeis than those infected with Mtb-WT or Mtb-c-eis ( Fig . 2B; P<0 . 05 , TNF-α; P<0 . 01 , IL-6 ) . We next examined whether autophagy played a role in the regulation of proinflammatory cytokine production in macrophages infected with Mtb-WT , Mtb-Δeis , or Mtb-c-eis . As shown in Figure S2 , the secretion of TNF-α and IL-6 was significantly increased in RAW264 . 7 cells transfected with siRNA specific for Beclin-1 ( siBeclin-1 ) or Atg5 ( siAtg5 ) , suggesting a negative regulatory role for autophagic pathways in proinflammatory cytokine production in macrophages infected with Mtb-Δeis . We further examined whether ROS levels differed between cells infected with the WT , Δeis , and c-eis strains of Mtb H37Rv . We measured the production of ROS by flow cytometry , using 2 , 7′-dichlorofluorescein-diacetate ( DCFH-DA ) and dihydroethidium ( DHE ) as probes for H2O2 and O2− , respectively ( Fig . 2C ) . Compared with BMDMs infected with Mtb-WT or Mtb-c-eis strains , cells infected with Mtb-Δeis displayed markedly increased intracellular DCFH-DA and DHE fluorescence ( Fig . 2C ) . To exclude the involvement of reactive nitrogen species ( RNS ) in detecting ROS generation , we pre-treated BMDMs with the specific nitric oxide synthase inhibitors nitro-L-arginine methyl ester ( L-NAME ) or NG-monomethyl-L-arginine ( L-NMMA ) prior to Mtb-Δeis infection and examined ROS generation . Pre-treatment with nitric oxide synthase inhibitors had no significant effect on ROS generation in BMDMs infected with Mtb-Δeis ( Fig . S3 ) , suggesting that up-regulated DCFH-DA and DHE fluorescence intensities were due principally to increased ROS generation in Mtb-Δeis-infected macrophages . Notably , flow cytometric analysis showed that infection with Mtb-Δeis yielded a stronger MitoSOX Red signal , which is specific for mitochondrial superoxide [29] , than infection with the Mtb-WT or Mtb-c-eis strains ( Fig . 2D ) . These data suggest that Mtb-Δeis more strongly induces the production of proinflammatory cytokines and ROS in BMDMs than do Mtb-WT or Mtb-c-eis . Recent studies have shown that NOX-derived ROS are involved in the activation of autophagy [30] . Additionally , we have shown that NOX2/gp91phox , the main catalytic component of NOX , interacts with TLR2 , a key effector of Mtb-induced proinflammatory responses [28] . Because ROS generation was significantly elevated in Mtb-Δeis-infected cells , we hypothesized that increased ROS production during Mtb-Δeis infection might be a trigger for autophagy activation and proinflammatory responses . As anticipated , pretreatment with the ROS scavengers [N-acetyl cysteine ( NAC ) , diphenyleneiodonium ( DPI ) , catalase and tiron ( 4 , 5-dihydroxy-1 , 3-benzene disulfonic acid-disodium salt ) ; for 1 h before infection] prevented Mtb-Δeis-induced autophagosome accumulation in BMDMs ( Fig . 3A ) and RAW 264 . 7 cells transfected with GFP-LC3 ( Fig . S4A ) . Additionally , the conversion of LC3-I to LC3-II in Mtb-Δeis-infected cells was suppressed by catalase and tiron ( Fig . 3B ) . We further examined whether ROS generation was involved in the induction of proinflammatory cytokines in Mtb-Δeis-infected BMDMs . ROS scavengers reduced the TNF-α and IL-6 levels in BMDMs infected with Mtb-Δeis ( Fig . 3C ) . We next determined the Mtb-Δeis-induced activation of autophagy and proinflammatory responses in NOX2-deficient macrophages . ROS induction was abolished in NOX2-deficient macrophages infected with Mtb-WT , Mtb-Δeis , or Mtb-c-eis ( Fig . S4B ) . Infection of NOX2-deficient BMDMs with Mtb-Δeis resulted in a dramatic reduction in autophagy , as assessed by LC3 puncta formation ( Fig . 3D ) and LC3-II conversion ( Fig . 3E ) at 18 h . However , neither starvation- nor rapamycin-induced autophagy was dependent on NOX2 expression ( Fig . S4C ) . Proinflammatory cytokine mRNA expression at 6 h ( Fig . S4D ) and protein levels at 18 h ( Fig . 3F ) following infection with Mtb-Δeis were significantly reduced in BMDMs taken from NOX2 KO mice . The release of proinflammatory cytokines in response to WT or c-eis Mtb was similarly reduced in NOX2-deficient macrophages ( Fig . 3F ) . Collectively , our data suggest that NOX2-derived ROS are centrally involved in the up-regulated autophagy and proinflammatory responses in BMDMs infected with Mtb-Δeis . Autophagy serves as a cell survival mechanism in some contexts , but triggers cell death in others [31] . To examine whether the eis gene can modulate host cell survival/death in macrophages , we infected BMDMs with Mtb-WT , Mtb-Δeis , or Mtb-c-eis and examined host cell viability . When BMDMs were infected with these three strains at an MOI of 10 , Mtb-Δeis-infected cells showed a significant decrease in cell viability after 24 h , whereas Mtb-WT- and Mtb-c-eis-infected cells displayed only low rates of cell death ( Fig . 4A ) . Infection with either Mtb-WT or Mtb-c-eis tended to reduce BMDM viability , dose-dependently , above an MOI of 25 ( Fig . S5A ) . We next assessed whether apoptosis played a role in the cell death induced by Mtb-Δeis using the TUNEL assay ( Fig . 4B ) . At 36 h post-infection , there was a marked increase in total cell death in BMDMs infected with Mtb-Δeis . However , only a slight increase in the number of apoptotic cells was observed ( Fig . 4B ) . Additionally , microscopic examination of Mtb-Δeis-infected cells at 36 h post-infection revealed morphological changes associated with cell death that were not observed in Mtb-WT- or Mtb-c-eis-infected cells ( Fig . 4C ) . http://www . jimmunol . org/cgi/content/full/179/2/939 - F1#F1To further examine the mechanism of cell death in Mtb-Δeis-infected cells , we cultured BMDMs infected with Mtb-WT , Mtb-Δeis , or Mtb-c-eis ( MOI = 10 ) in the presence or absence of the broad-spectrum caspase inhibitor z-VAD-fmk ( administered 1 h prior to infection ) . We found that z-VAD-fmk only partially blocked Mtb-Δeis-mediated cell death ( Fig . 4D ) . Also , caspase-3 enzyme activities did not differ significantly between Mtb-WT- , Mtb-Δeis- , and Mtb-c-eis-infected macrophages ( data not shown ) , suggesting that the reduction in macrophage viability caused by Mtb-Δeis infection did not result primarily from caspase activation . We further assessed the role of autophagy in modulating cell death induced by Mtb-Δeis . BMDMs were pretreated with a known inhibitor of autophagy , 3-MA , prior to Mtb-Δeis infection . Pretreatment with 3-MA effectively prevented Mtb-Δeis-induced macrophage cell death , but had no such effect on Mtb-WT- or Mtb-c-eis-infected cells ( Fig . 4D ) http://www . jimmunol . org/cgi/content/full/180/1/207 - F6#F6 . To further assess the role of autophagy in Mtb-Δeis-induced cell death , we depleted Beclin-1 or Atg5 by siRNA transfection of Mtb-Δeis-infected RAW 264 . 7 cells . Transfection of RAW 264 . 7 cells with siBeclin-1 or siAtg5 significantly inhibited Mtb-Δeis-induced cell death , as assessed by propidium iodide ( PI ) staining ( Fig . S5B ) . Moreover , a trypan blue exclusion assay showed that blockade of autophagy increased the survival of Mtb-Δeis-infected BMDMs ( Fig . S5C ) . Collectively , these results support the concept that Mtb Eis actively inhibited CICD . It is known that ERK and JNK mitogen-activated protein kinase ( MAPK ) signaling pathways are important in oxidative stress-induced cell death [32]–[34] . No significant difference in activation kinetics of phosphorylated p38 and ERK1/2 was detected between cells infected with Mtb-WT- , Mtb-Δeis- , and Mtb-c-eis ( Fig . 5A ) . In contrast , a significant increase in JNK/SAPK phosphorylation was observed in cells infected with Mtb-Δeis , this response preceding similar responses in macrophages infected with Mtb-WT or Mtb-c-eis ( Fig . 5A ) . Densitometric quantification of phosphorylated p38 , ERK1/2 , and JNK band intensities showed that the active form of JNK was uniquely increased in macrophages infected with Mtb-Δeis ( Fig . 5B ) . These results indicate that differences in JNK signaling may be responsible for differences in the responses to different bacterial strains . To further explore the link between MAPK signaling and Mtb-Δeis-induced ROS generation and cell death , cells were pretreated with specific inhibitors of JNK ( SP600125 ) , p38 ( SB203580 ) , and MEK ( U0126 ) for 1 h prior to infection with Mtb-Δeis . Inhibition of JNK , but not the other two kinases , dose-dependently reduced Mtb-Δeis-induced ROS generation , as measured by flow cytometry ( Fig . 5C ) . Additionally , inhibition of JNK signaling , but not p38 or ERK1/2 signaling , dose-dependently reduced Mtb-Δeis-induced macrophage death ( Fig . 5D ) . Moreover , transfection of RAW264 . 7 cells with siRNA specific for JNK ( siJNK ) markedly reduced cell death induced by Mtb-Δeis ( Fig . 5E ) . Together , these data suggest that Eis modulated macrophage survival through JNK-dependent regulation of ROS signaling . We next investigated the activation of autophagy , inflammation , cell death , and mycobacterial growth in vivo . Mice were challenged , by aerosol exposure to Mtb-WT , Mtb-Δeis , or Mtb-c-eis , and maintained for 4 weeks . Rates/levels of pulmonary granulomatous inflammation were approximately 35–50% at 4 weeks post-infection ( data not shown ) . Similar to the in vitro results ( Fig . 1 ) , numerous lamellar structures with cytoplasmic autophagic vacuoles were observed in the cytosol of alveolar macrophages isolated from the lungs of mice 4 weeks after infection with Mtb-Δeis , but not Mtb-WT or Mtb-c-eis ( Fig . 6A and other data not shown ) . These ultrastructural features demonstrated the presence and degradation of bacteria within autophagic vesicles in the lungs of Mtb-Δeis-infected mice ( Fig . 6A ) . Additionally , quantitative RT-PCR analysis demonstrated that TNF-α and IL-6 mRNA levels were significantly higher in lung tissues from Mtb-Δeis-infected mice than in those from Mtb-WT- or Mtb-c-eis-infected mice ( Fig . 6B ) . Moreover , rates of cell death , measured by PI staining , were significantly higher in bronchoalveolar lavage fluid cells isolated from Mtb-Δeis-infected mice than those from Mtb-WT- or Mtb-c-eis-infected mice ( Fig . 6C ) . There was no significant difference in the number of TUNEL-positive apoptotic cells in lung tissues from Mtb-WT- , Mtb-Δeis- , and Mtb-c-eis-infected mice ( data not shown ) . To analyze bacterial survival in vivo , five mice per group were sacrificed 4 weeks post-challenge and bacterial counts were determined from lung and spleen homogenates . Numbers of viable bacteria in lung and spleen did not differ among mice infected with the three Mtb strains ( Fig . 6D ) . Furthermore , we determined the in vitro intracellular growth of Mtb-WT , Mtb-Δeis , and Mtb-c-eis in macrophages . The three strains grew in macrophages at almost identical rates ( Fig . 6E ) , consistent with our previous observations [16] . Collectively , these data suggest that numbers of autophagic vacuoles , the strength of the inflammatory response , and rates of cell death were significantly increased during in vivo infection with Mtb-Δeis , although there was no obvious effect on bacterial elimination . We previously showed that Mtb-infected macrophages release Eis into the cytosol and the culture supernatant [16] . Thus , the potential of recombinant Eis protein to inhibit ROS generation and inflammatory cytokine production in macrophages infected with Mtb-Δeis was assessed by Eis pretreatment or transfection with the eis gene . Induction of ROS by Mtb-Δeis was significantly decreased by pretreatment with Eis , but not by control mycobacterial antigens , such as the recombinant 85A ( 30 k ) antigen of Mtb ( Fig . 7A ) . Eis is a member of the GCN5-related family of N-acetyltransferases [16] . To test whether the acetyltransferase domain of Eis was required for the induction of ROS , we transfected THP-1 cells with an Eis-WT ( WT-eis-expressing ) or Eis-ΔAT ( N-acetyltransferase domain deletion mutant ) construct , or a mock control plasmid , and infected them with Mtb-Δeis . Eis-WT , but not Eis-ΔAT , blocked the induction of superoxide and H2O2 generation by Mtb-Δeis ( Fig . 7B ) . We next examined the effect of Eis pretreatment on the proinflammatory cytokine production in Mtb-Δeis-infected BMDMs . Pretreatment with Eis , but not 85A antigen , dose-dependently inhibited Mtb-Δeis-induced secretion of TNF-α and IL-6 ( Fig . 7C ) . Moreover , we examined the effects of over-expressing Eis-WT or Eis-ΔAT plasmids on proinflammatory cytokine responses in THP-1 cells infected with Mtb-Δeis . Cells over-expressing wild-type Eis secreted 2 . 6-fold less TNF-α and 2 . 7-fold less IL-6 than those expressing an Eis protein lacking the AT domain ( Fig . 7D ) . Notably , inhibition of the JNK pathway by pre-treatment with pharmacological inhibitors markedly blocked Mtb-Δeis-mediated up-regulation of superoxide generation ( Fig . 7E ) and proinflammatory cytokine levels ( Fig . 7F ) in THP-1 cells transfected with either mock control or Eis-ΔAT constructs . In contrast , JNK inhibition did not significantly affect Mtb-Δeis-induced ROS production ( Fig . 7E ) or cytokine secretion ( Fig . 7F ) in THP-1 cells over-expressing Eis-WT constructs . These data suggest that the N-acetyltransferase domain of Eis is critical to Eis's modulation of host cell ROS generation and proinflammatory cytokine responses through the JNK pathway .
Earlier studies demonstrated that the eis gene of Mtb can enhance survival of the non-pathogenic M . smegmatis in macrophages [15] . Moreover , Eis protein was detected in Mtb-containing phagosomes and the cytoplasm of parasitized cells , as well as in cell culture supernatants of Mtb-infected macrophages [16] , [35] . Studies have demonstrated the presence of anti-Eis antibodies in TB patients , indicating that Eis is produced during human infection [35] . Eis also modulates TNF-α secretion and T cell responses [16] , [19] . However , its precise role in innate immune responses has not been clearly determined . The present study provides evidence that Eis plays an essential role in modulating host innate responses and cell death through ROS-dependent pathways . Our demonstration that Mtb-Δeis increased the production of proinflammatory cytokines by BMDMs ( i . e . , Eis production alters patterns of cytokine production ) is consistent with our previous findings [16] . Additionally , we provide evidence that Eis performs previously unrecognized functions in modulating specific types of cell death , dependent on autophagy and ROS-mediated signaling . Although autophagic pathways have been widely explored as a strategy for overcoming mycobacterial escape from phagosomal maturation , excessive activation of autophagy , and the resulting cell death ( caused by a robust increase in ROS generation ) , did not apparently directly impact host defenses in Mtb-Δeis-infected cells . Autophagy is a well-organized homeostatic cellular process responsible for the removal of damaged organelles and the elimination of intracellular pathogens [5] . Induction of autophagy is critical to the eradication of Mtb from murine and human macrophages [7] , [8] . Recent reports have emphasized the role of autophagy in host defense against human tuberculosis caused by Mtb [36] . Prolonged or excessive autophagy can result in non-apoptotic type II programmed cell death [10] . We recently reported that mycobacterial BCG cell wall induced autophagic cell death in radiosensitized cancer cells [34] . Indeed , it is known that several cytokines , including TNF-α , can activate autophagy pathways [5] . Thus , because they have been shown to be potent inducers of cytokine production [37] , it is possible that mycobacterial proteins and/or other cell components increase the activation of autophagy by inducing the production of TNF-α . In our recent study , we found that mycobacterial LpqH can trigger the activation of autophagy [38] . Additionally , various mycobacterial components , including ESAT6 [39] , PE_PGRS 33 [40] , and nuoG [41] , have been reported in modulating host cell death , i . e . , apoptosis or necrosis . Moreover , a recent report showed that Mtb mutation of nuoG or KatG leads to ROS accumulation in phagosomes , with subsequent induction of host cell apoptosis [42] . However , the genetic basis of mycobacterial induction of autophagy-dependent cell death in normal macrophages has not been characterized . Macrophages that died after Mtb-Δeis infection displayed morphological features of autophagic ( type 2 ) cell death , characterized by the accumulation of autophagic vacuoles ( autophagosomes ) in the cytoplasm [43] . Massive autophagic vacuolization may be the consequence of a failed attempt by Mtb to adapt to its cellular environment , which ultimately results in cell death [43] . Macrophages infected with Mtb-Δeis at a relatively low MOI ( 5–10 ) displayed higher rates of CICD and autophagy activation than did cells infected with wild-type or complemented strains of Mtb H37Rv . These data partly correlate with the previous finding that infection with Mtb H37Rv at the same MOI slightly increased macrophage cytotoxicity over control levels [44] . It has also been shown that attenuated strains of mycobacteria at an MOI≤10 trigger TNF-α-induced apoptosis , which is associated with innate host defenses against intracellular mycobacteria [45] . Macrophages infected with Mtb-Δeis showed a modest , but significant , increase in the rate of apoptosis , as assessed by the TUNEL assay . Additionally , we observed no prominent sign of necrosis [46] , such as intracellular vesicular swelling , rupture of plasma membranes , or dilation of cytoplasmic organelles , in macrophages infected with Mtb-Δeis . Thus , our data show that Mtb Eis is involved in the control of a novel type of cell death , characterized by massive autophagic vacuolization . This type of cell death was modulated by inhibitors of autophagy: 3-MA ( see Fig . 4 ) . After showing that Eis is involved in autophagy-dependent cell death , we considered the possibility that Eis may affect the intracellular survival of bacteria . We previously reported that an eis deletion mutant of Mtb had no growth defect in human monocytic U937 cells or in mice [16] . The current study confirms our previous findings [16] that an eis deletion mutant of H37Rv multiplies at a rate similar to WT or complemented strains in the lungs and spleen of infected mice ( see Fig . 6D ) . In contrast , a recent study reported that deletion of eis reduced the growth of the clinical Mtb strain TB294 in Mono Mac 6 cells [18] . This clinical strain was found to express 20-fold higher levels of Eis than H37Rv [18] . This discrepancy may be the result of strain-specific differences in the production of Eis and/or the use of different host cells [18] . Recent findings showed that an eis promoter mutation that increases Eis expression conferred resistance to kanamycin in clinical Mtb strains , by increasing its acetylation and inactivation [17] . We thus suggest that overproduction of Eis may enable some clinical Mtb strains to modulate autophagy and cell death , especially those with eis promoter mutations . It will be interesting to determine whether clinical strains overproducing Eis exhibit altered intracellular growth and disease outcomes through subversion of autophagy , cell death , and host defense . Activation of an exacerbated inflammatory response during Mtb-Δeis infection may explain the lack of effect on bacterial elimination despite the induction of autophagy . Indeed , our previous [16] and current studies showed that inflammatory responses are profoundly up-regulated in Mtb-Δeis-infected monocytes/macrophages . Here , proinflammatory cytokine production was negatively regulated by autophagy activation in Mtb-Δeis-infected macrophages ( Fig . S2 ) . Despite the potential contribution of autophagy to this inflammatory balance , increased ROS and subsequent organelle damage by Mtb-Δeis infection may trigger an amplifying positive feedback loop and in so doing induce massive autophagy and cell death . If production of pro-inflammatory cytokines and chemokines during mycobacterial infection is excessive or inappropriate , it may hinder protective immunity and exacerbate the pathology [47] . Our data show that significant up-regulation of ROS production ( for which NOX and mitochondria are largely responsible ) is required for Mtb-Δeis to increase macrophage inflammatory and autophagic responses , which are normally controlled by Eis . These findings partially agree with our previous studies showing that NOX2-dependent ROS generation played a key role in TLR2-dependent inflammatory signaling and cathelicidin production in macrophages [28] . Selective autophagic degradation of catalase and subsequent ROS accumulation cause lipid membrane damage and autophagic cell death , indicating the complex nature of the relationship between ROS and non-apoptotic programmed cell death [48] . Additionally , overproduction of ROS contributed to CICD in macrophages treated with lipopolysaccharide and the pan-caspase inhibitor , Z-VAD [48] . At the molecular level , “Toll/IL-1 receptor ( TIR ) domain-containing adaptor-inducing IFN-β” ( TRIF ) and “receptor-interacting protein 1” ( RIP-1 ) operate upstream of ROS production and are involved in inducing autophagy and CICD [49] . In starvation-induced autophagy , ROS serve as signaling molecules that induce autophagy and regulate cysteine protease HsAtg4 [50] . Moreover , previous studies showed that activation of TLRs or Fcγ receptors induced autophagy through NADPH oxidase-derived ROS [30] . Regarding the signaling pathways linking ROS and cell death , our data provide evidence for the involvement of JNK signaling in macrophages infected with Mtb-Δeis . Recent studies have shown that JNK pathways contribute to the induction of non-canonical autophagy by activating Atg7 [51] . Additionally , other studies showed that increased oxidative stress results in the induction of endoplasmic reticulum stress , which , in turn , can lead to autophagy and cell death through activation of a JNK/p38 signaling pathway [52] . Moreover , JNK signaling has been shown to play an important role in autophagic cell death [53] . We found that the activation of JNK was required for Mtb-Δeis-induced ROS generation and cell death . Thus , it appears that oxidative stress and JNK/SAPK constitute a positive feedback loop that contributes to the induction of cell death with autophagy by Mtb-Δeis . Given the specific pathologic events that occur in Mtb-Δeis-infected macrophages , several mechanisms could explain the observed excessive autophagy and resulting cell death . First , excessive ROS generation ( for which NOX and mitochondria are primarily responsible ) , may contribute to increased activation of autophagy . Data generated using ROS inhibitors and NOX2-deficient mice show that excessive ROS generation is responsible for the induction of autophagy and inflammation by Mtb-Δeis . The marked induction of autophagy by Mtb-Δeis may be attributed to the expected need for increased protein/organelle turnover in injured cells undergoing oxidative stress , such as those with damaged mitochondria [54] . Second , our findings suggest that Eis regulates a key player in host innate immunity through its N-acetyltransferase domain . This idea is supported by the observations that Mtb-Δeis-mediated ROS generation and inflammatory cytokine production were inhibited by pretreatment with Eis and realized in an N-acetyltransferase domain-dependent manner ( see Fig . 7 ) . Finally , the phenotype of Mtb-Δeis-infected macrophages may depend on their activation state . When macrophages were primed using interferon-γ and lipopolysaccharide prior to exposure to Mtb-Δeis , they showed a significant decrease in overall cell death , but a concurrent increase in the rate of apoptosis ( data not shown ) . These data indicate that the activation of macrophages may alter their mechanism of cell death during subsequent Mtb-Δeis infection . Thus , excessive activation of autophagy appears to play an important role in cell death , although cell death with autophagy does not affect the ability of host cells to efficiently eliminate invading bacteria . Our data provide evidence that Eis plays an essential role in regulating both the early generation of ROS and inflammatory responses in macrophages . These activities are dependent on the acetyltransferase moiety of Eis . Previously , we reported that Eis is a member of the GCN5-related family of N-acetyltransferases , as determined through bioinformatics analyses [16] . Members of this family of proteins are involved in a variety of activities , ranging from transcriptional activation to antibiotic resistance [55] . The well-characterized effector YopJ from Yersinia spp . acetylated critical serine and threonine residues in the activation loop of MAPKK6 , thereby blocking its phosphorylation [56] . This resulted in the inhibition of MAPK and nuclear factor-κB signaling and , thus , the innate immune responses to Yersinia infection [56] , [57] . The current data suggest that the mycobacterial effector Eis regulates eukaryotic cell function through the direct modification of target proteins , effected by its N-acetyltransferase domain . Together , our results provide novel insights into the roles of mycobacterial Eis in controlling and suppressing host inflammatory responses and cell survival/death , which it achieves by modulating ROS-dependent JNK activation . Excessive activation of autophagy was shown to cause cell death , as well as inefficient bacterial clearance , in macrophages infected with Mtb-Δeis . Eis itself regulated oxidative stress and inflammation through its acetyltransferase domain . Our present characterization of the mycobacterial effector Eis as a modulator of autophagy and cell death presents a previously unknown paradigm for understanding host-pathogen interactions in mycobacterial infection .
Mtb-WT , Mtb-Δeis , and Mtb-c-eis strains were generated and used in these experiments . The eis gene was disrupted in H37Rv by means of a two-step gene replacement strategy using a pMJ10 allelic exchange vector ( ts oriM; sacB counterselection; KanR , GentR ) as described previously [58] . A vector was constructed that contained the eis gene disrupted by a hygromycin cassette ( eis:hyg/pMJ10 ) . This vector ( 5 µg ) was introduced into electrocompetent Mtb H37Rv cells . Transformants were first selected by growth on 7H10-OADC-Tween 80 plates containing hygromycin B ( 50 µg/mL ) at 37°C for 3–4 weeks . Individual antibiotic-resistant colonies were selected and subcultured onto fresh plates . Several clones were then picked and grown in 50 mL of 7H9-ADC broth containing hygromycin B at 37°C for 48 h . Cells from the broth cultures were then diluted in 7H9-ADC broth and plated on 2% sucrose-7H10-OADC-hyg and incubated at 39°C for 3–4 weeks . The double-resistant ( sucR/hygR ) clones were selected and shown , by Southern blotting , to be Δeis mutants ( data not shown ) . The H37RvΔeis mutant was complemented using an integration vector ( pMV306 ) containing a single copy of the eis gene ( mycobacterial integration vector; integrates into the attB site; KanR ) [59] . To obtain purified Eis protein , N-terminally His-tagged Eis was induced , harvested and purified from , E . coli expression strain BL-21 DE-3 pLysS , as described by Samuel et al . [16] following standard protocols recommended by Novagen . Mtb strains were grown as described previously [13] . The bacterial cultures were divided into 1-mL aliquots in cryovials and stored at −70°C prior to use . Representative vials were thawed , and viable CFUs were counted on Middlebrook 7H10 agar . Single-cell suspensions of mycobacteria were prepared as described previously [60] . All animal procedures were approved by the Institutional Animal Care and Use Committees of Yonsei University Health System and Chungnam National University . All animal experiments were performed in accordance with Korean Food and Drug Administration ( KFDA ) guidelines . For in vivo experiments , pathogen-free female C57BL/6 mice , aged 5–6 weeks , purchased from Japan SLC Inc . ( Shijuoka , Japan ) were maintained under barrier conditions in a BL-3 biohazard animal room at Yonsei University Medical Research Center . Animals were fed a sterile commercial mouse diet and water ad libitum . NOX2 ( C57BL/6 background ) mice were kindly provided by Y . S . Bae ( Iwha University , Seoul ) . Mice used as a source of cells for in vitro experiments were housed in specific pathogen-free conditions . Those used in individual experiments were age- and sex-matched mice ( and 5–8 weeks of age ) . BMDMs were isolated and then differentiated by growth for 5–7 days in medium containing M-CSF ( 25 µg/mL; R&D ) , as described previously [13] . RAW 264 . 7 cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% fetal bovine serum , as described previously [13] . Human THP-1 ( ATCC TIB-202 ) monocytic cells were grown in RPMI 1640/GlutaMAX , supplemented with 10% FBS [8] . Cells were treated with 20 nM PMA ( Sigma-Aldrich , St . Louis , MO ) for 24 h to induce their differentiation into macrophage-like cells and then washed three times with PBS . In vitro macrophage infection was performed as described previously [13] . Briefly , cells were infected with mycobacteria at different MOIs and incubated for 4 h at 37°C in a 5% CO2 atmosphere . After allowing time for phagocytosis , cells were washed four times with fresh PBS to remove extracellular bacteria and then incubated with complete DMEM without antibiotics . As controls , cultures of uninfected macrophages ( UI ) were maintained under the same conditions . The infection rates for the three strains were approximately 35–45% when BMDMs were infected at an MOI of 5 . Rates of infection were increased in infected macrophages when the MOI was increased . There was no significant difference in infection rates between the three strains . To test the capacities of the Mtb-WT , Mtb-Δeis , and Mtb-c-eis strains to survive intracellularly , BMDMs were infected with each strain at MOIs of 1 and 5 . Then , 4 h later , cells were washed with PBS three times , and the majority of extracellular bacteria ( >99% ) were removed , as determined through staining with auramine-rhodamine ( Merck , Darmstadt , Germany ) . After washing , the cells were incubated in fresh medium for a further 3 days . They were then lysed in autoclaved distilled water to allow intracellular bacteria to be collected [8] . The lysates were then re-suspended and sonicated for 5 min in a preheated 37°C water bath sonicator ( Elma , Singen , Germany ) . Aliquots of the resulting sonicates were serially diluted in 7H9 broth , plated separately on 7H10 agar plates , and incubated at 37°C in 5% CO2 for 12 d . Colony counting was then performed in triplicate . Mice were challenged by aerosol exposure with Mtb-WT , Mtb-Δeis , or Mtb-c-eis using an inhalation device ( Glas-Col , Terre Haute , IN , USA ) calibrated to deliver approximately 50 bacteria into the lungs . Five mice per group were sacrificed at 4 weeks post-challenge , and bacteria in lung and spleen homogenates were counted . Numbers of viable bacteria in lung/spleen were determined by plating serial dilutions of whole organ homogenates on Middlebrook 7H11 agar ( Difco , Detroit , MI , USA ) . Colonies were counted after 3–4 weeks of incubation at 37°C . DPI ( a NOX inhibitor ) , NAC ( an antioxidant ) , catalase , and z-VAD-fmk ( a pan-caspase inhibitor ) were purchased from Calbiochem ( San Diego , CA , USA ) . 3-MA , tiron ( a commercial deflocculant ) , and DAPI were purchased from Sigma . DMSO ( Sigma ) was added to cultures at a concentration of 0 . 1% ( v/v ) as a solvent control ( SC ) . The plasmid that encoded EGFP-LC3 [20] was a gift from Tamotsu Yoshimori ( Osaka University , Japan ) . pCMV-Eis-WT and pCMV-Eis-ΔAT constructs were created by subcloning the whole eis gene ( WT ) , or an acetyltransferase domain-deletion mutant ( lacking the sequence encoding residues 61–137 of the 402-amino-acid Eis protein ) from pET21a . This was achieved by cutting at the BamHI and SacI restriction sites and then ligating the resulting inserts into the pCMV-Tag1 mammalian expression vector ( Stratagene Co . , USA ) . Anti-LC3 antibodies ( Abs ) used for Western blotting and immunofluorescence analysis were purchased from Novus Biologicals and MBL International ( Woburn , MA , USA ) , respectively . Anti-rabbit IgG-Alexa488 and IgG-TRITC , and anti-mouse IgG-Cy2 , were purchased from Jackson Immunoresearch ( West Grove , PA , USA ) . siRNAs specific for mBeclin 1 ( sc-29798 ) , mAtg5 ( sc-41446 ) , and mJNK1 ( sc-29381 ) , each a pool of five target-specific 19–25 nt siRNAs , were purchased from Santa Cruz Biotechnology ( Santa Cruz , CA , USA ) . Cells were transfected with plasmids and/or siRNAs using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer's protocol . Intracellular ROS levels were measured by DCFH-DA and DHE assays as described previously [61] . Briefly , BMDMs were differentiated in culture dishes and infected with bacterial strains ( MOI = 10 ) for 30 min . Cells were then incubated with either DCFH-DA ( 5 µM ) or DHE ( 10 µM; Molecular Probes ) for 30 min at 37°C in 5% CO2 and then washed with Krebs-Hepes buffer ( for DHE staining ) or HBSS ( for DCFH-DA staining ) . Total intracellular levels of ROS were determined by FACS analyses of the oxidative conversion of cell-permeable DCFH-DA ( Molecular Probes ) to fluorescent DHE ( Molecular Probes ) , using the FACSCanto II system ( Becton Dickinson , San Jose , CA , USA ) . A mitochondrion-specific hydroethidine-derivative fluorescent dye ( MitoSOX; M36008; Calbiochem ) was used to determine relative mitochondrial O2− levels in BMDMs . Cells were incubated for 30 min in PBS containing 5 µM MitoSOX . They were then washed twice and analyzed using the FACSCanto II system . All FACS data were collected using 50 , 000 to 100 , 000 cells and analyzed using FlowJo software ( Tree Star , Ashland , OR , USA ) . Cell viability was assessed by PI staining and then examined by fluorescence microscopy or flow cytometric analysis . Trypan blue-stained cells were counted using a ViCell counter ( Beckman Coulter , Fullerton , CA , USA ) . Apoptosis was examined by TdT-mediated dUTP Nick-End Labeling ( TUNEL; Promega ) , according to the manufacturer's instructions . Labeled cells were examined under a laser-scanning confocal microscope ( model LSM 510; Zeiss ) . Each condition was assayed in triplicate , and at least 200 cells per well were counted . To analyze in vivo cell death , single-cell suspensions were prepared in RPMI 1640 medium by passing cell populations through a nylon mesh with 50 µm pores and were subjected to further analysis . Treated BMDMs were processed for analysis by sandwich ELISA , Western blotting , and RT-PCR as described previously [2] . For Western blot analysis , primary Abs were diluted 1∶1000 . Membranes were developed using a chemiluminescent reagent ( ECL; Pharmacia-Amersham , Freiburg , Germany ) and subsequently exposed to film ( Pharmacia-Amersham ) . Supernatant TNF-α and IL-6 levels were measured by sandwich ELISA using Duoset Ab pairs ( Pharmingen , San Diego , CA , USA ) [2] . To provide RNA for RT-PCR analysis , paraffin-embedded tissue sections were first deparaffinized in octane [62] . After vigorous vortexing , 150 µL of methanol were added . Samples were vortexed again and the tissue was pelleted by centrifugation ( 10 , 000×g , 2 min ) . Supernatants were removed , and the remaining tissue was vacuum-dried for 20 min . Next , pellets were resuspended in digestion buffer ( 20 mM Tris-HCl , pH 7 . 6 , 0 . 5% N-laurylsarcosine , 1 M guanidine thiocyanate , 25 mM 2-mercaptoethanol ) containing proteinase K ( 5 mg/mL; Sigma ) . After overnight digestion at 55°C , RNA was extracted using TRIzol ( Invitrogen ) according to the manufacturer's instructions . For quantitative RT-PCR analysis was performed by using SYBR Green ( Molecular Probes ) PCR core reagents ( Applied Biosystems ) , and transcript levels were quantified by using an ABI 7900 Sequence Detection System ( Applied Biosystems ) . The mean value of triplicate reactions was normalized against the mean value of β-actin . Primers were used at 400 nM . Autophagosome formation was measured by LC3 punctate staining , as described previously [8] . To quantitate autophagy , we used fluorescence microscopy to count the percentages of GFP-LC3-positive autophagic vacuoles in transfected cells or the numbers of endogenous LC3 punctate dots in primary cells . Each condition was assayed in triplicate , and at least 200 cells per well were counted . LC3 conjugation was evaluated by Western blot analysis using an antibody raised to LC3-I/II . Infected and stimulated RAW 264 . 7 macrophages were washed with PBS and then fixed with 3% formaldehyde , 2% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) for 1 h . They were then post-fixed in 1% osmium tetroxide , 0 . 5% potassium ferricyanide in cacodylate buffer for 1 h; embedded in straight resin; and cured at 80°C for 24 h . Ultrathin sections ( 70–80 nm ) , cut using an ultramicrotome ( RMC MT6000-XL ) , were stained with uranyl acetate and lead citrate and examined using a Tecnai G2 Spirit Twin transmission electron microscope ( FEI Company , USA ) and a JEM ARM 1300S High Voltage electron microscope ( JEOL , Japan ) . Immunofluorescence analysis was performed as described previously [8] . Briefly , cells were fixed with 4% paraformaldehyde in PBS at 4°C for 10 min and permeabilized with 0 . 01% Triton X-100 in PBS for 10 min . Cultures were then stained for 2 h at room temperature with primary antibodies , including rabbit anti-mouse LC3 ( 1∶400; MBL International ) . After washing , to remove excess primary antibody , cultures were then incubated for 1 h at room temperature with an anti-rabbit IgG-Alexa488 secondary antibody ( Jackson Immunoresearch ) . Nuclei were stained by incubation with DAPI for 5 min . Slides were examined using a laser-scanning confocal microscope ( model LSM 510; Zeiss ) . Data obtained from independent experiments ( presented as mean±SD ) were analyzed by the paired Student's t-test with Bonferroni correction or analysis of variance ( for multiple comparisons ) . A p value<0 . 05 was deemed to indicate statistical significance . The GenBank accession number for the eis gene is AF144099 . | Tuberculosis is a global health problem: at least one-third of the world's population is infected with Mycobacterium tuberculosis ( Mtb ) . Mtb is a successful pathogen that enhances its own intracellular survival by arresting phagolysosomal fusion . Recently , autophagy has emerged as a host defense strategy against Mtb infection , through stimulation of the fusion of phagosomes and lysosomes . However , excessive and uncontrolled autophagic activity can be detrimental to host cells and can result in their death . The Mtb “enhanced intracellular survival” ( eis ) gene has been implicated in the intracellular survival of M . smegmatis . However , its exact role and how it regulates host innate immune responses have not been fully explained . Here , we provide evidence that Eis suppresses macrophage autophagy , inflammation , and cell death through the inhibition of reactive oxygen species ( ROS ) generation . Although it has previously been demonstrated that autophagy is a key host defense response to mycobacterial infections , our data indicate that excessive autophagy , and the resulting cell death , do not significantly affect host defense responses to mycobacteria . Additionally , our data reveal that Eis's ability to regulate ROS generation and proinflammatory responses depends on its N-acetyltransferase domain . These results underscore a previously unrecognized role of Eis in modulating host inflammatory responses , oxidative stress , and cell survival/death during mycobacterial infection . | [
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] | 2010 | Mycobacterium tuberculosis Eis Regulates Autophagy, Inflammation, and Cell Death through Redox-dependent Signaling |
Plasmodium sporozoites are deposited in the skin by Anopheles mosquitoes . They then find their way to the liver , where they specifically invade hepatocytes in which they develop to yield merozoites infective to red blood cells . Relatively little is known of the molecular interactions during these initial obligatory phases of the infection . Recent data suggested that many of the inoculated sporozoites invade hepatocytes an hour or more after the infective bite . We hypothesised that this pre-invasive period in the mammalian host prepares sporozoites for successful hepatocyte infection . Therefore , the genes whose expression becomes modified prior to hepatocyte invasion would be those likely to code for proteins implicated in the subsequent events of invasion and development . We have used P . falciparum sporozoites and their natural host cells , primary human hepatocytes , in in vitro co-culture system as a model for the pre-invasive period . We first established that under co-culture conditions , sporozoites maintain infectivity for an hour or more , in contrast to a drastic loss in infectivity when hepatocytes were not included . Thus , a differential transcriptome of salivary gland sporozoites versus sporozoites co-cultured with hepatocytes was established using a pan-genomic P . falciparum microarray . The expression of 532 genes was found to have been up-regulated following co-culture . A fifth of these genes had no orthologues in the genomes of Plasmodium species used in rodent models of malaria . Quantitative RT-PCR analysis of a selection of 21 genes confirmed the reliability of the microarray data . Time-course analysis further indicated two patterns of up-regulation following sporozoite co-culture , one transient and the other sustained , suggesting roles in hepatocyte invasion and liver stage development , respectively . This was supported by functional studies of four hitherto uncharacterized proteins of which two were shown to be sporozoite surface proteins involved in hepatocyte invasion , while the other two were predominantly expressed during hepatic parasite development . The genome-wide up-regulation of expression observed supports the hypothesis that the shift from the mosquito to the mammalian host contributes to activate quiescent salivary gland sporozoites into a state of readiness for the hepatic stages . Functional studies on four of the up-regulated genes validated our approach as one means to determine the repertoire of proteins implicated during the early events of the Plasmodium infection , and in this case that of P . falciparum , the species responsible for the severest forms of malaria .
Protozoan parasites of the genus Plasmodium are the causative agents of malaria , the most devastating parasitic disease in humans . The infection is initiated when Plasmodium sporozoites are deposited in the skin of their vertebrate hosts through the bite of an infected female Anopheles mosquito . The sporozoites released from oocysts in the mosquito migrate to the salivary gland where they lodge in the acinar lumen ready for inoculation during a feeding bite . In the mosquito , salivary gland sporozoites retain infectivity for many days , and even weeks under optimal conditions of temperature and humidity . Once inoculated into the mammalian host , the sporozoites migrate to the liver where they cross the sinusoid wall and subsequently migrate through several hepatocytes before infecting a final hepatocyte [1] , [2] . Although some sporozoites can reach the blood circulation very rapidly [3] , recent studies suggest that the majority trickle out of the injection site over several hours [4]–[6] . There are clear indications that sporozoites retain infectivity at least one hour in vivo [3] , [6] , which contrasts with the rapid loss of infectivity observed when sporozoites are maintained in vitro at 37°C [7] . Incubation at the higher temperature does not necessarily lead to the death of all sporozoites , since it was shown that about one in 10 sporozoites incubated for 24 hours at 37°C in the presence of serum transform into forms morphologically indistinguishable from early exo-erythrocytic parasites [8] . These observations , indicate that when shifted from the insect to the mammalian host environment , the relatively quiescent salivary gland sporozoites are somewhat activated in preparation for hepatocyte infection and that their infectivity is preserved until then . The above observations led us to hypothesise that both the shift in temperature and contact with host cells contribute to the preservation of sporozoite infectivity and its activation . In that case , the genes whose expression is modified by the shift to the mammalian host environment could be identified by whole genome transcriptome analyses . Sufficient quantities of parasite material necessary for such analyses cannot be obtained from in vivo infections . Therefore , we have subjected sporozoites to host-like conditions in vitro . We have opted for the hepatocyte to represent host cell contact , as hepatocytes are the only cell type in which the sporozoite develops to maturity in the host . Furthermore , it has been recently shown that specific molecular interactions with hepatocyte activate sporozoites for invasion [9] . Although Plasmodium species that infect rodents offer an adequate and practical model for the study of pre-erythrocytic stages , about a third of the genes found in P . falciparum , the parasite associated with the bulk of mortality and morbidity due to malaria , do not have orthologues in these species [10] . Therefore , the studies presented here were conducted using P . falciparum sporozoites and human primary hepatocytes .
In order to ensure that data whole genome transcription analyses were valid biologically , we considered it necessary to ensure that the sporozoites experimentally subjected to host-like conditions were still infectious . For P . falciparum sporozoites biological investigations are ethically restricted to in vitro infections thus , infectivity assays can only be conducted in primary human hepatocytes [11] . In a first series of experiments ( Figure 1A ) , sporozoites were incubated at 37°C in medium supplemented with serum for different periods of time , and then tested in the standard sporozoite infectivity assay ( see Materials and Methods ) . Infectivity was substantially reduced by 30 minutes of incubation ( 15% of control ) and nearly fully abrogated after two hours ( 2% of control ) . When incubation at 37°C was conducted in the presence of a cell line ( HaCaT ) derived from human skin keratinocytes or of primary human hepatocytes , the loss of infectivity could be fully prevented during the first hour of incubation ( 37°C ) , and >50% of the sporozoites retained infectivity by the end of the second hour of incubation ( Figure 1A ) . In parallel , we analysed the ability of the sporozoites to migrate through cells , a phenomenon associated with successful infection [12] . Incubation of sporozoites at 37°C in the absence of host cells was similarly shown to adversely affect sporozoite migration ability ( Figure 1B ) , whereas in the presence of skin keratinocytes or primary hepatocytes P . falciparum sporozoite migration ability was mostly preserved after one hour incubation ( Figure 1B ) . In one final experiment , it was established that when the initial incubation periods of the standard sporozoite infectivity assay were carried out at room temperature ( See Materials and Methods ) , the efficiency with which sporozoites invade human primary hepatocytes was drastically reduced ( <25 infected hepatocytes per well , data not shown ) . These observations demonstrated that efficient hepatocyte infection requires a shift in temperature ( to 37°C ) , which on its own would lead to a rapid loss of infectivity in vitro . However , this loss was substantially delayed when the incubation at 37°C was carried out in the presence of human cells . In order to explore the molecular basis for differences between quiescent salivary gland and invasion-ready P . falciparum sporozoites , their transcriptomes were sought using a DNA microarray covering the whole P . falciparum genome [13] . Given the large numbers of P . falciparum sporozoites needed , hepatocytes , rather than keratinocytes , were chosen as the host cells with which the sporozoites were to be incubated . This choice was dictated by two reasons: first , hepatocytes are the specific host cell in which sporozoites develop , and second , primary human hepatocytes directly isolated from the liver were deemed more suitable than a cell line of keratinocytes adapted to in vitro cultivation . It was considered likely that the viable and infectious sporozoites obtained by incubation at 37°C in the presence of hepatocytes for 1 hour were physiologically similar to the sporozoites found into the mammalian host immediately prior to productive hepatocyte invasion event . The transcriptome of P . falciparum salivary gland sporozoites had been previously derived using Affymetrix GeneChip arrays [14] , though an amplification step was needed to compensate for the relatively small quantities of available RNA . In order to increase signal detection sensitivity without the potential bias inherent to RNA amplification , radiolabeled cDNA [15]–[17] was used to probe an oligonucleotide ( 70-mer ) microarray [13] . In order to optimise specificity , the RNA used was obtained from highly purified salivary gland sporozoites , and an excess of unlabelled RNA purified from uninfected mosquitoes was added to the microarray hybridisation mix . Whole transcriptome profiling was carried out for P . falciparum sporozoites incubated for 1 hour at 37°C with primary human hepatocytes , and for control salivary gland sporozoites . The transcriptome data was derived from three independent experiments each conducted with a different lot of sporozoites . When the microarray datasets for the salivary gland and the incubated sporozoites were compared , the expression levels of 611 genes were found altered by 2-fold or more ( Table S1 ) . Steady-state RNA levels were decreased ( down-regulated ) for 79 genes and increased ( up-regulated ) for 532 genes . For both the up- and the down-regulated genes , 311 encode hypothetical proteins and 300 encode annotated predicted proteins . These 300 proteins could be classified into 13 families ( Table S1 ) , five of which were only represented in the up-regulated subset: proteins associated with parasite invasion ( n = 13 ) , metal-ion homeostasis ( n = 3 ) , or cytoskeleton ( n = 9 ) and stress responses ( n = 15 ) . Members of the remaining eight families were noted in both subsets and included proteins expressed on the surface of the infected RBC surface , or in the parasitophorous vacuolar membrane ( PVM ) ( Table S1 ) . The predicted proteins encoded by 19% of the down-regulated and 23% of the up-regulated genes had been previously detected by mass-spectrometry in P . falciparum salivary gland sporozoite extracts [18] . It was considered that the down-regulated genes were less likely to be implicated in downstream events and these were not analysed further at present . The magnitude of the up-regulation observed did not exceed 30-fold , and only 13 genes were found to be up-regulated 10-fold or more . In order to validate the notion that genes with up-regulated expression are indeed likely to be implicated in events leading and contributing to the liver phase of the infection , genes coding for proteins known to play a role in the hepatic stages were sought amongst the up-regulated genes . Several heat shock protein genes were found , including that of HSP-70 found up-regulated in P . berghei sporozoites transformed by incubation at 37°C in axenic cultures [8] . Furthermore , the orthologue genes of rodent malaria proteins implicated in liver stage maturation , UIS3 [19] and UIS4 [20] , or located in the PVM , EXP-1 [21] and UIS4 [20] , were also found . It is interesting to note that four members of the Etramp gene family [22] , in addition to that of UIS4 , were also present , as was the gene of PfEXP-2 [23] an erythrocytic parasite PVM protein . Several parasite genes that encode proteins known to be involved in hepatocyte invasion , TRAP [24] , AMA-1 [25] , SPATR [26] , SPECT-1 [27] , SPECT-2 [28] , phospholipase [29] and aldolase [30] , were also present within the up-regulated subset . Despite the difficulties in obtaining simultaneously large numbers of P . falciparum sporozoites and primary human hepatocytes , the microarray data was obtained from three replica runs . Only those genes , for which the steady-sate expression level data from the three experiments were similar , were retained as hits . In order to obtain an independent confirmation of the validity of microarray data , 21 up-regulated genes were selected and their expression levels analyzed by Taqman quantitative RT-PCR ( RT-qPCR ) ( Figure 2 ) . Five genes encoded proteins known to be implicated in hepatocyte invasion ( TRAP , AMA-1 , SPECT-2 , and aldolase ) , or to be expressed in the hepatic stages ( HSP-70 ) . Ten other genes were predicted to encode proteins likely to be expressed during hepatic stage development as they are implicated in transcription , transportation , metabolism , proteolytic pathway or are known to be located at the PVM ( PfEXP-2 and Etramp 8 ) . The remaining six genes mainly encoded hypothetical proteins , one of which ( PFD0425w ) was reported to be recognized by immune cells obtained from human immunized by radiation-attenuated sporozoites [31] and is an orthologue of a genes reported to be expressed in P . yoelii sporozoites [32] . The proteins encoded by these 21 genes included some that were predicted to have a signal peptide , or a transmembrane domain , both , or neither . Two genes whose expression was found to be unaffected by activation were included as controls: the gene for the circumsporozoite protein ( CSP ) , and PFL0800c that encodes the orthologue of P . berghei CelTOS , a micronemal protein involved in cell migration through the sinusoidal cell layer [33] . RT-qPCR analysis preformed with sporozoites incubated for 1 hour at 37°C in the presence of hepatocytes , confirmed the microarray data derived from equivalent material for all the 21 genes tested ( Figure 2 ) . The time course of up-regulation for the selected 21 genes was determined by RT-qPCR using RNA purified from salivary gland sporozoites collected after incubation for 30 minutes , one hour , or two hours at 37°C . Incubation was performed either in the presence or in the absence of hepatocytes ( Figure 2 ) , because this might provide and indication of the relative contribution of host cell contact and temperature shift to the up-regulation of expression . For sporozoites incubated at 37°C in the presence of hepatocytes , two patterns could be distinguished . A modest transient up-regulation ( median up-regulation 1 . 5 , 2 . 2 . and 1 . 7 for the three time points ) was noted for 8 genes , with the peak observed after 30 minutes or after 1 hour incubation , however , no significant up-regulation ( <2-fold ) was observed for these genes when the sporozoites were solely incubated at 37°C ( P<0 . 05 using the one-way analysis of variance , Figure 2 ) . The four genes encoding proteins known to be implicated in hepatocyte invasion displayed this transient low-level up-regulation pattern . Sustained up-regulation was noted for the other 13 genes , increasing throughout incubation to reach relatively high levels after two hours ( median up-regulation 2 . 7 , 4 . 1 and 7 . 3 for the three time points ) . This pattern was equally observed in sporozoites incubated at 37°C either in presence or in the absence of hepatocytes ( Figure 2 ) . The group of genes that displayed the continuous up-regulation pattern included all those encoding proteins known or likely to be expressed in infected hepatocytes . We hypothesised that proteins of unknown function encoded by genes with a transient pattern of up-regulation were likely to be involved in the migratory and invasive processes of the sporozoite in the mammalian host , while those with a continuous up-regulation pattern were likely to be related to parasite development within the hepatocyte . In order to test this hypothesis , specific antibodies were sought in order to conduct functional analyses . Success in producing recombinant proteins and in raising specific antibodies was met for 4 genes: PFD0425w and PF08_0005 that showed the transient pattern of up-regulation , and PFL0065w and PFB0105c that showed the continuous pattern of up-regulation . Antibodies were raised against a mixture of three polypeptides of 40 kDa that cover the entire PFD0425w protein ( 3 kb ORF with a predicted 113 kDa polypeptide with transmembrane domains ) , and against the PF08_0005 full recombinant protein ( 1 . 2 kb ORF encoding a predicted 45 kDa polypeptide with no transmembrane domains ) . Both proteins have a predicted signal peptide suggesting that they might be targeted to the secretory pathway and might be surface exposed . Western blotting was performed on pellets and supernatants from P . falciparum sporozoites incubated or not at 37°C . Antibodies raised against the PFD0425w protein revealed a ≈95 kDa polypeptide in the pellet from control sporozoites ( Figure 3 ) that was barely detectable in the pellet from 37°C-incubated sporozoites . By contrast , no reactivity was observed in the supernatant fraction from control sporozoites , whereas a ≈75 kDa band could be seen in that from the 37°C-incubated sporozoites . This pattern is reminiscent of AMA-1 and TRAP cleavage in sporozoites [25] . Antibodies raised against the PF08_0005 protein , revealed a ≈75 kDa protein in the pellets and supernatant fractions from both sporozoite preparations ( Figure 3 ) . The recombinant protein migrated at the 45 kDa level ( data not shown ) , thus the higher size of the in vivo protein might be due to post-translational modifications . There was a clear though modest increase in the amount of shed protein in the supernatant of 37°C-incubated as compared to control sporozoites . Localisation of the two proteins in the parasite was deduced from immunofluorescence assays ( IFA ) performed on salivary gland sporozoites . Specific staining for either protein revealed a strong peripheral and granular fluorescence similar in location and pattern to that obtained for CSP , suggesting localization on the sporozoite surface ( Figure 4 ) . The functional role of these proteins was explored by antibody inhibition assays . Pre-incubation of sporozoites with either of the two specific antibodies significantly decreased the percentage of cell traversal ( Figure 5A ) . This inhibitory activity was comparable to that observed with anti-CSP serum at the 1∶100 dilution . The effect of the two antibodies on hepatocyte infection was then assessed using primary human hepatocytes to which P . falciparum sporozoites were added [34] . Again both antibodies led to a significant dose-dependent inhibition of invasion ( Figure 5B ) , though inhibition was half that observed for anti-CSP serum at dilution of 1∶100 . In order to investigate the fate of the two proteins after hepatocyte invasion , IFA were performed on P . falciparum-infected primary human hepatocytes throughout the developmental stages ( days 1 to 5 post-infection ) . Neither of the two proteins could be detected in liver stage parasites , including early forms ( data not shown ) , strongly indicating that they were lost after hepatocyte invasion . Furthermore , when IFA and Western blots were performed on mixed blood stage parasites , no signal could be detected ( data not shown ) , an observation consistent with the low levels of expression recorded in the blood stage parasite transcriptome [14] , [35] and the absence from the blood stage proteome [18] . These observations are consistent with predominant expression in the sporozoite and a role in hepatocyte invasion . Thus , the proteins encoded by PFD0425w and PF08_0005 were named Sporozoite Invasion-Associated Protein-1 and -2 ( SIAP-1 and SIAP-2 ) , respectively . Orthologues of SIAP-1 were found in the genomes of Plasmodium species that infect rodents ( P . berghei , P . chabaudi and P . yoelii ) and primates ( P . reichenowi , P . vivax and P . knowlesi ) , whereas SIAP-2 orthologues were only found in those that infect primates . Genes PFL0065w and PFB0105c encoded 2 proteins , with predicted transmembrane domains , of 12 kDa and 35 kDa , respectively . PFL0065w was characterized by the presence of a predicted signal peptide and PFB0105c by the presence of a PEXEL/VTS trafficking motif and a PHISTc domain [36]–[38] . Antibodies were raised against the full-length recombinant proteins and used as above in Western blots of sporozoites . No specific bands could be detected for the 2 proteins even when total extracts from 2×106 sporozoites were probed ( data not shown ) . When IFA were conducted on sporozoites , no reactivity was detected using anti-PFB0105c serum , but a weak internal punctuate staining was noted using anti-PFL0065w serum ( Figure 4 ) . Thus , it is likely that PFB0105c is not significantly expressed in sporozoites , whereas PFL0065w might . Nonetheless , anti-PFL0065w serum had no significant effect on sporozoite cell traversal , or on sporozoite invasion of primary human hepatocytes ( Figure 5 ) . These observations , suggested that the PFL0065w protein , if present in sporozoites , it would be so in small quantities and located internally . When IFA were performed on liver stage parasites , the PFL0065w protein was detected on transforming sporozoites ( Figure 6A , day 1 ) within hepatocytes but not on those outside the hepatocyte ( data not shown ) , and throughout liver stage development up to late schizont forms ( Figure 6B day 7 ) . By contrast , PFB0105c was detected only in older parasites , from trophozoite ( 2 days culture ) up to the late schizont forms 7 days post-infection ( Figure 6C and 5D ) . PFL0065w staining was strong , smooth and peripheral with one or several broad reinforcements observed within most exo-erythrocytic forms ( EEF ) . The peripheral pattern overlapped extensively with the weak and discontinuous pattern observed for CSP , while in the regions of reinforcement PFL0065w staining appeared to lie outside the parasite surface with the fluorescence reaching into the hepatocyte cytoplasm ( Figure 6B ) . PFB0105c staining displayed an uneven and peripheral pattern mostly co-localized with CSP with fluorescence reaching into the parasite cytoplasm and increasing in intensity with parasite age ( Figure 6D ) . As the hepatic parasites matured , the PFL0065w staining pattern that was reminiscent of that previously observed for LSA-1 [39] , suggested that the protein might be distributed around the developing schizonts ( Figure 6B , day 5 ) before becoming confined around the cytomeres just before merozoite individualization ( Figure 6B , day 7 ) . It was interesting to observe that for PFB0105c , the staining in the maturing schizont was confined to granular areas with sharply defined edges between the nuclei of developing merozoites ( Figure 6D , day 7 ) . No PFL0065w protein was detected by IFA or Western blots performed on mixed blood stage parasites ( data not shown ) , whereas a moderate PFB0105c IFA signal was detected on mature erythrocytic parasites ( Figure S1 ) . These observations were consistent with data from the P . falciparum blood stages transcriptome [14] , [35] and its blood and mosquito stages proteomes [18] . Taken together the above observations strongly suggest that PFL0065w and PFB0105c were mainly expressed in the liver stages of P . falciparum . These proteins were consequently named Liver Stage-Associated Protein -1 and -2 ( LSAP-1 and LSAP-2 , respectively ) . It was only possible to identify putative orthologue of LSAP-1 and LSAP-2 in the genomes of Plasmodium species that infect primates , P . reichenowi , P . vivax and P . knowlesi for LSAP-1 , but only P . vivax for LSAP-2 [38] .
Plasmodium sporozoites are abruptly subjected to the environment of the warm-blooded host often after extended periods of quiescence in the insect’s salivary gland lumen . In the mammalian host , the inoculated sporozoites are likely to remain extracellular for a few hours before infecting the hepatocytes , the only cell type where they can develop to maturity . We hypothesized that during this period , sporozoites are activated to a state of readiness for hepatocyte invasion . Parasite material corresponding to this transition period suitable for molecular investigations would be very difficult to obtain in vivo , especially if P . falciparum sporozoites are to be investigated . We explored the influence of temperature and host cell contact , the two host environmental factors amenable to investigation in vitro , on the infectivity of P . falciparum sporozoite to hepatocytes [11] . We demonstrated that a temperature shift to 37°C is required to make salivary gland sporozoites infective to hepatocytes , though infectivity is lost within 30 minutes when only the sporozoites are incubated at this temperature . We showed that the loss in the P . falciparum sporozoites' infectivity can be prevented when incubation at 37°C is made in the presence of either of two types of human cells , skin keratinocytes or primary hepatocytes ( Figure 1 ) . Recently , in vivo studies in rodents showed that the majority of sporozoites are deposited in the skin [4] , [40] and migrate away from the site of inoculation over the next few hours [4]–[6] . One could speculate that residence in the mammalian host for a few hours is needed to bring the majority of the salivary sporozoites from a state of quiescence to that of hepatocyte-invasion readiness . Our observations performed in vitro with a human skin cell line or with human primary hepatocytes are consistent with this notion . The role of higher temperature ( 37°C ) in optimal sporozoite infectivity might be simply explained in terms of the metabolic activation required to power motility . However , our results and previous studies indicate that this temperature shift has more wide-ranging consequences . First , sporozoites motility per se occurs without a temperature rise in the mosquito since the sporozoites released from the oocysts into the haemolymph migrate to and invade salivary gland sporozoites , albeit the mechanisms and speed of migration might differ from those in the mammalian host . Second , it was shown that a shift to 37°C activated exocytosis of P . falciparum sporozoite micronemes , a phenomenon associated with productive infection of hepatocytes , and this was enhanced when parasites were incubated with hepatocytes [25] . Third , a mere shift to 37°C , in the presence of serum , was sufficient to induce the transformation of P . berghei sporozoites into forms morphologically indistinguishable from early EEF [8] . Nonetheless , the importance of host cell contact was clearly demonstrated by the ability of hepatocytes to preserve sporozoite infectivity during lengthy incubation at 37°C ( Figure 1 ) . We considered that the infectious sporozoites obtained after incubation in the co-cultures were physiologically similar to those found in vivo a few hours after the infectious mosquito bite . Thus , it was possible to obtain large numbers of viable and infectious sporozoites after incubation at 37°C in the presence of hepatocytes , so as to conduct whole transcriptome profiling analyses that would identify the modifications in steady-state levels of transcripts induced by the insect-to-mammalian host transition . It was clear that exposure of salivary gland sporozoites to 37°C in the presence of hepatocytes for merely one hour , triggered complex and genome-wide changes in transcript levels . This substantial gene modulation probably participated to prepare , or possibly to activate , the sporozoite for successful infection . Indeed , the mRNA levels for 611 genes were altered in activated P . falciparum sporozoites , and for most genes ( 532/611 ) there was up-regulation . The genes identified included those that encode proteins involved in parasite transcription , translation , signalisation pathways , transportation , metabolism and also invasion . These observations are consistent with the concept that sporozoites that are ready to invade the hepatocyte productively are functionally different from the salivary gland sporozoites inoculated by the mosquito . To date , little data is available on gene expression in P . falciparum pre-erythrocytic stages . Three genome wide expression data sets have become recently available for P . yoelii pre-erythrocytic stages: a cDNA library from sporozoites transformed axenically into early EEF forms , where 652 unique transcripts were identified [41]; a cDNA library of laser capture microdissected mature liver stages , where 623 unique transcripts were identified [42]; and most recently the transcriptome of liver stages at three points during their development , where 1985 actively transcribed genes were identified [43] . From a technical point of view , the data we present is most significantly comparable to that of Tarun et al . 2008 [43] since in both cases microarray analysis of the transcriptome was carried out , and changes in steady state levels were used as a criterion for gene identification . However , the data from Wang et al . 2004 [41] , is biologically more relevant to our data , since it is derived from sporozoites that had been incubated at 37°C . Of the 611 P . falciparum genes identified in this study , 120 did not have orthologues in any of the three species that infect rodents according to orthology mapping data of Tarun et al . 2008 ( see Figure S2 and Table S1 ) . Of the remaining 491 P . falciparum genes with such orthologues , 321 were not represented in the sets identified in P . yoelii transformed sporozoites by Wang et al . 2004 , or in the liver stage parasites used by Tarun et al . 2008 . These relatively low levels of overlap are probably due to the fact that the data sets were derived from different stages: maturing hepatic stage parasites [43] or sporozoites incubated at 37°C for 24 hours in the absence of hepatocytes [41] as compared to the sporozoites incubated for 1 hour in the presence of hepatocytes from which our data was derived . Confirmation that the microarray data presented reliably identified up-regulated genes was obtained through independent RT-qPCR analysis of a subset of 21 up-regulated genes . The RT-qPCR analysis further afforded the opportunity to investigate the time course of up-regulation over two hours of activation , and to explore the relative contribution of temperature or host cell contact on the alterations in gene expression . Two distinct patterns of up-regulation were observed ( Figure 2 ) . It was hypothesised that these patterns can provide an indication as to the likely role of the corresponding proteins . The modest transient up-regulation observed following incubation at 37°C in the presence of hepatocytes was characteristic for the selected genes known to be associated with invasive processes . In this case , it would appear that contact with hepatocytes alone accounted for the observed up-regulation ( Figure 2 ) , since no up-regulation was observed when the sporozoites were incubated at 37°C without hepatocytes . For the group of genes that included those encoding proteins known or likely to be expressed during hepatic stage development , up-regulation increased continuously with the duration of incubation at 37°C in the presence of hepatocytes , and the levels reached tended to be high . In this case , the shift in temperature seemed to be the effective signal ( Figure 2 ) , since similar changes in expression were observed irrespective of the contact with hepatocytes . At this stage , it is not possible to conclude that these increases translate into higher protein levels . Although global analysis of protein and RNA levels showed a positive correlation between mRNA and protein abundance , delays between mRNA and protein accumulation were noted for many genes [44] . This issue can only be addressed by analysis of individual genes and their products . Functional studies of two hypothetical proteins encoded by genes with the transient pattern of up-regulation , confirmed that they were likely to be implicated in host cell invasion . IFA staining patterns suggested that SIAP-1 and SIAP-2 were located at the surface of the sporozoite . Western blotting showed that they were released in the supernatant upon incubation at 37°C , and that SIAP-1 was cleaved after release . Further studies are required to determine whether cleavage is achieved using the same proteolytic machinery implicated in the shedding of TRAP and AMA-1 [25] . Cell invasion inhibition assays showed that both anti-SIAP-1 and anti-SIAP-2 blocked sporozoite migration and productive invasion of hepatocytes , further supporting surface localization . The fact that inhibition levels for cell traversal were similar to those afforded by anti-PfCSP antibodies , while those for hepatocyte invasion were lower ( in particular for SIAP-2 ) suggest that SIAP-1 and SIAP-2 might have a more prominent role in sporozoite gliding than in hepatocyte invasion . Future studies would require inhibitory activities to be measured in terms of the quantity of specific antibodies rather than in terms of dilution to evaluate the potential of these 2 proteins as vaccine candidates . It should be noted that the P . yoelii orthologue of SIAP-1 was detected in the proteome of the mature liver stages of this parasite [43] , but not in that of P . berghei parasites . It is not clear at present whether the IFA negative results obtained for P . falciparum liver stage reflect the absence of SIAP-1 , post-translational modifications that adversely affect antibody recognition , or species differences . The modest increases noted for transiently up-regulated genes is consistent with proteins associated with motility and/or invasion , because these proteins are likely to be already present in salivary gland sporozoites , as is the case for TRAP and aldolase that participate in the motor machinery powering gliding motility [24] , [30] , [45]–[47] . At best , the transient up-regulation of invasion-related genes might serve to compensate for the loss of invasive proteins shed by the parasite during migration to its final target cell . Characterization of the two hypothetical proteins encoded by genes with the continuous pattern of up-regulation showed that they were expressed throughout hepatic parasite development . The two corresponding genes had reached high expression levels as a result of activation . Detectable increases in LSAP-1 protein levels were noted as early as day 1 post-inoculation in transforming sporozoites , whereas LSAP-2 protein could only be detected from day 2 post inoculation . LSAP-1 was mainly found at the periphery of the intracellular hepatic parasite throughout its development , but not in blood stage parasites and possibly in minor quantities in salivary gland sporozoites . Thus , LSAP-1 might represent the second liver stage-specific protein to be identified in P . falciparum , the other one being LSA-1 [48] . LSAP-2 was also mainly expressed at the periphery of the intracellular hepatic parasite , but it was additionally detectable in moderate quantities in blood stage parasite though not in salivary gland sporozoites . The presences of a PEXEL/VTS domain in LSAP-2 and of a signal peptide in LSAP-1 are consistent with location at the PVM and eventual export to the host cell cytoplasm , though the proteins were not detected in the cytoplasm of infected hepatocytes . Knock-out studies , such as those used for UIS3 [49] , UIS4 [20] and P36p [50] will be required to ascribe a function for these protein or to establish whether they are essential for liver stage development . It is interesting to note that there were no identifiable orthologues for either gene in the rodent model species , as was the case for SIAP-2 and LSA-1 , which suggests that they might interact with components specific to the primate hepatocyte . In conclusion , whole genome transcriptome analysis of P . falciparum sporozoite activated by relatively brief exposure to mammalian host-like conditions , revealed major changes in gene expression . A fifth of genes identified as being up-regulated in activated P . falciparum sporozoites had no orthologues in the genomes of the Plasmodium species used as experimental models in rodents . Some of the proteins encoded by these genes might have specifically evolved to optimise the interactions between the parasite and the human liver . The data presented provides a gateway for the identification of new P . falciparum genes implicated in the processes of hepatocyte invasion and liver stage development , and as such some of these might prove valuable as potential vaccine targets . In a previous study , 16 P . falciparum sporozoite proteins were identified as being highly antigenic based on stimulation of immune cells obtained from volunteers immunized with radiation-attenuated sporozoites [31] . Five of these proteins including SIAP-1 were encoded by genes up-regulated in the activated sporozoite transcriptome ( Table S1 ) . It is hoped that future studies might provide the basis for unravelling the biology of , and eventually elaborate therapies against , the few short-lived stages that occur between the bite of an infected mosquito and the release of merozoites , which constitute the obligatory steps without which the establishment of a Plasmodium infection cannot occur .
Adult Anopheles stephensi females were infected with the NF54 strain of P . falciparum [51] . After 14–21 days , the salivary glands were aseptically dissected and sporozoites were purified over a 27% then a 75% Percoll gradient and kept at 4°C . Primary human hepatocytes were isolated from human liver fragments , collected during unrelated surgery after informed consent and in agreement with French national ethical regulations , and were cultivated as described elsewhere [25] . Hepatocytes were seeded in 24-well culture plates ( 5×105 cells/well ) for incubation with sporozoites , in eight-chamber plastic Lab-Tek slides ( 2 . 1×105 cells/well ) for in vitro culture of liver stage parasite , or in 6-well culture plates ( 2 . 5×106 cells/well ) for P . falciparum sporozoite transcriptome analyses . Human hepatocytes were cultured for at least 24 hour before inoculation with P . falciparum sporozoites . After the removal of medium from the culture chambers , sporozoites in culture medium were added to the Lab-Tek wells ( 1×105 sporozoites/well ) . After 3 hours at room temperature ( for sporozoite sedimentation ) and 3 hours at 37°C ( for sporozoite invasion ) , the cultures were washed to remove sporozoites that have not penetrated hepatocytes , and then incubated at 37°C in fresh medium for 3 days to obtain liver schizonts . Hepatic cultures were then fixed in methanol and stained using an anti-HSP-70 mouse serum followed by goat anti-mouse Alexa 494 conjugate ( Invitrogen , Paisley , United Kingdom ) and counted under a fluorescence microscope . Typically , 300 to 1000 hepatic parasites are obtained per well . Infectivity was expressed as the percentage of the number of schizonts observed as compared to that obtained in controls where salivary gland sporozoites were used . The importance of the temperature shift on sporozoite infectivity was determined by incubating the sporozoites with hepatocytes for the whole 6 hours at room temperature prior to washing and subsequent incubation for 3 days at 37°C . The infectivity of sporozoites subjected to different conditions ( data presented in Figure 1 ) was assessed using the standard sporozoite infectivity assay . Salivary gland sporozoites were added to 24-well culture plates that were seeded or not with human primary hepatocytes or human skin keratinocytes ( HaCaT cell line , a gift from Dr Alain Simon , Faculty of Pharmacy , Limoges , France ) . The plates were then centrifuges ( 1800×g for 5 min ) at room temperature to ensure optimal contact with the host cells , and then incubated for 30 minutes , 1 hour , or 2 hours at 37°C . At the end of the incubation period , the sporozoites that had not invaded or irreversibly attached were recovered by washing and counted ( 80% recovery was generally obtained ) . The infectivity of these sporozoites was then tested by the standard sporozoites infectivity assay ( see above ) . Cell-traversal activity of sporozoites was assessed as described previously with slight modifications [2] . Sporozoites ( 3 . 0×105 ) were added to HeLa cells seeded on a 96-well plate . The plate was centrifuged to spin down the sporozoites and then incubated for 3 hours in the presence of 0 . 5 mg/ml FITC-dextran 10000 MW ( Invitrogen , Paisley , United Kingdom ) at 37°C . The cells were then washed , trypsinized , and resuspended with 1% formaldehyde in PBS . The percentage of wounded cells ( FITC-positive ) was determined by FACS . Activated sporozoites were obtained as follows: 40 millions P . falciparum salivary gland sporozoites were added to human primary hepatocytes cultures , and the plates centrifuged ( 1800×g for 5 min ) at room temperature to ensure optimal contact with the host cells . The plates were then incubated at 37°C for 1 hour , at the end of which unattached sporozoites were recovered by washing ( >80% recovery ) and enumerated . An equivalent number of salivary gland sporozoites ( to the ones recovered above ) were used as control sporozoites . RNA from both sporozoite populations was purified using an RNeasy micro kit ( Qiagen , Hilden , Germany ) . [3H]-radiolabelled cDNA , synthesized from 0 . 25 µg total RNA [17] , was hybridized to a pan-genomic DNA microarray containing 12037 unique 70-mer oligonucleotides [13] , in the presence of 5 µg of total RNA purified from uninfected mosquitoes . Acquisition of the arrays was carried out as described previously [52] . Three biological replicates for each condition were performed and data were normalized according to the median value of the total intensities of all spots . Background was defined as the average value of the control spots without oligonucleotides . Genes with an intensity >3 fold the background ( the average of three independent experiments ) either in the sporozoites incubated with hepatocytes or in the salivary gland sporozoites conditions , and with a two-fold or more change in expression between the two types were considered to be up- or down-regulated . Forty millions control sporozoites and the same number of sporozoites added to culture plates and then centrifuged before incubation at 37°C in the presence or in the absence of human hepatocytes were processed for RNA extraction . Total RNA was treated with DNase and then reverse transcribed . TaqMan PCR data was derived from two independent experiments each performed in triplicate using the cDNA produced from 10 ng of total RNA [17] . None of the primers and probes ( Table S2 ) cross-reacted with mosquito or human DNA ( data not shown ) . Control reactions were performed without reverse transcriptase to verify the absence of genomic DNA contamination ( data not shown ) . Quantitative gene expression data were normalized with respect to the parasite's 18S RNA cDNA levels and relative gene expression was expressed via the log2 of the ratio expression using the 2T−ΔΔC method [53] . Since several hours were needed for the dissection of the 1200 mosquitoes required to gather the 80 million sporozoites used for transcriptome profiling , it was necessary to store them at 4°C until incubation at 37°C , because storage at room temperature for extended periods would have led to a loss in motility and infectivity [7] . In order to confirm that storage of sporozoites on ice for many hours did not influence the pattern of up-regulation observed , RT-qPCR was conducted for a subset of 13 out of the 21 genes included in the RT-qPCR analysis . Sporozoite samples were kept at 4°C for 2 hours before incubation at 37°C with hepatocytes , or were directly incubated after dissection at room temperature . No differences in the levels of up-regulation were noted between the two groups of sporozoites ( Table S3 ) . DNA fragments corresponding to coding sequences of SIAP-1 ( PFD0425w ) , SIAP-2 ( PF08_0005 ) , LSAP-1 ( PFL0065w ) and LSAP-2 ( PFB0105c ) were PCR amplified from 3D7 P . falciparum genomic DNA and inserted into a pEXP5-Nt/TOPO or pEXP5-Ct/TOPO plasmid ( Invitrogen , Paisley , United Kingdom ) , both His-Tag expression vectors . Recombinant proteins were expressed using an Active-pro in vitro translation kit ( Ambion ) and purified with Ni-Sepharose-6-Fast-Flow beads ( GE Healthcare , Buckinghamshire , United Kingdom ) following the manufacturer's batch protocol . His-Tagged proteins linked to Ni-Sepharose-6-Fast-Flow beads were washed with Phosphate-buffered saline ( PBS ) and injected intra-peritoneally into female BALB/C or Swiss mice at 2 week interval . One week after the third immunization , blood was collected to prepare serum . Specificity of the anti-sera , from BALB/C ( SIAP-1 , SIAP-2 , LSAP-2 sera ) or Swiss ( LSAP-1 serum ) mice , was confirmed by probing Western blots of the recombinant proteins ( data not shown ) . Purified sporozoites were equally divided and suspended in medium without serum and incubated for 2 hours either at 4°C or 37°C . Supernatants and sporozoite pellets , obtained after centrifugation at 16 , 000×g for 3 minutes , were dissolved in Laemmli buffer and incubated at 90°C for 5 minutes , before being subjected to 10% -15% SDS-PAGE ( 1×106 parasites/lane for SIAP detection , and 3×104 parasites/lane for CSP detection ) and transferred onto nitrocellulose membranes . These were incubated for 1 hour with Odyssey blocking solution ( Li-cor , Lincoln , USA ) and probed overnight at 4°C with primary antibody [dilution 1/500 for anti-SIAP sera and 1/15000 for monoclonal E9 anti-CSP antibodies [54]] . Membranes were then incubated for 1 hour at room temperature with an IRDye 800CW Goat Anti-Mouse IgG secondary antibody ( Li-cor , Lincoln , USA ) and immuno-stained proteins were visualized using the Odyssey infrared imaging system ( Li-cor , Lincoln , USA ) . Non-infected salivary glands and E . coli protein extracts were used as negative controls ( data not shown ) . All Western blots were performed at least twice using extracts from two independent experiments . Air-dried P . falciparum sporozoites , permeabilized with 0 . 1% triton X100 and blocked with 3% BSA in PBS , were incubated with mouse anti-SIAP and anti LSAP sera ( dilution 1/200 ) , followed by incubation with anti-mouse Alexa Fluor 494 ( Invitrogen , Paisley , United Kingdom ) , and 1 µg/ml DNA stain diamidino-phenylindole ( DAPI ) . Non-immune mouse serum was used as control . Dual staining with anti-CSP was performed using rabbit anti-CSP serum ( 1/7000 ) ( a gift from Dr Laurent Rénia , Cochin Institute , Paris , France ) using anti-rabbit IgG Alexa Fluor 594 ( Invitrogen , Paisley , United Kingdom ) as a secondary antibody . Liver stage staining was performed on hepatocyte cultures fixed with 4% paraformaldehyde and then permeabilized with methanol . Slides were then examined by confocal fluorescence microscopy . In order to quantify the inhibitory potential of antisera on sporozoite cell-traversal activity , sporozoites ( 3 . 0×105 ) incubated in the presence or the absence of antisera ( dilution 1/100 ) were added to HeLa cells seeded on a 96-well plate , and the cell-traversal assay carried out as described above . The extent of inhibition was expressed as the percent ratio of the percentage of wounded cells observed in the presence of antibody over that observed in the absence of antibody . To determine the effects of antisera on sporozoite invasion ability , triplicate hepatocyte cultures were inoculated with P . falciparum sporozoites ( 1 . 0×105/Lab-Tek well ) incubated with anti-SIAP or anti-LASP ( dilution 1/20 or 1/100 ) , anti-CSP ( dilution 1/100 ) , or non-immune mice sera . Liver stage parasite were cultivated and stained as described above . The extent of inhibition was expressed as a percentage calculated as the number of schizonts in wells exposed to antibody over that in control wells . Orthologues of P . falciparum SIAP and LSAP genes were sought in the genomes of Plasmodium species that infect rodents and primates: P . yoelii , P . berghei , and P . chabaudi , P vivax , and P knowlesi ( BLAST program available at www . PlasmoDB . org ) , and P . reichenowi ( TBLASTN program available at http://www . sanger . ac . uk/cgi-bin/blast/submitblast/p_reichenowi ) . Orthologous sequences from two genomes were defined as reciprocal best hits with BLAST E-values less than 1e-15 . Failure to detect orthologues for SIAP and LSAP proteins with program based on protein sequence homology , was confirmed by additional analyses based on gene synteny maps [55] . Data obtained after functional assays were analyzed for statistical significance using the one-way analysis of variance followed by the Tukey multiple comparison test . PFD0425w : SIAP-1 PF08_0005 : SIAP-2 PFL0065w : LSAP-1 PFB0105c : LSAP-2 PF13_0201 : TRAP PF14_0425 : fructose-bisphosphate aldolase PF11_0344 : AMA-1 PFB0570w : SPATR MAL13P1 . 212 : SPECT-1 PFD0430c : SPECT-2 PF08_0054 : heat shock 70 kDa protein PFC0210c : Circumsporozoite ( CS ) protein PFL0800c : CelTOS PF10_0164 : UIS-4 PF13_0012 : UIS-3 PF11_0224 : EXP-1 PF14_0678 : EXP-2 MAL8P1 . 6 : Etramp 8 MAL6P1 . 135 : Phospholipase PF08_0099 :P36p | Sporozoites , the infective form of the malaria parasites Plasmodium , are deposited in the skin by Anopheles mosquitoes . They then find their way to the liver where they specifically invade hepatocytes , in which they develop to yield another form , the merozoite , infective to red blood cells . Relatively little is known of the molecular interactions during these initial obligatory phases of the infection . We studied the changes in gene expression in sporozoites , from the parasite species P . falciparum that infects humans , in an in vitro system where they were co-cultured with their natural host cells , primary human hepatocytes . The whole genome transcriptome profiling carried out led to the identification of 532 genes that were up-regulated following co-culture . This genome-wide up-regulation of expression supports the hypothesis that the shift from the mosquito to the mammalian host contributes to activate quiescent salivary gland sporozoites into a state of readiness for the hepatic stages . Functional studies on four of the up-regulated genes we identified validated our approach as one means to determine the repertoire of proteins implicated during the early events in the infection by P . falciparum , the species responsible for the severest forms of malaria . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/tropical",
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"diseases",
"microbiology/parasitology",
"genetics",
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] | 2008 | Temperature Shift and Host Cell Contact Up-Regulate Sporozoite Expression of Plasmodium falciparum Genes Involved in Hepatocyte Infection |
In angiosperms , the egg cell forms within the multicellular , haploid female gametophyte . Female gametophyte and egg cell development occurs through a unique process in which a haploid spore initially undergoes several rounds of synchronous nuclear divisions without cytokinesis , resulting in a single cell containing multiple nuclei . The developing gametophyte then forms cell walls ( cellularization ) and the resulting cells differentiate to generate the egg cell and several accessory cells . The switch between free nuclear divisions and cellularization-differentiation occurs during developmental stage FG5 in Arabidopsis , and we refer to it as the FG5 transition . The molecular regulators that initiate the FG5 transition during female gametophyte development are unknown . In this study , we show using mutant analysis that two closely related MYB transcription factors , MYB64 and MYB119 , act redundantly to promote this transition . MYB64 and MYB119 are expressed during the FG5 transition , and most myb64 myb119 double mutant gametophytes fail to initiate the FG5 transition , resulting in uncellularized gametophytes with supernumerary nuclei . Analysis of cell-specific markers in myb64 myb119 gametophytes that do cellularize suggests that gametophytic polarity and differentiation are also affected . We also show using multiple-mutant analysis that MYB119 expression is regulated by the histidine kinase CKI1 , the primary activator of two-component signaling ( TCS ) during female gametophyte development . Our data establish a molecular pathway regulating the FG5 transition and implicates CKI1-dependent TCS in the promotion of cellularization , differentiation , and gamete specification during female gametogenesis .
The alternation between haploid gametophyte and diploid sporophyte generations is a fundamental aspect of the plant life cycle . In all species , gametophytes are essential for gamete formation . Angiosperms have two gametophytes: a female gametophyte , which is also referred to as the embryo sac , and a male gametophyte , which is also called the pollen grain . In angiosperms , the egg and sperm cells form within the female and male gametophytes , respectively . The angiosperm female gametophyte most commonly consists of one egg cell , one central cell , two synergid cells , and three antipodal cells , and the male gametophyte contains two sperm cells encased within a vegetative cell . The female and male gametophytes develop within the flower's sexual organs and are spatially separated . During sexual reproduction , the male gametophyte forms a pollen tube that grows through the floral tissues to deliver its two sperm cells to the female gametophyte . Following sperm cell delivery , the egg cell and central cell both become fertilized and subsequently give rise to the embryo and endosperm of the seed , respectively . The synergid cells are required to attract the pollen tube . However , the function of the three antipodal cells is currently unknown [1] , [2] . Female gamete specification occurs during female gametophyte development , also referred to as female gametogenesis . During female gametogenesis ( Figure 1A ) , the developing embryo sac initially goes through a coenocytic phase , during which a haploid megaspore undergoes two rounds of mitosis without cytokinesis ( stages FG1–FG4 ) . These nuclear divisions are accompanied by rapid cell growth resulting in an enlarged four-nucleate coenocyte . Gametogenesis then undergoes a major developmental transition: the coenocytic developmental pattern ceases , and during a third round of mitosis , division is accompanied by phragmoplast and cell plate formation , resulting in the nuclei becoming surrounded by cell walls ( cellularization ) . In addition to cellularization , mitosis ceases , cell growth attenuates , and the resulting cells differentiate [1] , [3] , [4] . All of these post-coenocytic events occur during stage FG5 , we therefore refer to this transition as the FG5 transition ( Figure 1A ) . The molecular pathways that regulate the FG5 transition are not understood . This transition involves the regulation of multiple processes including cell wall formation , cell-cycle regulation , cell growth , and cellular differentiation . Regulatory genes that control all of these processes have not been identified . However , a few mutants affected in a subset of these processes have been characterized , including retinoblastoma related ( rbr ) and cytokinin independent 1 ( cki1 ) . RBR encodes a homolog of the tumor suppressor gene pRb and performs an evolutionarily conserved role in Arabidopsis to suppress entry into S-phase of the cell cycle . Mutations affecting RBR result in additional nuclear divisions during female gametogenesis . The extra divisions most often occur post-cellularization , resulting in cells with supernumerary nuclei , but occasionally occur prior to cellularization , resulting in the production of extra egg or synergid cells [5]–[9] . Additionally , rbr gametophytes fail to express some cell specific markers , suggesting that they are defective in cell differentiation [9] . CKI1 encodes an Arabidopsis histidine kinase ( AHK ) related to the three cytokinin receptors AHK2 , AHK3 , and AHK4 [10] . In contrast to AHK2–AHK4 , the extracellular domain of CKI1 does not bind cytokinins [11] . However , ectopic expression of CKI1 induces constitutive cytokinin-like responses in the absence of cytokinin , and this activity involves the downstream components of the cytokinin two-component signaling ( TCS ) pathway [10] , [12]–[15] . cki1 mutants are defective in female gametogenesis starting at stage FG5 , have cellularization defects , and occasionally contain supernumerary nuclei [16]–[18] . Mutations affecting TCS components also affect the female gametophyte and exhibit phenotypes similar to cki1 [18] , [19] . By contrast , analysis of ahk2 ahk3 ahk4 triple mutants indicates that AHK2–AHK4 are not necessary for female gametophyte development [18]–[23] . These observations suggest that CKI1 activates the TCS pathway independent of cytokinin within the female gametophyte . However , it is unclear what developmental processes the CKI1-dependent TCS pathway regulates during female gametogenesis . Here , we show that MYB64 and MYB119 act redundantly to promote all aspects of the FG5 transition during female gametogenesis in Arabidopsis . MYB64 and MYB119 are predicted to encode two closely related R2R3-MYB transcription factors that are expressed during the FG5 transition . We also show that MYB119 is regulated by CKI1 , providing new insights into the molecular functions of CKI1 within the female gametophyte .
We previously identified MYB64 ( At5g11050 ) and MYB119 ( At5g58850 ) in a differential expression screen for transcription factor genes expressed in the female gametophyte [24] . In that study , expression in the mature female gametophyte was verified for MYB64 , but not MYB119 , using a transcriptional reporter . To confirm the expression patterns of MYB64 and MYB119 and to characterize the expression of these genes throughout female gametophyte development , we analyzed transgenic plants containing translational GFP fusion constructs ( ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP ) . ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP individually were capable of complementing the seed phenotype of myb64 myb119 double mutants discussed below ( Table S1 ) . Both fusion proteins were localized to nuclei , which is consistent with their predicted role in transcriptional regulation ( Figure 1B–1D and 1F–1H ) . ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP expression was transient during female gametogenesis . Both fusion proteins were first detected in the coenocytic female gametophyte at stage FG4 ( four-nucleate stage ) . At this stage , MYB64-GFP and MYB119-GFP were detected in all four nuclei of the female gametophyte ( Figure 1B and 1F ) . Post-cellularization , both fusion proteins were detected in the central cell ( unfused polar nuclei at stage FG5 and secondary nucleus at stage FG6 ) ( Figure 1C , 1D , 1G and 1H ) . MYB64-GFP was additionally detected in the egg cell nucleus during stages FG5 and FG6 ( Figure 1C and 1D ) . The levels of both fusion proteins were dramatically reduced in mature female gametophytes ( stage FG7 ) : MYB119-GFP was not detectable , while MYB64-GFP expression was very weak and detectable in only a minority ( 26% ) of gametophytes . We also generated and analyzed transcriptional fusions for both MYB64 and MYB119 ( ProMYB64:H2B-GFP and ProMYB119:H2B-GFP ) . The transcriptional fusions had expanded , but overlapping expression patterns relative to their respective translational fusions: ProMYB64:H2B-GFP expression was detected in all cells of the female gametophyte and ProMYB119:H2B-GFP expression was detected in the antipodal cells in addition to the central cell ( Figure 1E and 1I ) . In addition to the female gametophyte , ProMYB64:H2B-GFP and ProMYB119:H2B-GFP expression was also detected in the septum of the ovary ( Figure S1A and S1B ) . To determine whether MYB64 and MYB119 are expressed elsewhere in the plant , we used quantitative real-time PCR ( qRT-PCR ) with cDNA from a variety of plant tissues ( Figure 2 ) . Consistent with the female gametophyte expression of ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP , strong expression was detected for both genes in the ovary , which contains the female gametophyte . Little to no expression was detected in siliques at 36 hours after pollination , and expression was not detected for either gene in 10-day-old seedlings consisting of roots , stems and leaves . By contrast , strong expression of MYB119 was detected in stamens . To localize expression within stamens , we analyzed GFP expression of ProMYB64:H2B-GFP and ProMYB119:H2B-GFP in male reproductive tissue . We did not detect any GFP expression in the male gametophyte for either construct; however , strong GFP expression was detected in the filament for ProMYB119:H2B-GFP ( Figure S1C ) . In summary , MYB64 and MYB119 encode nuclear-localized proteins and are expressed within the female gametophyte . Furthermore , these genes are expressed during a specific period of female gametogenesis , from just before the FG5 transition through stage FG6 . To determine whether MYB64 and MYB119 are required for female gametophyte development , we obtained T-DNA insertion mutants for both genes from the Arabidopsis Biological Resource Center ( ABRC ) [25]–[27] . We analyzed two alleles of MYB64 ( myb64-1 and myb64-4 ) and three alleles of MYB119 ( myb119-1 , myb119-3 , and myb119-5 ) ( Figure S2 ) ( see Table S2 for additional alleles not discussed in this paper ) . With all five single mutants , defects in vegetative or reproductive tissues were not apparent and siliques contained full seed set . Confocal scanning laser microscopy ( CSLM ) analysis of myb64-1 and myb119-3 ovules indicated that female gametophyte development was unaffected in these mutants ( Figure S3 ) . The absence of mutant phenotypes in myb64 and myb119 single mutants , together with the overlapping expression patterns and high sequence similarity of these two genes [28] , suggested that MYB64 and MYB119 may be functionally redundant in the female gametophyte . To test this , we analyzed transmission of multiple myb64 myb119 mutant allele combinations ( Table 1 and Table 2 ) . In all mutant allele combinations tested , self-fertilized myb64/MYB64 myb119/myb119 plants segregated ∼1∶1 for myb64/MYB64 and MYB64/MYB64 progeny ( Table 1 ) . Similarly , self-fertilized myb64/myb64 myb119/MYB119 plants segregated ∼1∶1 for myb119/MYB119 and MYB119/MYB119 progeny ( Table 2 ) . These results suggest that gametophytic transmission of the myb64 myb119 double mutation is affected . To determine whether transmission of the myb64 myb119 double mutation is affected through the female gametophyte and/or male gametophyte , we performed reciprocal crosses of myb64/myb64 myb119/MYB119 and myb64/MYB64 myb119/myb119 with wild type . For all allele combinations tested , transmission of myb64 myb119 double mutations was not significantly affected through the male gametophyte ( Table 1 and Table 2 ) . To confirm that male gametophyte development was unaffected , we stained mature pollen grains from myb64-1/MYB64 myb119-3/myb119-3 plants with DAPI and found that they were phenotypically wild type ( N = 113 ) ( Figure S4 ) . In contrast to male gametophyte transmission , transmission of myb64 myb119 double mutations through the female gametophyte was severely reduced ( Table 1 and Table 2 ) . We did not detect any transmission of myb64-1 myb119-3 and myb64-1 myb119-5 double mutations through the female gametophyte . However , the myb64-4 myb119-1 double mutation was transmittable through the female gametophyte at very low frequency ( <2% ) ( Table 1 ) . This partial penetrance allowed us to isolate lines doubly homozygous for myb64-4 and myb119-1 . myb64-4 myb119-1 double-homozygous plants had no obvious vegetative phenotypes . To determine whether myb64 myb119 mutations affect female gametophyte development , we analyzed ovules from wild-type and myb64-1/MYB64 myb119-3/myb119-3 plants using CSLM ( Figure 3 ) . During coenocytic development ( stages FG1–FG4 ) , myb64-1 myb119-3 female gametophytes were indistinguishable from wild type . Abnormal myb64-1 myb119-3 female gametophytes were first apparent beginning at stage FG5 , during which wild-type female gametophytes cellularize and differentiate ( Figure 3A and 3B ) . At this time point , myb64-1 myb119-3 gametophytes had eight nuclei but were not cellularized and were over-expanded , causing the embryo sac to protrude from the micropyle of the ovule ( Figure 3E and S5A–S5E ) . As development progressed , myb64-1 myb119-3 gametophytes continued to expand and underwent additional nuclear divisions , resulting in enlarged , single-celled gametophytes containing supernumerary nuclei ( Figure 3F and 3I–3N ) . The number of nuclei in these coenocytic gametophytes was variable , ranging from 10 to 18 with an average of 13 . 5 ( +/−2 . 2 ) ( N = 24 ) . At maturity , 46% of the myb64-1 myb119-3 gametophytes were collapsed and degenerated ( Figure 3H ) , 32% were enlarged multi-nucleate coenocytes , and 22% were cellularized ( N = 215 ) . Cellularized myb64 myb119 gametophytes contained extra cells and exhibited little or no morphological similarity to wild-type gametophytes ( compare Figures 3C and 3D to Figure 3G and Figure S5F–S5J ) . We also analyzed mature ovules of myb64-4 myb119-1 double-homozygous plants . myb64-4 myb119-1 female gametophytes had a similar but slightly weaker phenotype relative to that of myb64-1 myb119-3 discussed above . At maturity , fewer myb64-4 myb119-1 gametophytes were collapsed and degenerated compared to those from myb64-1 myb119-3 plants ( 31% versus 46% , respectively ) . Additionally , myb64-4 myb119-1 gametophytes cellularized more frequently than myb64-1 myb119-3 gametophytes ( 52% versus 22% , respectively ) ( N = 114 ) . In summary , most myb64 myb119 gametophytes fail to cellularize , cease nuclear division , and attenuate cell growth , resulting in enlarged coenocytes with supernumerary nuclei . These data suggest that MYB64 and MYB119 are required for the FG5 transition during female gametogenesis . To determine if cellular differentiation is also affected in myb64 myb119 gametophytes , we analyzed the expression of several cell-specific GFP markers in myb64 myb119 gametophytes . The markers analyzed were ProDD31:GFP , which is expressed in the synergid cells ( Figure 4A ) ; ProDD45:GFP , which is expressed in the egg cell ( Figure 4B ) ; ProDD65:GFP , which is expressed in the central cell ( Figure 4C ) ; and ProDD1:GFP , which is expressed in the antipodal cells ( Figure 4D ) [29] . Using crosses , we generated myb64-1/myb64-1 myb119-3/MYB119 plants homozygous for each respective GFP marker and scored GFP expression in mature female gametophytes from these plants . The expression of all markers tested was affected in myb64 myb119 female gametophytes . The synergid cell marker ( ProDD31:GFP ) was the most severely affected and was not detected in myb64 myb119 gametophytes ( Figure 4E and 4I ) . Expression of the egg and central cell markers was also strongly affected . ProDD45:GFP and ProDD65:GFP were expressed in 7% and 15% of myb64 myb119 gametophytes , respectively ( Figure 4J and 4K ) . When expressed , ProDD45:GFP was detected in a single , egg-like cell at the micropylar end ( Figure 4F ) , whereas ProDD65:GFP was abnormally expressed throughout the female gametophyte ( Figure 4G ) . By contrast , the antipodal cell marker was expressed at a much higher frequency in myb64 myb119 female gametophytes ( 57% ) ( Figure 4L ) , although it was also abnormally expressed throughout the female gametophyte ( Figure 4H ) . In summary , myb64 myb119 female gametophytes either do not express cell-specific markers or express them in an atypical pattern , suggesting that MYB64 and MYB119 are required for proper cell differentiation . The expression of micropylar cell markers was either absent or severely reduced , and chalazal cell markers had expanded expression domains; this suggests that gametophytic polarity is also affected in myb64 myb119 gametophytes , with an expansion of chalazal cell identity at the expense of micropylar cell identity . With all allele combinations tested , siliques from myb64/MYB64 myb119/myb119 or myb64/myb64 myb119/MYB119 plants contained ∼50% normal seeds and ∼50% defective seeds ( Table 3 ) . Correspondingly , siliques from myb64-4 myb119-1 double-homozygous plants contained mostly ( ∼97% ) defective seeds ( Table 3 ) . In all cases , the defective seeds consisted of mostly desiccated ovules and a smaller proportion of white or collapsed seed-like structures ( Figure S6 and Table 3 ) . To determine whether myb64 myb119 double mutations affect seed development maternally , we pollinated myb64-4 myb119-1 double-homozygous plants with wild-type pollen and analyzed cleared seeds at 3 days after pollination ( DAP ) ( N = 106 ) . Siliques resulting from this cross contained seed-like structures ( Figure 5A–5E ) , indicating that the seed-development defect results from absence of maternal expression of MYB64 and MYB119 . The resulting seed-like structures fell into three categories: most ( 96% ) lacked an embryo , but did contain tissue resembling proliferating endosperm nuclei ( Figure 5B ) ; ∼2% contained both proliferating endosperm and embryos that resembled wild-type embryos at this time point ( Figure 5C ) ; and ∼2% contained proliferating endosperm and an embryo-like structure that did not resemble any stage of wild-type embryo development and typically consisted of only a few cells ( Figure 5D and 5E ) . The majority of seed-like structures in myb64 myb119 siliques were similar to autonomous seeds in mutants affected in the Fertilization Independent Seed ( FIS ) Polycomb Repressive Complex 2 ( PRC2 ) [30]–[36] . To determine whether myb64 myb119 gametophytes also initiate autonomous seed development , we emasculated flowers from myb64-4 myb119-1 double-homozygous plants and examined the contents of the pistils at 3 days after emasculation ( DAE ) . Wild-type pistils at 3 DAE contained only ovules with mature female gametophytes ( Figure 5F ) . By contrast , myb64-4 myb119-1 pistils contained a mixture of ovules with collapsed female gametophytes ( 81% ) and seed-like structures ( 19% ) ( Figure 5G and 5H ) ( N = 437 ) . These seed-like structures did not contain embryos or embryo-like structures , but did contain proliferating nuclei that resembled endosperm . Additional analysis of autonomous myb64 myb119 seed-like structures suggests that the proliferating nuclei within them have endosperm identity , as indicated by the expression of the endosperm-specific marker ProAGL62:AGL62-GFP ( Figure S7A–S7F ) [37] . They also initiate seed coat development , as indicated by vanillin staining ( Figure S7G–S7I ) [38] , [39] . Together , these data suggest very strongly that myb64 myb119 gametophytes produce autonomous seeds . The autonomous seed-like structures could result from absence of FIS PRC2 activity . To test this , we analyzed expression of the FIS PRC2 subunit FIS2 in myb64 myb119 gametophytes . We generated myb64-1/myb64-1 myb119-3/MYB119 plants homozygous for a FIS2 transcriptional GFP fusion ( ProFIS2:GFP ) by crossing and analyzed GFP expression at maturity ( Figure 6A–6C ) . GFP was observed in only 5% of myb64 myb119 gametophytes , indicating severely reduced expression ( Figure 6C ) . When expressed in myb64 myb119 gametophytes , ProFIS2:GFP expression was typically observed throughout the female gametophyte ( Figure 6B ) , whereas in wild-type gametophytes its expression was limited to the central cell ( Figure 6A ) . We confirmed the downregulation of FIS2 using qRT-PCR , which showed that FIS2 expression was strongly reduced in cDNA from myb64-4/myb64-4 myb119-1/myb119-1 ovaries relative to wild-type ovaries ( Figure 6D ) . The frequency of myb64 myb119 gametophytes expressing ProFIS2:GFP was reduced as compared to the other central cell marker ProDD65:GFP ( 5% versus 15% , respectively; two sample t-test: p-value = 0 . 03 ) , and the difference between their frequencies roughly correlates with the number of seed-like structures observed in this genotype ( Table 3 ) , suggesting that the autonomous seeds may arise from myb64 myb119 gametophytes with central cell identity but without a functional FIS PRC2 . These data suggest that MYB64 and MYB119 are required to activate FIS2 expression during the FG5 transition . As with myb64 myb119 , the cki1 mutation affects stage FG5 of female gametophyte development and occasionally produces female gametophytes containing supernumerary nuclei , suggesting that CKI1 may also be required for the FG5 transition [16]–[18] . qRT-PCR analysis of double-homozygous myb64-4 myb119-1 ovaries indicated that CKI1 expression was not affected in myb64 myb119 gametophytes ( Figure S8 ) . We therefore investigated whether MYB64 and MYB119 are regulated through the CKI1 pathway . To determine if MYB64 and MYB119 expression is regulated by CKI1 , we analyzed the expression of GFP fusion constructs in cki1 mutants . Due to the transient expression of the translational fusions , we initially used transcriptional GFP reporters for both genes , which exhibited sustained expression at maturity ( ProMYB64:H2B-GFP and ProMYB119:H2B-GFP ) . For this analysis we used cki1-9 , which is a new cki1 allele in the Col-0 accession that we obtained from the ABRC [27] . The T-DNA in cki1-9 is inserted within the third exon of CKI1 ( Table S2 ) , and CSLM analysis of cki1-9 ovules confirmed that this allele produces an identical female gametophyte-lethal phenotype to previously reported alleles in other Arabidopsis accessions ( Figure S8 and Table S1 ) [16]–[18] . Using crosses , we generated plants heterozygous for cki1-9 and homozygous for each transcriptional GFP fusion construct , and analyzed GFP expression within the female gametophyte ( Figure 7 ) . In these plants , ProMYB64:H2B-GFP was expressed in 98% of the female gametophytes ( Figure 7C and 7G ) . By contrast , ProMYB119:H2B-GFP was expressed in only 50% of the female gametophytes ( Figure 7D and 7G ) . We obtained similar results when using the ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP translational fusions ( Figure S9A–S9C ) , and confirmed that MYB119 was downregulated in cki1 mutants using qRT-PCR with cDNA from ovaries of the homozygous cki1-8 allele ( Figure S9D ) . These data suggest that CKI1 is required for MYB119 expression . A second regulatory gene that could potentially be required for the FG5 transition is RBR . Similar to myb64 myb119 mutants , rbr mutant female gametophytes contain supernumerary nuclei or cells and also exhibit defects in differentiation [5] , [6] , [9] . RBR is expressed before MYB64 and MYB119 during early female gametogenesis [6] , suggesting that RBR may be required for MYB64 and MYB119 expression . To test this , we crossed ProMYB64:H2B-GFP and ProMYB119:H2B-GFP into rbr1-2 plants , and analyzed plants heterozygous for rbr1-2 and homozygous for each transcriptional GFP fusion . GFP expression for both constructs was unaffected in the rbr1-2 mutant ( Figure 7E–7G ) , suggesting that RBR does not regulate MYB64 or MYB119 expression in the female gametophyte . The above results suggest that MYB119 is regulated through the CKI1 pathway . If this is true , cki1 myb64 double-mutants should exhibit a phenotype similar to that of myb64 myb119 double-mutants . To test this , we generated double- and triple-mutant female gametophytes and analyzed their phenotypes using CSLM . We analyzed double-mutant female gametophytes in cki1-9/CKI1 myb119-3/myb119-3 and cki1-9/CKI1 myb64-1/myb64-1 plants . In both genotypes , ∼50% of the female gametophytes were defective ( Figure 8 ) . cki1 myb119 gametophytes were indistinguishable from cki1 gametophytes ( Figure 8B and 8C ) ( N = 229 ) . By contrast , most ( 69% ) cki1 myb64 gametophytes did not resemble cki1 gametophytes but instead resembled myb64 myb119 gametophytes: 47% of cki1 myb64 gametophytes were enlarged , protruded from the micropyle of the ovule , and contained supernumerary nuclei ( Figure 8D–8J ) ; and 22% were collapsed or cellularized in a manner similar to myb64 myb119 gametophytes . The remaining 31% of cki1 myb64 gametophytes resembled cki1 gametophytes ( N = 311 ) . These results are consistent with our expression data showing that MYB119 is downregulated in cki1 female gametophytes . We also analyzed triple-mutant female gametophytes in cki1-9/CKI1 myb64-1/MYB64 myb119-3/myb119-3 plants . Pistils from these plants contain female gametophytes with four different genotypes: 25% CKI1 MYB64 myb119 , 25% CKI1 myb64 myb119 , 25% cki1 MYB64 myb119 , and 25% cki1 myb64 myb119 . As expected , pistils from triple mutant plants contained ∼75% defective gametophytes . Of the total gametophytes examined , 24% resembled cki1 gametophytes while 53% resembled gametophytes from myb64 myb119 plants ( N = 139 ) . Therefore , cki1 myb64 myb119 gametophytes resemble myb64 myb119 gametophytes , whereas cki1 MYB64 myb119 gametophytes resemble cki1 gametophytes . These results demonstrate that MYB64 has activity in cki1 myb119 gametophytes , which is consistent with our expression data showing that MYB64 is expressed in cki1 gametophytes . If MYB119 expression is regulated through the CKI1 pathway , these two genes should be co-expressed . To test this , we analyzed transgenic lines containing a translational fusion construct ( ProCKI1:CKI1-GFP ) . ProCKI1:CKI1-GFP was capable of complementing the cki1-9/CKI1 and cki1-9/CKI1 myb64-1/myb64-1 silique phenotypes ( Table S1 ) . This analysis also allowed us to determine the subcellular localization of CKI1 within the female gametophyte; although CKI1 has been shown to localize to the plasma membrane when ectopically expressed in Arabidopsis protoplasts [10] , localization within the developing gametophyte has not been determined . ProCKI1:CKI1-GFP expression was detectable during all stages of female gametophyte development ( stages FG1–FG7 ) . Before cellularization ( stages FG1–FG4 ) , expression was detected throughout the gametophyte ( Figure 9A–9C ) . During these stages , CKI1-GFP was primarily localized to the plasma membrane; however during stages FG1–FG3 weak cytoplasmic localization was also detected ( Figure 9A and 9B ) . Post-cellularization ( stages FG5–FG7 ) , ProCKI1:CKI1-GFP expression was restricted to the three antipodal cells and the central cell , and CKI1-GFP was primarily localized to the plasma membrane ( Figure 9D–9F ) . The post-cellularization expression of CKI1 is consistent with the reported phenotype of cki1 gametophytes , which primarily exhibit defects in the chalazal region of the female gametophyte including improper positioning of the antipodal cell nuclei , unfused polar nuclei , and degeneration of the central cell [17] ( Figure 7C and Figure 8B ) . These data show that MYB119 and CKI1 are co-expressed during stages FG4–FG6 of female gametophyte development . Additionally , CKI1 expression within the gametophyte becomes polarized during the FG5 transition , indicating that CKI1-dependent TCS activity is restricted to the chalazal pole .
During wild-type female gametogenesis , the embryo sac initially develops coenocytically , during stages FG1–FG4 . Then , during the FG5 transition , the coenocytic pattern ceases and the developing embryo sac cellularizes . Concomitantly , nuclear division ceases , cell expansion attenuates , and the resulting cells differentiate . myb64 myb119 female gametophytes are defective in all aspects of the FG5 transition . Most myb64 myb119 gametophytes continue the coenocytic developmental pattern at stage FG5 and fail to cellularize , cease nuclear division , and attenuate cell growth , resulting in enlarged coenocytes with supernumerary nuclei ( Figure 3 ) . Furthermore , in cases where myb64 myb119 gametophytes do cellularize , they contain extra cells and the resulting cells are defective in cellular differentiation , as indicated by reduced expression of cell-type specific markers ( Figure 4 ) . As putative transcription factors , it is likely that MYB64 and MYB119 function to regulate a large number of genes required for the multiple processes that occur during the FG5 transition of female gametogenesis , including cell growth , cellularization , differentiation , and cell cycle regulation . The regulation and timing of MYB64 and MYB119 expression is therefore a critical step in formation of female gametes . We have shown that MYB119 expression is downregulated in cki1 female gametophytes . This conclusion is supported by both expression ( Figure 7 and Figure S9 ) and genetic ( Figure 8 ) data . CKI1 is the primary activator of TCS within the female gametophyte , as none of the known cytokinin receptors ( AHK2–AHK4 ) are necessary for female gametogenesis [18]–[23] . Although CKI1 is required for female gametophyte development [16]–[18] , the specific developmental processes it regulates are largely unknown . We have shown that at least one of these processes is to promote the FG5 transition through the regulation of MYB119; however , whether MYB119 is a direct target of the CKI1-TCS pathway has yet to be determined . Although MYB64 acts redundantly with MYB119 , expression of MYB64 is not affected in cki1 gametophytes , suggesting that it is independently regulated . This conclusion is supported by both expression ( Figure 7 and Figure S9 ) and genetic ( Figure 8 ) data . Independent regulation of MYB64 and MYB119 can also be observed in their slightly different expression patterns ( Figure 1 ) . These data suggest that two parallel , yet redundant pathways exist to promote the FG5 transition in Arabidopsis . One pathway involves MYB119 , which is regulated by CKI1 , and a second pathway involves MYB64 , which is regulated by an as yet unknown regulator ( Figure 10 ) . Although cki1 mutants arrest development during stage FG5 , our data indicate that the cki1 single mutant phenotype results from functions of CKI1 that are independent of MYB119 downregulation . First , MYB119 and MYB64 are functionally redundant proteins but only MYB119 is downregulated in cki1 mutants ( Figure 7 and Figure S9 ) . Second , myb119 gametophytes are phenotypically wild type , indicating the cki1 phenotype is not due to downregulation of MYB119 ( Figure S3 ) . Third , our triple mutant analysis demonstrates that MYB64 has activity in cki1 myb119 gametophytes . Together these data suggest that MYB64 expression is sufficient to initiate the FG5 transition in the absence of CKI1 or MYB119 . Consistent with our data , cki1 mutants typically contain synergid and egg cell structures ( Figure 8B ) [17] . The female gametophyte is a highly polarized structure consisting of the egg and synergid cells at the micropylar pole , and the antipodal cells at the chalazal pole . Gametophytic polarity within myb64 myb119 gametophytes is defective; specifically , myb64 myb119 gametophytes exhibit an expansion of chalazal cell identity and a loss of micropylar cell identity ( Figure 4 ) . Establishment of polarity within the female gametophyte is poorly understood . However , several lines of evidence suggest that nuclear positioning within the coenocytic gametophyte is a primary determinate of cell fate [5] , [6] , [40]–[43] and that positional information is conveyed through an asymmetric gradient of the plant hormone auxin emanating from the micropylar pole [44] . Initiation of the micropylar auxin gradient is reported to occur very early ( stages FG1–FG3 ) whereas MYB64 and MYB119 expression is not observed until stage FG4; therefore , it is unlikely that these genes are required for establishment of the auxin gradient . However , MYB64 and MYB119 could be required to interpret this positional information prior to cellularization . Alternatively , the micropylar auxin gradient may be disrupted in myb64 myb119 gametophytes due to their prolonged coenocytic development . CKI1 activates the cytokinin TCS pathway independent of cytokinin [10] , [12]–[15] , [45]; therefore , CKI1 expression likely represents areas of TCS activity . During the FG5 transition , CKI1 expression becomes restricted to the chalazal-most cells of the female gametophyte ( antipodal cells and central cell ) ( Figure 9 ) , suggesting the existence of polarized CKI1-dependent TCS activity within the female gametophyte . Consistent with the observed CKI1-GFP expression pattern , mutations in CKI1 primarily affect the central cell and antipodal cells [17] ( Figure 7B and Figure 8B ) . Notably CKI1 is expressed at the opposite pole from which an auxin source within the female gametophyte is initiated [44] . An antagonizing role between auxin and cytokinin-dependent TCS has been documented during a number of key developmental steps in Arabidopsis [46] , suggesting that interactions between chalazal CKI1-dependent TCS and a micropylar auxin source may play a role in regulating the FG5 transition . In most plant species , seed development initiates only following fertilization . In the absence of fertilization , the FIS PRC2 represses initiation of endosperm development within the central cell , and gametophytes without a functional FIS PRC2 initiate endosperm development in the absence of fertilization [30]–[36] . myb64 myb119 gametophytes also give rise to seed-like structures in the absence of fertilization ( Figure 5 , Figure S6 and Figure S7 ) , suggesting that MYB64 and MYB119 are required to activate FIS PRC2 activity within the central cell during the FG5 transition . Consistent with this , expression of the FIS PRC2 subunit FIS2 is reduced in myb64 myb119 gametophytes ( Figure 6 ) , indicating that a functional FIS PRC2 is not present . MYB64 and MYB119 are expressed transiently during female gametogenesis ( Figure 1 ) ; therefore , it is unlikely that they directly regulate FIS2 expression . For example , regulation of FIS2 by MYB64 and MYB119 may act through DEMETER and/or DNA METHYLTRANSFERASE 1 , which are required for FIS2 activation or repression , respectively [7] , [47]–[49] . Further experiments will be required to place MYB64 and MYB119 within this pathway . In summary , the results presented here indicate that MYB64 and MYB119 act redundantly as regulators of the FG5 transition and are independently regulated . MYB119 is regulated by CKI1 whereas the regulator of MYB64 has yet to be determined . During the FG5 transition , MYB64 and MYB119 regulate multiple developmental processes including cell growth , nuclear divisions , cellularization , differentiation and activation of the PRC2 subunit FIS2 ( Figure 10 ) .
All Arabidopsis thaliana ( L . ) Heynh plants used were derived from the Columbia ( Col-0 or Col-3 ) and Wassilewskija ( Ws ) accessions . Seeds from myb64-1 ( Col-0 ) , myb64-2 ( Col-0 ) , myb64-3 ( Col-3 ) , myb64-4 ( Col-3 ) , myb119-1 ( Col-0 ) , myb119-2 ( Col-0 ) , myb119-3 ( Col-0 ) , myb119-4 ( Col-3 ) , myb119-5 ( Col-3 ) , cki1-9 ( Col-0 ) and Ws ( Stock # CS28823 ) plants were obtained from the Arabidopsis Biological Resource Center . Seeds from rbr1-2 ( Col-0 ) plants were kindly provided by Frédéric Berger . Seeds from ProFIS2:GFP ( Col-0 ) plants were kindly provided by Ramin Yadegari . Seeds from cki1-8 ( Ws ) plants were kindly provided by Jianru Zou . T-DNA borders for myb64-1 , myb64-4 , myb119-1 , myb119-3 , myb119-5 and cki1-9 were determined by amplifying the borders with either standard PCR or inverse PCR followed by sequencing . T-DNA borders and primer sequences used for amplification are listed in Table S2 and Table S3 , respectively . Genotypes were determined by standard PCR reactions using primers listed in Table S3 . Seeds were surface sterilized with chloride gas and sown on 0 . 5X MS salts , 0 . 05% MES , 1% sucrose and 0 . 8% Phytagar . For T1 selection , the appropriate selective agent was also added to the media . Seedlings were transferred to soil after 12 days of growth . All plants were grown at 20°C under 24-hour illumination . ProMYB64:MYB64-GFP , ProMYB119:MYB119-GFP and ProCKI1:CKI1-GFP were generated by amplifying ∼2 kb of upstream sequence and the full gene coding sequence minus the stop codon from Col-0 genomic DNA using the primers listed in Table S3 . The PCR fragments were then cloned into the pENTR/D-TOPO vector ( Invitrogen ) . pENTR/D-TOPO clones were then recombined into the destination vector pGWB450 [50] using LR Clonase II ( Invitrogen ) . Approximately 1 . 0 kb of 3′ sequence from MYB119 was amplified using the primers listed in Table S3 and cloned into ProMYB119:MYB119-GFP at a SacI site 3′ of the GFP coding region . ProMYB64:H2B-GFP and ProMYB119:H2B-GFP were generated by amplifying ∼2 . 0 kb of upstream sequence from Col-0 genomic DNA using primers listed in Table S3 . The PCR products were digested with appropriate restriction enzymes and ligated into the pBI-n1gfp vector [24] . Plants were transformed with Agrobacterium tumefaciens ( GV3101-pMP90 ) using a modified floral dip procedure [51] . For all constructs multiple independent T1 plants were analyzed , and are summarized in Table S4 . Pistils were emasculated at stage 12c prior to collection . For qRT-PCR analysis of MYB64 and MYB119 in wild type , pistils were collected 24 hours after emasculation . For qRT-PCR analysis of CKI1 , FIS2 , and MYB119 in myb64-4 myb119-1 double-homozygotes or cki1-8 homozygotes , pistils were collected 12–16 hours after emasculation . Siliques were collected 36 hours after pollination of emasculated flowers . Whole seedlings were germinated on GM and collected 10 days after germination . Stamens were collected from stage 14 flowers . All tissue was immediately frozen in liquid nitrogen . RNA was extracted using the RNeasy Mini Kit ( Qiagen ) . cDNA was transcribed from 1 µg of total RNA using the QuantiTect Reverse Transcription Kit ( Qiagen ) . qRT-PCR was performed using SYBR Green with the primers listed in Table S3 . Relative expression was calculated according to the ΔΔCT method , with the average of three biological replicates normalized to ACTIN2 reported , unless otherwise noted in the figure legend . For epifluorescence microscopy , tissue was dissected in water and analyzed using a Zeiss Axioplan compound microscope with DIC and epifluorescent optics . Mature pollen was stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) as previously described [52] . For confocal fluorescence microscopy , tissue was dissected in water and analyzed using a Zeiss LSM 510 microscope . Analysis of ProMYB64:H2B-GFP and ProMYB119:H2B-GFP in cki1 and rbr mutants was done by emasculating stage 12c flowers and examining the ovules 16 hours later . Analysis of ProMYB64:MYB64-GFP and ProMYB119:MYB119-GFP in cki1 mutants was done by emasculating stage 12c flowers and examining the ovules 12 hours later . For CSLM analysis of gametophyte development , pistils were sliced open along the replum using a needle and immersed in fixative containing 1×PBS and 4% glutaraldehyde . Tissue was fixed at room temperature for 2 . 5 hours under vacuum , followed by an ethanol dehydration series for 15 minutes each: 1×PBS 10% ethanol , 1×PBS 20% ethanol , 1×PBS 40% ethanol , 0 . 5×PBS 60% ethanol , 80% ethanol . Tissue was incubated in 95% ethanol overnight followed by two 30 min . incubations in 100% ethanol . Tissue was cleared in 2∶1 Benzyl Benzoate∶Benzyl Alcohol for 30 minutes . After rinsing pistils off in immersion oil , ovules were dissected directly into a drop of immersion oil and the coverslip was secured with nail polish . Ovules were then imaged using a Zeiss LSM 510 as previously described [53] . Seeds were cleared by incubating siliques , opened along the replum , in 9∶1 Ethanol∶Acetic acid for 2 hours , followed by two washes in 90% ethanol for 30 minutes each . Seeds were dissected out directly into a drop of chloral hydrate ( chloral hydrate∶water∶glycerol ( 8∶2∶1 ) ) . Cleared tissue was imaged using a Zeiss Axioplan compound microscope with DIC optics . For vanillin staining , pistils or siliques were sliced open along the replum using a needle and immersed in 1% ( w/v ) vanillin , 6N HCl for 30 minutes under vacuum . Carpels were then removed and ovules or seeds were imaged using an Olympus BX50 compound microscope with DIC optics . | Female gamete formation in angiosperms occurs through a unique process in which a haploid spore initially undergoes a series of free nuclear divisions without cytokinesis , resulting in a single cell containing multiple nuclei . The nuclei then differentiate and are partitioned with cell walls to generate the egg cell and several accessory cells . The molecular regulators that initiate the switch between free nuclear divisions and differentiation during female gametophyte development are unknown . In this study we show that two transcription factors , MYB64 and MYB119 , redundantly act to promote this process in the model organism Arabidopsis . We also show that one of them , MYB119 , is transcriptionally regulated by the histidine-kinase CKI1 . Our data establish the framework of a gene regulatory network required to promote cellularization , differentiation , and gamete specification during female gametogenesis . | [
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] | [] | 2013 | MYB64 and MYB119 Are Required for Cellularization and Differentiation during Female Gametogenesis in Arabidopsis thaliana |
Migratory lung dendritic cells ( DCs ) transport viral antigen from the lungs to the draining mediastinal lymph nodes ( MLNs ) during influenza virus infection to initiate the adaptive immune response . Two major migratory DC subsets , CD103+ DCs and CD11bhigh DCs participate in this function and it is not clear if these antigen presenting cell ( APC ) populations become directly infected and if so whether their activity is influenced by the infection . In these experiments we show that both subpopulations can become infected and migrate to the draining MLN but a difference in their response to type I interferon ( I-IFN ) signaling dictates the capacity of the virus to replicate . CD103+ DCs allow the virus to replicate to significantly higher levels than do the CD11bhigh DCs , and they release infectious virus in the MLNs and when cultured ex-vivo . Virus replication in CD11bhigh DCs is inhibited by I-IFNs , since ablation of the I-IFN receptor ( IFNAR ) signaling permits virus to replicate vigorously and productively in this subset . Interestingly , CD103+ DCs are less sensitive to I-IFNs upregulating interferon-induced genes to a lesser extent than CD11bhigh DCs . The attenuated IFNAR signaling by CD103+ DCs correlates with their described superior antigen presentation capacity for naïve CD8+ T cells when compared to CD11bhigh DCs . Indeed ablation of IFNAR signaling equalizes the competency of the antigen presenting function for the two subpopulations . Thus , antigen presentation by lung DCs is proportional to virus replication and this is tightly constrained by I-IFN . The “interferon-resistant” CD103+ DCs may have evolved to ensure the presentation of viral antigens to T cells in I-IFN rich environments . Conversely , this trait may be exploitable by viral pathogens as a mechanism for systemic dissemination .
Influenza virus replicates productively in the epithelial cells of the respiratory tract [1] , [2] . In close contact to the infected epithelial cells lies a network of specialized antigen presenting cells ( APCs ) known as dendritic cells ( DCs ) [3] , [4] . Two major subsets of lung DCs known as CD103+ DCs and CD11bhigh DCs can be identified in the steady-state [5] , [6] , [7] , [8] . Following influenza virus infection these cells migrate to the draining mediastinal lymph nodes ( MLNs ) loaded with viral antigens ( Ag ) [9] , [10] , [11] , [12] to initiate T cell responses that are critical for virus clearance and recovery from infection [13] , [14] , [15] . The strategic localization of lung DCs adjacent to the productively infected epithelial cells ensures a supply of viral antigen for presentation to T cells , but also makes DCs an ideal target for virus infection . Following aerosol infection of mice [9] , [16] , lung DCs begin to migrate 2 days post-infection ( dpi ) concomitant with the abrupt production of type I interferons ( I-IFNs ) and a myriad of other pro-inflammatory cytokines [10] , [17] . I-IFNs have potent antiviral activity limiting virus replication in infected cells by inducing the transcription of hundreds of interferon-stimulated genes ( ISGs ) [18] , [19] , [20] , [21] . The induction of ISGs or the antiviral state by I-IFNs , and other related cytokines such as interferon-lambda , also protect adjacent cells from infection thus restricting unabated spread of the virus in the respiratory tract [22] , [23] . I-IFNs have also been shown to function as natural adjuvants for maturing human [24] and mouse DCs in vitro [25] and splenic DCs [26] in vivo . The timing of I-IFN production and lung DC migration are temporally related and it is not known to what extent I-IFNs influence the function of these cells and their interaction with influenza virus . Here we investigated the role of I-IFN in lung DC function during influenza virus infection by studying the two major migratory lung DC subsets: CD103+ DCs and CD11bhigh DCs . We demonstrate a striking dichotomy in the sensitivity to I-IFN by lung DCs that defines the level of virus replication and Ag presentation to virus-specific naïve CD8+ T cells . Additionally , our findings show evidence that CD103+ DCs permit productive influenza replication and release infectious virus in the draining lymph nodes while CD11bhigh DCs do not .
Our previous work showed that influenza virus suppressed innate immunity in the lungs of mice for the first 2 days after infection; a period we termed “the stealth phase” [10] , [27] . During this privileged time , virus replication proceeds without instigating inflammation or triggering the migration of lung DCs to the MLNs . The end of the stealth phase is characterized by the abrupt and vigorous production of I-IFNs and cytokines and the initiation of Ag-bearing DC migration to the MLNs [10] , [28] . The cells arriving in the MLNs were identified by the presence of intracellular viral proteins and no determination was made as to whether they were actually infected or simply carrying viral proteins from the site of infection . To distinguish these two possibilities we isolated MLNs from PR8-infected mice at defined time points after aerosol infection and measured viral mRNA transcripts from all 8 segments of influenza virus . As shown in Figure 1A transcripts of all 8 segments were identified in the MLN of infected mice . The kinetics of the appearance of the viral mRNA in the MLN correlated perfectly with our previous results for Ag-bearing DC migration [10] as it was only detectable starting at 48 hpi , increased steadily , peaked by 96 hpi , and persisted for at least 6 dpi . Homogenates collected from the MLN demonstrated the presence of infectious virus particles at time points that correlated with the appearance of viral mRNA and Ag-bearing DC migration ( Figure 1B ) [9] , [10] , [11] . Inspection of CD11c+ cells in the MLNs by immunohistochemistry confirmed viral antigen positive cells only after 48 hpi ( Figure 1C ) . Migration of lung DCs is controlled by the chemokine receptor CCR7 [8] , [29] , [30] , and can be blocked by the use of pertussis toxin ( PTX ) [7] . Intranasal PTX administration to infected mice completely abolished the appearance of viral mRNA in the MLNs ( Figure 2A ) , and significantly reduced the numbers of CCR7+ DCs in the MLNs ( Figure 2B ) but had no effect on virus replication in the lungs ( Figure 2C ) . These data show that virus reaches the draining MLNs by cell-associated transport rather than by leakage of free virus through efferent lymphatics . Both of the major lung DC subsets , CD103+ DCs and CD11bhigh DCs , transport viral Ag to the MLNs [11] , [31] , but whether it is in the form of infectious virus has not been determined . Figure 3A-C shows a sorting strategy that was used to isolate migratory DCs and other cells from the MLNs of infected animals . Total migratory DCs were isolated and co-cultured ( gate V , Figure 3B ) with virus permissive MDCK cells in vitro using decreasing numbers of cells . The DC-depleted lymph node cells were similarly cultured with MDCK cells ( Figure 3B , gate i-iii pooled together ) . Infectious virus was isolated from MDCK cells cultured with 1 , 000 fold less migratory DCs than was observed when DC-depleted lymph node cells were used indicating that DCs were the primary transporters of infectious virus to the MLNs ( Figure 3D ) . Plaque immunostaining of MDCKs infected with supernatant from these co-cultures confirmed the presence of live virus ( Figure 3D’ ) . When individual migratory lung DC subsets were stained for viral NP and visualized by confocal microscopy both CD103+ DCs ( gate VI , Figure 3B ) and CD11bhigh DCs ( gate VII , Figure 3B ) were found to have abundant intracellular Ag ( Figure 3E ) . NP co-localized to the nucleus in CD103+ DCs ( Figure 3E ) . In contrast , NP in CD11bhigh DCs surrounded but did not co-localize with the nucleus ( Figure 3E ) . To test which DC subset transferred infectious virus particles to MDCKs in vitro , CD103+ DCs and CD11bhigh DCs isolated from MLNs at 72 or 96 hpi were injected separately into embryonated-chicken eggs ( Figure 3F ) . 40 h later the allantoic fluid was collected and tested for the presence of replicating virus by agglutination of chicken red blood cells ( RBCs ) . Only CD103+ DCs ( gate VI , Figure 3B ) injected into eggs led to the production of virus particles ( Figure 3F ) , and the presence of infectious virus was confirmed by plaque immunostaining of MDCKs infected with the allantoic fluid from these samples ( Figure 3F’ ) . Neither CD11bhigh DCs ( gate VII , Figure 3B ) nor any other major leukocyte populations ( gate B: B220+ cells; gate G: Gr1+ cells; gate P2: pDCs , Figure 3C ) caused infectious virus production in embryonated eggs ( Figure 3F ) . Moreover , depletion of Langerin+ CD103+ DCs in PR8-infected Langerin-DTR EGFP mice [32] reduced virus mRNA significantly in the MLNs ( Figure 4A ) , with no apparent compromise of lung virus titers ( Figure 4B ) . Total depletion of CD11c+ cells from infected mice virtually eliminated viral mRNA from the MLN [33] ( Figure 4C and 4D ) . Multicycle replication of PR8 virus in vitro requires L-1-tosylamido-2-phenylethyl chloromethyl ketone treated-trypsin ( TPCK-trypsin ) to promote HA cleavage and spread to uninfected cells [34] . We next tested whether virus infection of MDCKs via contact with migratory DCs was dependent on TPCK-trypsin . As shown in Figure S1A , MDCK cells were infected in the absence of trypsin when co-cultured for 2 days with migratory DCs ( see black arrows ) , showing that the transfer of infectious virus to MDCKs was independent of an exogenous added protease . As expected , subsequent robust spread of PR8 virus in MDCK cells was dependent on TPCK-trypsin ( Figure S1A ) . We repeated the experiment with the closely related influenza strain known as WSN virus that is not dependent on TPCK-trypsin for multicycle replication [35] , [36] . MDCKs co-cultured with MLN-DCs sorted from WSN infected mice were infected independently of trypsin ( Figure S1A ) . Similar to the ability of CD103+ DCs to transfer infectious virus to embryonated eggs ( Figure 3 ) , virus could be transferred to MDCK cells upon co-culture with particular DC subsets isolated from PR8 and WSN infected mice . Specifically , only CD103+ DC but not CD11bhigh DCs caused ex-vivo virus infection of MDCKs , and trypsin was necessary for multicycle replication depending on the type of virus utilized ( Figure S1B ) . As shown here and in previous work , at 2 dpi a rapid inflammatory response develops in the infected lung creating a milieu of proinflammatory cytokines including I-IFNs . IFNAR signaling in hematopoietic cells such as DCs induces the expression of several ISGs that promote the antiviral state [37] , [38] , [39] . The migration of lung DCs is initiated amidst this inflammatory environment and it was surprising that viral replication in the CD103+ DCs was not inhibited by the antiviral response triggered by I-IFN signaling [10] , [40] . Therefore we compared elements associated with IFNAR signaling in steady state CD103+ DCs and CD11bhigh DCs sorted from lungs of naïve mice ( sorting strategy , Figure 5A ) . As shown in Figure 5B , lung CD103+ DCs show a reduced expression of Ifnar1 and Ifnar2 chains , the Jak1 kinase , as well as the transcription factor Stat1 , when compared to lung CD11bhigh DCs . These four genes represent key elements of the IFNAR receptor-signaling complex . To determine if the decreased expression of IFNAR signaling components translates into differences in ISG upregulation CD103+ DCs and CD11bhigh DCs were sorted from the MLNs during infection . Both DC subsets found in the MLNs of infected animals showed upregulated ISGs , however wild type CD11bhigh DCs produced higher levels of the ISG gene products than CD103+ DCs , a difference that was abrogated in the absence of IFNAR signaling ( Figure 5C ) . In agreement with a differential IFNAR signaling and ISG response by lung DCs , CD103+ DCs collected from MLNs of infected animals showed greater permissivity to virus replication when compared to CD11bhigh DCs , expressing approximately ten times more viral NP mRNA ( Figure 5D ) . IFNAR deficiency exacerbated the virus burden in CD103+ DCs and increased significantly the viral mRNA content in CD11bhigh DCs ( Figure 5D ) . The difference in virus susceptibility between the CD103+ DCs and CD11bhigh DCs can be observed at the earliest time points in DCs isolated directly from the lungs of infected mice and mirrors the observations in cells collected from the MLN ( Figure 5E ) . These results suggest that IFNAR signaling effectively restricts virus replication in CD11bhigh DCs whereas CD103+ DCs are somewhat refractory to this cytokine . Consistent with these findings , viral mRNA levels in the MLN of infected IFNAR -/- mice were significantly higher than in wild type animals ( Figure 1A ) indicating that the amount of virus transported to the MLN was higher in IFNAR -/- mice ( Figure 5F ) . To determine if the increase in viral mRNA seen in the MLNs from IFNAR -/- mice could result from enhanced virus replication from migratory lung DCs , we sorted CD103+ DCs and CD11bhigh DCs from infected IFNAR -/- and IFNAR +/+ mice at 3 and 4 dpi . Individual DC subsets were injected into embryonated eggs and the presence of infectious virus determined 40 h later by hemagglutination of the allantoic fluid . In contrast to what was observed with CD11bhigh DCs from wild type animals , IFNAR -/- CD11bhigh DCs were now permissive for productive infection by influenza virus ( Figure 6A ) . Data from a number of laboratories have confirmed that CD103+ DCs are superior APCs for the priming of naïve CD8+ T cells [11] , [12] , [31] . Our data indicated a possible relationship between virus replication and potent Ag presentation capacity . Therefore we isolated CD103+ DCs and CD11bhigh DCs from IFNAR +/+ and IFNAR -/- mice infected with PR8-OT-I virus to determine whether suppressed viral replication in CD11bhigh DCs by IFNAR signaling would be the etiological reason behind this difference in Ag presentation . As anticipated , in vitro proliferation of naïve CD8+ OT-I T cells was superior when co-cultured with IFNAR +/+ CD103+ DCs compared to IFNAR +/+ CD11bhigh DCs ( Figure 6B ) . Strikingly , OT-I T cell proliferation was comparable when both DC subsets were isolated from IFNAR -/- mice ( Figure 6B ) and the production of interferon-gamma ( IFN-γ ) from the OT-I T cells was almost equivalent ( Figure 6C ) . As a control , lymph-node resident CD8α+ DCs , known to be potent APCs for CTL priming [12] , were sorted from PR8-OT-I infected IFNAR +/+ and IFNAR -/- mice ( sorting strategy , Figure S2 ) and co-cultured with OT-I cells in vitro ( Figure 6D ) . IFNAR +/+ CD8α+ DCs induced OT-I T cell proliferation in vitro but not as robustly as IFNAR +/+ CD103+ DC . In contrast , IFNAR -/- CD8α+ DCs gained an enhanced ability to present Ag in the absence of IFNAR signaling similarly to what is observed for CD11bhigh DCs . These data show that viral replication in a given migratory DC population correlates with priming efficiency of naïve CD8+ T cells and the observed differences in priming are not necessarily an intrinsic feature of each DC subset and can be modulated by I-IFN signaling .
The lung serves as a portal for the entry of a myriad of respiratory viruses throughout the lifetime of the host . Induction of cellular T cell immunity requires the obligate interaction of lung DCs with viruses , with the imminent risk of infection . Our data demonstrates that both lung DC subsets are infected with influenza virus in vivo and this event does not impair their migration to the MLNs or their function as APCs . By carefully examining each lung DC subset we found that CD103+ DCs had much greater levels of viral mRNA and produced infectious virus particles , a finding that challenges the widely held belief that influenza virus replicates only in the lung epithelium [1] , [2] . On the other hand , CD11bhigh DCs expressed viral mRNA to a much lesser extent and did not support virus growth ex-vivo suggesting that these cells were abortively infected . The dramatic differences in virus susceptibility were striking and led us to investigate whether I-IFNs were uniquely sensed by both DC subsets . Based on our previous observations , DCs begin to migrate in vivo only when lung inflammation is triggered ∼2 dpi with influenza virus [10] [27] . At this time , the antiviral cytokines , I-IFNs , are readily abundant but their effects on lung DC function were not previously addressed . Importantly , I-IFNs confer only partial protection to leukocytes during respiratory virus infection in vivo [40] , and this is further supported by in vitro experiments where DCs and other leukocytes sustain some degree of viral transcription even after treatment with very high concentrations of I-IFNs [41] , [42] . Therefore , migrating lung DCs are not necessarily refractory to influenza virus infection , even if they are I-IFN-primed before encountering the virus . In the present study we found that both lung DCs displayed an “interferon signature” ( upregulation of ISGs ) but the magnitude of the responses was quite different depending on the DC subset analyzed . Indeed , CD103+ DCs were found to be “inherently resistant” to IFNAR signaling with an attenuated ISG response when compared to CD11bhigh DCs . Consequently , our confocal imaging data showed nuclear localization of NP only in CD103+ DCs but not in CD11bhigh DCs suggesting that the latter subset has a tight control over virus replication mediated by stronger I-IFN signaling . By ablating IFNAR signaling in CD11bhigh DCs we could increase their viral mRNA replication to levels comparable to wild type CD103+ DCs , and it was enough to promote infectious virus production by CD11bhigh DCs . Experimental evidence suggests a divergent hematopoetic origin of lung DC subsets . CD103+ DCs appear to arise from pre-DC precursors while CD11bhigh DCs are monocyte derived [43] , [44] , [45] , [46] , and this dichotomy could determine unique IFNAR signaling differences . Currently we are studying in more detail the underlying mechanisms explaining why CD103+ DCs have an attenuated IFNAR signaling and an inherent susceptibility to virus infection . Some of these include , inhibitory genes downstream of the IFNAR signaling pathway [47] , [48] , silencing of ISGs by epigenetic modifications of chromatin [49] , specific expression of microRNAs that favor virus replication or targeting of particular IFNAR signaling components [50] , and signaling by suppressor cytokines such as IL-10 [51] . The differential susceptibilities to virus infection by both lung DCs suggested that the relative viral Ag abundance might act as a limiting factor for peptide presentation by mayor histocompatibility class-I ( MHC-I ) molecules . CD103+ DCs have been characterized as APCs with superior abilities to prime naïve CD8+ T cells relative to CD11bhigh DCs , though both are equally competent at priming naïve CD4+ T cells [11] , [31] , [52] . The underlying reason for this fundamental difference has been ascribed to a division of labor theory [53] ( first described for splenic DCs ) where unique gene programs define specialized machinery for MHC-II or MHC-I Ag presentation within each DC subset [8] . Here we propose an alternative model for this dichotomy based on the strength of their response to I-IFNs that determines the efficiency in MHC-I Ag presentation by controlling the level of virus replication within each DC subset . The prominent natural susceptibility of CD103+ DCs to virus infection may represent a mechanism that ensures adequate MHC-I Ag presentation in a cytokine milieu dominated by I-IFNs [10] , [40] , [42] leading to a higher density of peptide-MHC-I complexes on the cell surface facilitated by constant degradation of newly synthesized viral proteins [54] , [55] , [56] , [57] . On the other hand , decreased virus replication in CD11bhigh DCs reduces peptide-MHC-I complex production , but loss of IFNAR signaling boosts viral replication and their ability to prime naïve CD8+ T cells to levels comparable to CD103+ DCs . In this study , we did not address whether cross-priming [58] represents a major component regulating Ag presentation by CD103+ DCs and CD11bhigh DCs and further studies will be required to address their relationship to direct infection of lung DCs . Intriguingly , lymph node resident-CD8α+ DCs known to be potent APCs for cytotoxic T cells [12] were also boosted by the absence of IFNAR signaling . CD8α+ DCs might benefit from increased numbers of virus-infected DCs ( CD103+ DC and CD11bhigh DC ) in infected IFNAR -/- mice that may undergo cell death or release viral Ag that upon endocytosis may be used as substrates for crosspriming . Alternatively , I-IFN signaling might be responsible for lymph-node CD8α+ DC crosspresentation in influenza-infected wild-type mice ensuring optimal DC maturation [26] and increased processing of viral antigen from migratory DCs [12]; despite tight control of virus replication by I-IFN . The hypersensitive IFNAR signaling by CD11bhigh DCs may divert these cells from naïve CTL priming to accomplish other specific tasks in the immune response to influenza virus , which is essentially multifactorial [15] , [59] . Some of these functions may include , the production of high affinity IgG antibodies that may depend upon the continuous migration of antigen-bearing CD11bhigh DCs derived from inflammatory monocytes [60] , [61] , [62] , restimulation of effector CD4+ and CD8+ T cells locally in the lungs to trigger cytokine production and cytotoxicity [63] , and as potent cytokine and chemokine secretion machines activating and recruiting innate and adaptive effector cell populations to the lungs and lymph nodes [64] . Additionally , the robust I-IFN response may have a protective role in sparing CD11bhigh DCs from death that may explain late antigen presentation during infection to CTLs in the MLNs and lungs , which lingers long after the virus has been cleared [65] , [66] , [67] , [68] . This knowledge about CD11bhigh DCs can potentially be harnessed to improve live DC vaccines in human medicine . The likely human version of CD11bhigh DCs is easily derived from blood monocytes [69] , [70] and is commonly used for in vivo immunization protocols [71] . IFNAR signaling suppression in these cells may increase the replication of recombinant attenuated viruses or vectors improving vaccine efficacy for diverse diseases such as cancer [72] , [73] , [74] and HIV [75] , [76] , [77] , [78] . A danger inherent to the unique sensitivity of CD103+ DCs to infection is that viruses likely evolved to exploit these cells as shuttles and spread to multiple locations . Various viruses of worldwide health concern , such as measles and varicella zoster use the respiratory tract as an entry route from where they disseminate systemically [79] , [80] . It would not be surprising , if these viruses hijacked the CD103+ DC equivalent in the human lung as an initial Trojan horse , and upon arrival to the MLNs spread to other susceptible cells that travel out of the lymph nodes into systemic circulation . The same might be happening with particular influenza virus strains in the human respiratory tract such as the highly pathogenic H5N1 virus whose HA contains a polybasic multi-cleavage site [81] , and may target CD103+ DCs to spread hematogenously . Our experiments with WSN strain partially demonstrate this phenomenon , since virus could spread from CD103+ DCs ex-vivo and infect MDCKs without the aid of an exogenously added protease such as trypsin . It remains to be determined whether in vivo , CD103+ DCs can promote systemic spread of viruses and whether lymph-node specific proteases aid the cleavage of diverse hemagglutinins of influenza viruses . Altogether , our findings are presented as a model summarized in Figure 7 , where the unique sensitivities to IFNAR signaling by individual lung DC subsets have divergent consequences for the adaptive immune response and as well as for viral pathogenesis .
All animal work was conducted in agreement with approved protocols by the Institutional Animal Care and Use Committee ( IACUC ) at the Mount Sinai School of Medicine ( Protocol Numbers #: 96-308 and 08-0951 ) and in accordance with guidelines in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The program is fully accredited by the Association for Assessment & Accreditation of Laboratory Animal Care , International ( AAALAC ) . C57BL/6 wild type mice were purchased from Taconic or Jackson laboratories . OT-I transgenic mice [82] were purchased from Jackson Laboratories . OT-I mice were crossed to B6 . SJL mice to generate CD45 . 1+ OT-I mice . C57BL/6 IFNAR -/- mice [83] , [84] were kindly provided by Dr . Wilson ( Immunology Department , University of Washington , USA ) . C57BL/6 Langerin diphtheria toxin receptor ( DTR ) EGFP [32] mice were kindly provided by Dr . Bernard Malissen ( INSERM , Lyon , France ) . C57BL/6 CD11c-DTR EGFP mice [33] were purchased from Jackson laboratories . All mouse colonies were kept under pathogen-free conditions at the Animal Facility of the Mount Sinai School of Medicine . Influenza virus strains A/PR/8/1934 ( H1N1 ) ( PR8 ) , recombinant PR8-OTI , and A/WSN/1933 ( H1N1 ) ( WSN ) were grown in 10-day embryonated eggs ( Charles Rivers , Spafas ) . PR8-OT-I virus [85] was kindly provided by Dr . Peter Doherty and Dr . Paul Thomas ( St . Jude's Research Children's Hospital , Memphis , TN ) . Mice were infected using an Inhalation Exposure System A42X ( Glass-Col , USA ) . Influenza virus was diluted in PBS to obtain a solution of 107 . 9 virus particles/12 ml . The virus solution was placed inside of a sterile glass nebulizer connected to the infection chamber . Total exposure time with aerosolized virus was 30 min . Under these conditions 100% of animals were infected and showed reproducible lung titers in several experiments at every time point analyzed as described previously [9] , [10] . WSN infection of mice was performed by intranasal infection . Briefly , mice were anesthetized with Avertin ( Tribromoethanol , Acros Organics ) by intraperitoneal injection , and 1 , 000 plaque forming units ( pfu ) of WSN virus in 35 µl PBS was applied intranasally to each mouse . Mice were monitored until they were fully awake and placed back into their cages . MLNs from infected animals were mechanically dissociated and digested in DMEM/1% FCS supplemented with 0 . 25 mg/ml collagenase ( Liberase type III , Roche ) for 20 min at 37°C . Collagenase was neutralized by adding sterile PBS containing 2% FCS and 2 . 5 mM EDTA for 5 min , and then passing the dissociated cell suspension through a 70 µm strainer ( BD Biosciences ) . Single cell suspensions were treated with red-blood cell lysis buffer ( BD Biosciences ) and resuspended in blocking buffer ( PBS containing 2% FCS and 10 µg/ml Fc-receptor block ) . Lungs from infected mice were perfused with PBS to eliminate excess blood . Lung lobes were gently dissociated using forceps and digested for 50 min at 37°C in DMEM/1% FCS supplemented with collagenase-D ( Roche ) at 0 . 25 mg/ml . Single cell suspensions were obtained as described for MLNs . For DC subset enumeration in the MLNs , lymph node cell suspensions were stained with antibodies against multiple surface antigens: anti-CD11c ( clone HL-3 ) , MHC-II ( clone 2G9 ) , Gr1 ( Ly6C/Ly6G clone RBC865 ) , Ly6C ( Al-21 ) , CD103 ( clone 2E7 ) , CD11b ( clone M1/70 ) , B220 ( clone RA3-6B2 ) , CD8α ( clone 53-6 . 7 ) . All antibodies were purchased from BD Bioscience , eBiosciences , and Biolegend . Lymph node DCs were identified as CD11chigh , MHC-II high , B220 negative , and Gr1 negative cells . Individual DC subsets were further gated as CD103+ DC and CD11bhigh DC that were negative for CD8α . Lymph node resident CD8α+ DCs were identified in the MLN by gating on CD11chigh MHC-II intermediate cells as described elsewhere [86] . For enumeration of lung DC subsets , cell suspensions were stained with antibodies against multiple surface antigens: anti-CD11c ( clone HL-3 ) , MHC-II ( clone 2G9 ) , Gr1 ( Ly6C/Ly6G clone RBC865 ) , Siglec-F ( clone E50-2440 ) , CD103 ( clone 2E7 ) , CD11b ( clone M1/70 ) , B220 ( clone RA3-6B2 ) , CD8α ( clone 53-6 . 7 ) . Lung DCs were identified as CD11chigh , MHC-II high , Siglec-F negative , B220 negative , and Gr1 negative cells . Individual lung DC subsets were further gated as CD103+ DCs and CD11bhigh DCs . Samples were acquired using a BD LSR-II flow cytometer at the Flow Cytometry Core Facility , Mount Sinai School of Medicine . Dead cells were excluded by DAPI staining . Data was analyzed using FlowJo software ( Treestar Corp . ) MLNs and lungs were isolated from infected and control mice as described above under sterile conditions for cell sorting . Total cell suspensions or enriched DC fractions were used to sort individual CD103+ DC , CD11bhigh DC , and CD8α+ DC subsets . Other MLN cell types such as B cells , pDCs and Gr1+ cells were sorted from total cell suspensions . CD11c+ cells were positively selected using anti-CD11c beads ( Miltenyi Biotech ) or by negative selection utilizing a cocktail of biotinylated mabs , that included anti-Gr1 ( clone RB6-8C5 ) , anti-CD19 ( clone 6D5 ) , anti-Ter119 ( clone Ter-119 ) , anti-CD3 ( clone 17A2 ) , and anti-CD49b ( clone DX5 ) followed by incubation with anti-biotin beads ( Miltenyi Biotec ) . In both cases , cells were passed through LS magnetic columns ( Miltenyi Biotec ) and positive or negative fractions were collected depending on the method employed , then cells were stained as described above and sorted using a BD Aria-II cell sorter ( Flow Cytometry Core Facility , Mount Sinai School of Medicine ) . MLNs isolated from PR8 infected mice , were embedded in optimal cutting temperature ( O . C . T ) medium ( Tissue-Tek ) , frozen over dry ice , and stored at -80° C until further use . 8 µm sections were cut with using a cryostat ( Leica ) , placed over coated microscope slides , and fixed with 4% paraformaldehyde in PBS for 5 min at room temperature . Sections were blocked with PBS containing 2% FCS and 1% mouse serum for 20 min . Then , incubated with purified rat anti-B220 ( clone RA3-6B2 ) and purified hamster anti-CD11c ( clone HL3 ) for 45 min , washed twice with PBS and incubated with goat anti-rat Alexa-647 ( Invitrogen ) and goat-anti-hamster FITC ( Jackson Immunoresearch ) polyclonal sera , to visualize B220 ( B cell areas ) and CD11c ( DCs ) , respectively . Following surface staining , slides were permeabilized with PBS containing Saponin 0 . 02% for 30 min and then incubated with a mixture of biotinylated antibodies to influenza NP ( clone HT103 ) and M ( 2E10 ) proteins . Slides were washed twice and then incubated with Streptavidin-Cy3 ( Sigma ) . Slides were air-dried , and mounted for fluorescence microscopy using Prolong Antifade with DAPI ( Invitrogen ) . Immunofluorescence was performed using a Zeiss Axioplan2 fluorescence microscope . Images were analyzed using ImageJ software . Plaque immunostaining assays were performed to quantitate virus titers from infected mice . Lung homogenates from infected mice ( 1 lung was homogenized in 1 . 8 ml of PBS containing 0 . 1% Gelatin ) were 10-fold serially diluted . 24 well plates were seeded with Mardy-Darby Canine Kidney ( MDCK ) cells , 1 day before the infection to achieve confluent monolayers . The plates were washed 3 times with DMEM , and incubated for 1 hr with 100 µl of infected-lung homogenates . After the inoculums were removed , the cells were washed once with DMEM , and 500 µl of overlay media ( DMEM-F12 containing 0 . 6% agar ( Oxoid ) , 0 . 5% Albumin ( MP biomedicals ) , 0 . 1% NaHCO3 , antibiotics and 1 µg/ml TPCK-trypsin ( Worthington ) ) was added on top . Infected monolayers were incubated for 40 h at 37°C , and then fixed with 4% paraformaldehyde for 1 hr . Agar overlays were removed gently under running water . Fixed monolayers were washed with PBS twice , and incubated with chicken anti-PR8 polyclonal sera ( Charles River ) followed by goat HRP-anti-chicken ( Jackson Immunoresearch ) . Plaques were visualized after incubation with the True Blue substrate ( KPL ) that produces a blue precipitate by an HRP mediated reaction . Plaque forming units ( pfu ) were counted and virus titers were expressed as pfu/ml . To determine virus titers in lymph nodes , MLNs were isolated at different time points after infection and homogenized in 500 µl of PBS . Immediately after homogenization , samples were pooled , and 10-fold serial dilutions were injected in triplicates into 10-day embryonated-chicken eggs . 40 h post-inoculation , allantoic fluid was harvested to determine virus particles by hemagglutination of red blood cells ( RBCs ) . Endpoint titers were determined by the method of Reed and Muench [87] . A CD8+ T cell isolation kit ( Miltenyi Biotec ) was used to isolate untouched OT-I CD8+ T cells following manufacturer instructions . OT-I naive T cells were labeled with carboxyfluorescein diacetate succinimidyl ester ( CFSE ) at a final concentration of 2 . 5 µM . Individual MLN CD11c+ cell populations ( CD103+ DCs , CD11bhigh DCs and CD8α+ DCs ) were isolated at day 4 post-infection from wild type or IFNAR -/- mice infected with PR8-OT-I and co-cultured in 96 well plates with CFSE-labeled OT-I transgenic T cells at a ratio of 10 , 000 DCs to 50 , 000 CFSE-labeled transgenic T cells per well . 60 h later , T cell proliferation was quantitated by CFSE dilution using flow cytometry as described elsewhere [9] . Dead cells were excluded using PI or DAPI staining . A suspension of sorted CD103+ DC and CD11bhigh DCs in 100 µl , were placed over microscope poly-lysine coated slides ( Shandon ) and let to adhere for 2 h at 37°C in an incubator inside a humidified hybridization chamber ( Sigma ) in order to avoid evaporation of the media . After the incubation time was over , the media was rapidly aspirated ( avoiding the cells to dry ) , and a drop of ∼100 µl of freshly prepared 4% paraformaldehyde in PBS was added to fix the adherent cells at room temperature . After 3 min , the solution was aspirated and replaced with fresh paraformaldehyde 4% ( 100 µl per sample ) and the cells were fixed for additional 15 min . Cells were permeabilized using a solution of Saponin 0 . 02% dissolved in PBS . Slides were blocked with PBS containing 1% FBS , 1% mouse serum ( Jackson Immunoresearch ) and Saponin 0 . 02% for 20 min , and stained for viral NP with biotinilated-HT103 monoclonal antibody for 30 min at room temperature . Followed by 3 washes in Saponin-PBS , secondary staining was performed using Streptavidin-Cy3 ( Sigma ) . Slides were air-dried and Prolong Antifade with DAPI ( Invitrogen ) was used as mounting media . Fluorescence Microscopy was performed on a Zeiss Inverted LSM 510 laser scanning confocal instrument mounted on a Zeiss Axiovert 200M microscope . All images were acquired using a 100x oil objective . Images were analyzed with ImageJ software . MLN DCs from PR8 ( day 2 to day 4 post-infection ) or WSN infected mice ( day 3 post-infection ) were sorted by FACs and co-cultured for 3 days with confluent MDCK cells seeded on 96 well plates in DMEM media containing 0 . 5% Albumin ( MP biomedicals ) , 0 . 1% NaHCO3 and antibiotics . The culture media was either supplemented or not with TPCK-trypsin ( 1 µg/ml ) . Supernatants from these cultures were assayed for the presence of replicating virus by hemagglutination of RBCs to determine whether DCs were capable of transferring infectious virus particles to MDCKs in vitro . Infected MDCK cells were visualized via immunostaining with polyclonal anti-chicken PR8 developed with horseradish peroxidase reaction with True Blue substrate as described for plaque immunostaining assays . Images of infected MDCK cells were acquired using a Nikon Eclipse TS100 microscope light microscopy . Alternatively , sorted DCs or other sorted cells from the MLNs were injected directly into 10-day embryonated eggs . 2 days later , allantoic fluid was isolated and presence of virus was determined by hemagglutination of RBCs . In parallel , allantoic fluid from these experiments was serially diluted and used to infect MDCK cells to determine whether the virus in these preparations was infectious as measured by plaque immunostaining on MDCK cells . Lungs or lymph nodes ( 3–5 pooled lymph nodes ) from infected mice were homogenized in 3 ml/sample of Trizol Reagent ( Invitrogen ) and RNA was isolated as indicated by the manufacturer . Total mRNA was converted to cDNA by RT-PCR using oligo-dT reaction ( Affinity Script , Stratagene ) . cDNA was diluted 50 times in water and triplicate reactions were setup in 384-well plates . qPCR reactions based on SYBR green detection , were performed using a Lightcycler equipment ( Roche , USA ) all reactions were normalized to α-tubulin as previously described [88] . qPCR reactions with sorted DCs required mRNA amplification . The WT-OVATION amplification Kit ( Nugen , San Diego , USA ) was used for this purpose as described by the supplier . The list of primers used in this study is the following: M1/2 forward: 5′-GGGAAGAACACCGATCTTGA-3′; M1/2 reverse: 5′-CGGTGAGCGTGAACACAAAT-3′; NA forward: 5′-CATCTCTTTGTCCCATCCGT-3′; NA reverse: 5′-GTCCTGCATTCCAAGTGAGA-3′; HA forward: 5′-GAGGAGCTGAGGGAGCAAT-3′; HA reverse: 5′-GCCGTTACTCCGTTTGTGTT-3′; PB1 forward: 5′-CCTCCTTACAGCCATGGGA-3′; PB1 reverse: 5′-GTGCTCCAGTTTCGGTGTTT-3′; PB2 forward: 5′-GGATCAGACCGAGTGATGGT-3′; PB2 reverse: 5′-CCATGCTTTAGCCTTTCGACT-3′; PA forward: 5′-CATCAATGAGCAAGGCGAGT-3′; PA forward: 5′-GCCCCTGTAGTGTTGCAAAT-3′; NP forward: 5′-CAGCCTAATCAGACCAAATG-3′; NP reverse: 5′-TACCTGCTTCTCAGTTCAAG-3′;NS1 forward: 5′-TTCACCATTGCCTTCTCTTC-3′; NS1 reverse: 5′-CCCATTCTCATTACTGCTTC-3′; Mx1 forward: 5′-CAACTGGAATCCTCCTGGAA-3′; Mx1 reverse: 5′-GGCTCTCCTCAGAGGTATCA-3′; Isg15 forward: 5′-GAGCTAGAGCCTGCAGCAAT-3′; Isg15 reverse: 5′-CTTCTGGGCAATCTGCTTCT-3′; Bst2 forward: 5′- CAAACTCCTGCAACCTGACC-3′; Bst2 reverse: 5′- CATTCTCAAGCTCCTTGATGC-3′; Ifnar1 forward: 5′ -GGTTGATCCGTTTATTCCATTC-3′; Ifnar1 reverse: 5′- CCACATGTTCCCGTCTTGT-3′; Ifnar2 forward: 5′-CTTCGTGTTTGGTAGTGATGGT-3′; Ifnar2 reverse: 5′-GGGGATGATTTCCAGCCGA -3′; Stat1 forward: 5′-CTTCAGCAGCTGGACTCCAA -3′; Stat1 reverse: 5′- GGTCGCAAACGAGACATCAT-3′; Stat2 forward: 5′-AAGAGGTGCAGCCCCCACCA-3′; Stat2 reverse: 5′-GCTGCGCCTGTTGGCTCTGA-3′; Jak1 forward: 5′- TGCAGGAGGGAGCCTGGCAT-3′; Jak1 reverse: 5′-AGCTTGCCCCAGGGGATCGT-3′; Tyk2 forward: 5′-AGCCATCTTGGAAGACAGCAA-3′; Tyk2 reverse: 5′-GACTTTGTGTGCGATGTGGAT-3′; α-tubulin forward: 5′-TGCCTTTGTGCACTGGTATG-3′; α-tubulin reverse: 5′-CTGGAGCAGTTTGACGACAC-3′ . 2 h after aerosol infection , mice were anesthetized and 2 µg of PTX dissolved in 35 µl of PBS was delivered intranasally to each mouse . Once a day thereafter , mice received 0 . 5 µg of PTX i . p to maintain the chemokine receptor blockade . MLNs and lungs of mock and PTX treated mice were collected at day 1 , 2 , and 3 post-infection . To further confirm ablation of lung DC migration , absolute numbers of CCR7+ CD11c+ cells were determined in infected mice treated with PBS or PTX . Mouse anti-CCR7 antibody ( clone 4B12 ) was used for this experiment . Langerin+ CD103+ DCs were depleted in PR8-infected Langerin-DTR EGFP mice with an intraperitoneal ( i . p ) injection of 1 µg of diphtheria toxin ( DT , Sigma ) , 1 day before infection . At day 0 , mice were infected with aerosolized PR8 virus . Thereafter , a daily dose of 200 ng was administered i . p to each mouse to maintain the DC depletion . Lungs and MLNs were harvested every day for RNA extraction and virus titer determination . For total CD11c+ cell depletion , CD11c-DTR EGFP mice were injected i . p with 4 ng/g ( body weight ) of DT , 1 day before PR8 infection . Thereafter daily doses of 30 ng of DT were administered i . p up to day 3 . Viral message in the MLNs was determined by qPCR as described above . Lung titers were determined by plaque immunostaining assay . To determine the concentration of IFN-γ from mixed DC-T cell cultures , an ELISA kit from R&D ( UK ) was used according to the manufacturer's instructions . Averaged results were expressed as means+/- standard deviation . A two-tailed Student's t test was used to determine statistical significance of selected samples . P values< 0 . 05 ( 95% Confidence ) were considered to be significant . Graphs were designed either in Excel or Graph Pad software . CD11c ( name: Itgax , ID: 16411 ) ; CD11b ( name: Itgam , ID: 16409 ) ; CD103 ( name: Itgae , ID: 16407 ) ; Ifnar1 ( name: Ifnar1 , ID: 15975 ) ; Ifnar2 ( name: Ifnar2 , ID: 15976 ) ; Stat1 ( name: Stat1 , ID: 20846 ) ; Stat2 ( name: Stat2 , ID: 288774 ) ; Jak1 ( name: Jak1 , ID: 16451 ) ; Tyk2 ( name: Tyk2 , ID: 54721 ) ; Ly6C ( name: Ly6c1 , ID: 17067 ) ; Ly6G ( name: Ly6g , ID: ) ; CD8α ( name: Cd8a , ID: 12525 ) ; Isg15 ( name: Isg15 , ID: 100038882 ) ; Mx1 ( name: Mx1 , ID: 17857 ) ; Bst2 ( name: Bst2 , ID: 69550 ) ; IFN-γ ( name: Ifng , ID: 15978 ) ; Siglec-F ( name: Siglec5 , ID: 233186 ) ; B220 ( name: Ptprc , ID: 19264 ) ; CD45 ( name: Ptprc , ID: 19264 ) ; Langerin ( name: Cd207 , ID: 246278 ) ; CCR7 ( name: Ccr7 , ID: 12775 ) ; HA ( name: HA , ID: 956529 ) ; NP ( name: NP , ID: 956531 ) ; NS1 ( name: NS1 , ID: 956533 ) ; NA ( name: NA , ID: 956530 ) ; PA ( name: PA , ID: 956535 ) ; PB1 ( name: PB1 , ID: 956534 ) ; PB2 ( name: PB2 , ID: 956536 ) ; M ( ( name: M1 , ID: 956527 and name: M2 , ID: 956528 ) ; MHC-II ( name: H2-Ab1 , ID: 14961 and name: H2-Aa , ID: 14960 ) . | Migratory lung dendritic cells ( DCs ) control the initiation of the adaptive immune responses to influenza virus by expanding virus-specific T cells in draining lymph nodes ( MLNs ) that will subsequently clear the pathogen from the respiratory tract . Here we demonstrate that both subsets of lung DCs , CD103+ DCs and CD11bhigh DCs become infected by influenza virus in vivo and migrate to the MLNs , but only CD103+ DCs support productive virus replication . Enhanced virus replication in CD103+ DCs compared to CD11bhigh DCs was responsible for their superior antigen presentation efficacy for naïve CD8+ T cells and originated from a difference in sensitivity of the two DC populations to type I interferon ( I-IFN ) . These data show that in contrast to most other immune cell types , DCs can become productively infected with influenza virus and I-IFN operates as a master regulator controlling which DC subset will present antigen during a viral infection . A deeper understanding of basic innate and adaptive immune response mechanisms regulated by I-FN may lead to the development of cutting edge therapies and improve vaccine efficacy against influenza and other viruses . | [
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"immunity"
] | 2011 | Unique Type I Interferon Responses Determine the Functional Fate of Migratory Lung Dendritic Cells during Influenza Virus Infection |
Parasite-specific antibodies protect against blood-stage Plasmodium infection . However , in malaria-endemic regions , it takes many months for naturally-exposed individuals to develop robust humoral immunity . Explanations for this have focused on antigenic variation by Plasmodium , but have considered less whether host production of parasite-specific antibody is sub-optimal . In particular , it is unclear whether host immune factors might limit antibody responses . Here , we explored the effect of Type I Interferon signalling via IFNAR1 on CD4+ T-cell and B-cell responses in two non-lethal murine models of malaria , P . chabaudi chabaudi AS ( PcAS ) and P . yoelii 17XNL ( Py17XNL ) infection . Firstly , we demonstrated that CD4+ T-cells and ICOS-signalling were crucial for generating germinal centre ( GC ) B-cells , plasmablasts and parasite-specific antibodies , and likewise that T follicular helper ( Tfh ) cell responses relied on B cells . Next , we found that IFNAR1-signalling impeded the resolution of non-lethal blood-stage infection , which was associated with impaired production of parasite-specific IgM and several IgG sub-classes . Consistent with this , GC B-cell formation , Ig-class switching , plasmablast and Tfh differentiation were all impaired by IFNAR1-signalling . IFNAR1-signalling proceeded via conventional dendritic cells , and acted early by limiting activation , proliferation and ICOS expression by CD4+ T-cells , by restricting the localization of activated CD4+ T-cells adjacent to and within B-cell areas of the spleen , and by simultaneously suppressing Th1 and Tfh responses . Finally , IFNAR1-deficiency accelerated humoral immune responses and parasite control by boosting ICOS-signalling . Thus , we provide evidence of a host innate cytokine response that impedes the onset of humoral immunity during experimental malaria .
Although robust immunity to malaria is difficult to generate in humans through natural infection or vaccination [1 , 2] , it is nonetheless clear that Plasmodium-specific antibodies offer the best known form of immunological protection against blood-stage parasites [3 , 4 , 5 , 6 , 7 , 8] , and may also control liver-infective sporozoites [9 , 10] . Considering that a highly effective malaria vaccine remains elusive , it is important to understand how the onset of humoral immunity to blood-stage Plasmodium parasites is controlled , and whether this process can be boosted , to accelerate or otherwise enhance antibody-mediated immunity to malaria . Mouse models of resolving , non-lethal blood-stage Plasmodium infection are useful for studying humoral immunity to malaria , since mice fail to control parasitemias and display increased disease severity in the absence of parasite-specific antibodies [4 , 11 , 12 , 13 , 14] . However , our understanding of how humoral immune responses develop in these models is currently modest . CD4+ T follicular helper ( Tfh ) cells and their associated cytokines , such as IL-21 , and germinal centre ( GC ) B-cells are critical mediators of humoral immune responses in many systems [15 , 16] , and appear to be similarly important during experimental malaria . For instance , an anti-parasitic role for T-cell-derived IL-21 was recently described during non-lethal Plasmodium chabaudi chabaudi AS ( PcAS ) infection [6] . Other recent studies using non-lethal Plasmodium yoelii 17XNL ( Py17XNL ) infection focused on co-stimulatory markers on CD4+ T-cells , and demonstrated that Programmed cell Death 1 ( PD-1 ) and LAG-3 blockade , or stimulation via OX40 boosted Tfh and GC B-cell responses , with positive effects on parasite control [4 , 17] . With the exception of these reports , in vivo studies of Tfh cells and GC B-cells during experimental malaria remain sparse . Moreover , while these recent reports focused on molecules expressed by CD4+ T-cells themselves , less effort has been directed towards determining whether T-cell extrinsic factors , such as innate or inflammatory cytokines , can control humoral immunity . It is becoming increasingly clear that inducible T-cell co-stimulatory ( ICOS ) receptor on CD4+ T-cells is vital for Tfh cell-dependent humoral immunity across numerous model systems [18 , 19] . ICOS has been implicated in Tfh differentiation via the stabilization of the transcription factor B-cell lymphoma-6 ( Bcl-6 ) [18 , 20 , 21] . Importantly , ICOS supports interactions of emerging Tfh cells with ICOS ligand ( ICOSL ) -expressing bystander B-cells at the periphery of B-cell follicles , a pivotal process for GC B-cell formation and maintenance [22 , 23] . Moreover , ICOS facilitates the expression of CXCR5 , a chemokine receptor essential for Tfh migration into B-cell zones [18 , 24] . Despite fundamental roles for ICOS on CD4+ T-cells in generating and optimizing B-cell responses and antibody production , its role during blood-stage Plasmodium infection was largely unexplored until recently [25] , when Wikenheiser et al . described weaker Tfh and B-cell differentiation in ICOS-deficient mice after the first week of PcAS infection . Furthermore , although T-cell-intrinsic mechanisms have been defined for regulating CD4+ T-cell ICOS levels , for example via Roquin1 and 2 [26 , 27] and microRNA146a [28] , whether or not T-cell extrinsic mechanisms can also modulate ICOS is unclear at present . Type I interferon ( IFN-I ) signalling can amplify adaptive immune responses [29 , 30 , 31] , and drive humoral immunity in vivo , particularly in the context of immunization [32 , 33] , viral infection [34 , 35] and autoimmunity [29 , 36] . Furthermore , IFN-I-signalling was reported to induce Bcl6 , CXCR5 and PD1 expression in naïve CD4+ T cells following TCR stimulation in vitro [37] . IFN-I-related immune responses have also been observed in PBMC from malaria patients [38 , 39 , 40] . Although their functional relevance in humans remains to be established , we recently showed in ex vivo cultures of PBMC from P . falciparum-infected humans , that signalling via IFNAR2 was immunoregulatory [41] . In addition , we showed in experimental mice that IFNAR1-signalling , acting via conventional dendritic cells ( cDCs ) [42] , and employing the canonical IFN-I transcription factor , IRF7 but not IRF3 [43] , suppressed Th1 responses and parasite control during experimental severe malaria caused by P . berghei ANKA ( PbANKA ) . Recent data has suggested that increased Th1 responses might suppress Tfh cells via IFNγ-signalling in experimental malaria [17] , and viral infection [44] . Therefore , the current literature supports a model in which IFNAR1/2-signalling suppresses Th1 responses yet promotes Tfh-dependent humoral immunity during blood-stage Plasmodium infection . The aim of this paper was to determine the effect of IFNAR1-signalling on humoral immune responses during experimental malaria . In this report , we investigated roles for CD4+ T cells , ICOS- and IFNAR1-signalling pathways in the development of humoral immune responses during blood-stage Plasmodium infection . We confirmed crucial roles for CD4+ T-cells and ICOS-signalling in controlling B-cell responses and anti-parasitic immunity . We showed that IFNAR1-signalling obstructed parasite control and antibody production , which was associated with regulation of numerous aspects of the humoral immune response including GC B-cell and plasmablast generation . In particular , IFNAR1-signalling acted early to limit proliferation and localization of activated CD4+ T-cells adjacent to and within B-cell follicles in the spleen . Finally , IFNAR1-deficiency boosted humoral immune responses and improved parasite control in an ICOS-dependent manner . Thus , we describe here the restrictive effect of an innate cytokine-signalling pathway on antibody-mediated immunity during experimental blood-stage malaria .
CD4+ T-cells are critical for control and resolution of blood-stage Plasmodium infection [4 , 11 , 45] , a phenomenon we first confirmed in Py17XNL-infected WT mice depleted of CD4+ cells ( Fig 1A & 1B ) . Despite this , to our knowledge there remained no direct evidence that CD4+ T cells promoted B-cell responses during experimental malaria . To examine this , wild-type ( WT ) mice depleted of CD4+ cells or given control IgG , were infected with Py17XNL and examined for resulting splenic GC B-cell and plasmablast responses ( Fig 1C ) . By day 10 post-infection ( p . i ) , a timepoint just before the CD4-depleted mice began to succumb to infection , plasmablast differentiation was 80% lower in CD4-depleted mice compared to infected controls ( Fig 1D ) , and GC B-cell formation ( Fig 1E ) was almost abrogated ( 95% reduction compared to controls ) , with a similar , substantial impairment in Ig-class switching ( Fig 1F ) . Together , these data formally demonstrated that GC B-cell and plasmablast generation was highly dependent upon CD4+ T-cells . While previous studies in mice and humans demonstrated that ICOS expressed on CD4+ T- cells was critical for effective humoral responses [18 , 19 , 23 , 46 , 47] , until recently , no such studies had been performed during Plasmodium infection [25] . Therefore , we first examined ICOS expression by CD4+ T-cells during Py17XNL infection ( Fig 2A ) , revealing significant up-regulation by 5–7 days p . i . ( Fig 2B ) , which progressively increased over the following 7–9 days . To examine a possible functional role for ICOS in the development of humoral immunity , WT mice were treated with α-ICOSL-blocking antibody ( α-ICOSL ) during Py17XNL-infection . Consistent with other experimental models [18 , 19 , 48] , ICOSL-blockade impaired GC B-cell formation ( Fig 2C ) , Ig-class switching ( Fig 2D ) , Tfh differentiation ( Fig 2E ) , and early production by day 16 p . i . of parasite-specific total IgG and IgG2b , but not IgM or IgG3 in the serum ( Fig 2F ) . Day 16 p . i . was an important timepoint during Py17XNL infection because it marked the point at which the rate of increase in parasitemia began to slow in WT mice , thus indicating the beginning of the resolution phase of infection . Finally , ICOSL-blockade over the first three weeks of infection exacerbated parasitemias and delayed resolution of infection ( Fig 2G ) . Taken together , these data indicated that ICOS-signalling promoted CD4+ T-cell dependent humoral immune responses and parasite control during Py17XNL-infection . We next examined the impact of IFNAR1-signalling on parasite control and humoral immune responses during Py17XNL-infection . Ifnar1-/- mice displayed similar initial parasitemias compared to infected WT controls for the first two weeks of infection , but thereafter exhibited faster control of blood-stage parasites than WT controls ( Fig 3A ) . Similar effects were also observed during PcAS-infection ( S1A Fig ) . Next , we noted increased parasite-specific IgM and total IgG levels in the sera of Py17XNL-infected Ifnar1-/- mice compared to WT controls at day 16 p . i . ( Fig 3B & 3C ) . More specifically , levels of parasite-specific IgG1 , IgG2b , IgG2c and IgG3 were all higher in Ifnar1-/- mice compared to WT controls ( Fig 3B & 3C ) . Next , we noted that GC B-cell ( Fig 3D ) and Ig-switched B-cell generation ( Fig 3E ) was limited by IFNAR1-signalling at day 16 p . i . In addition , IFNAR1-signalling impaired plasmablast formation at day 6 p . i . ( Fig 3F ) . We also observed similar regulation of plasmablasts and emerging GC B-cells during PcAS-infection ( S1B & S1C Fig ) . Finally , we explored the longer-term effect of IFNAR1-signalling on parasite-specific antibody production . At day 25 p . i . with Py17XNL , Ifnar1-/- mice maintained higher serum IgG levels , including IgG2b and IgG3 , but not IgM , compared to WT controls ( Fig 3G ) . By day 40 p . i . , total IgG , IgG2b and IgG3 concentrations had risen further to similar levels in WT and Ifnar1-/- mice , while IgM levels had dropped , again with no differences between groups ( Fig 3G ) . Taken together , our data indicated that IFNAR1-signalling delayed parasite control , B-cell responses and the onset of antibody production during blood-stage Plasmodium infection . Given that GC B-cell and plasmablast formation was dependent on CD4+ T cells ( Fig 1D–1F ) , and likewise that Tfh differentiation depended on the presence of B-cells ( S2 Fig ) , we next examined the impact of IFNAR1-signalling on splenic CD4+ T-cell responses ( Fig 4 ) . Compared to infected WT controls , Ifnar1-/- mice displayed increased Tfh proportions and numbers during Py17XNL infection ( Fig 4A ) , and increased proportions during PcAS infection ( Fig 4B ) . We chose to assess emerging Tfh cells around day 6–8 p . i . during PcAS infection , since our previous work had suggested that strong CD4+ T helper cell responses were detectable around this time [43 , 49] . Enhanced Tfh differentiation was associated in both models with a substantial increase by 6 days p . i . , in ICOS expression by splenic CD4+ T-cells ( Fig 5A & 5B ) . ICOS expression by CD4+ T cells facilitates interaction with ICOSL-expressing B-cells at the periphery of B-cell zones , and sustains Tfh cells within B-cell follicles [19 , 22 , 23] . Therefore , we examined the impact of IFNAR1-signalling on CD4+ T-cell localization within the spleen ( Fig 5C ) . At day 5 p . i . we observed higher densities of ICOS+ T-cells at the T/B border in Ifnar1-/- mice compared to WT controls ( Fig 5C ) , with a similar although more modest effect within B-cell follicles themselves ( Fig 5C ) . Therefore , our data suggested that IFNAR1-signalling limited Tfh cell differentiation and the localization of activated CD4+ T-cells adjacent to and within B-cell follicles . To rule out possible developmental or immune homeostatic defects in Ifnar1-/- mice accounting for phenomena described above , we next treated PcAS-infected WT mice with an IFNAR1-blocking antibody [42 , 43] . IFNAR1-blockade enhanced ICOS expression on CD4+ T-cells ( Fig 6A ) , boosted Tfh cell responses but not Bcl-6 expression ( Fig 6B ) , and increased early plasmablast and GC B-cell formation ( Fig 6C & 6D ) . Together , these data supported the hypothesis that IFNAR1-signalling regulated ICOS expression by CD4+ T-cells , limited Tfh differentiation , and restricted splenic B-cell responses . Elevated IFNγ responses during blood-stage malaria have been associated with impaired humoral immune responses [17 , 50 , 51] . Since IFNAR1-signalling had suppressed IFNγ production by Th1 cells in our previous reports using PcAS and PbANKA infection [42 , 43] , we next determined the concurrent effect of IFNAR1-signalling on Th1 and Tfh differentiation . On day 6 p . i . with PcAS , as expected , Ifnar1-/- mice exhibited increased Th1 differentiation compared to WT controls [43] ( Fig 7A ) . Importantly , emerging Tfh responses at day 6 p . i . were also higher in Ifnar1-/- mice compared to WT controls ( Fig 7B ) . This indicated that IFNAR1-signalling had simultaneously limited both Th1 and Tfh cell formation , rather than skewed CD4+ T cell responses to either helper subset . Moreover , IFNAR1-mediated regulation of ICOS expression in CD4+ T cells was observed in both the Th1 and emerging Tfh compartments during PcAS infection ( Fig 7C ) ; and IFNAR1-signalling also reduced ICOS+ Tfh cell numbers during Py17XNL infection ( Fig 7D ) . Next , given the simultaneous effect of IFNAR1-signalling on Th1 and Tfh differentiation , we examined whether IFNAR1-signalling exerted a generalized effect on CD4+ T-cells . At day 6 p . i . during PcAS infection , the proportion of CD4+ T-cells that were proliferating , as assessed by Ki-67-staining ( Fig 7E ) , was modestly higher in Ifnar1-/- mice compared to WT controls . Similarly , in α-IFNAR1-treated Py17XNL-infected mice at day 6 p . i . , CD4+ T-cells were more proliferative compared to isotype-treated controls ( Fig 7F ) . We also examined the effect of IFNAR1-signalling on CD4+ T-cell activation using the markers CD11a and CD49d [4 , 41] . At day 8 p . i . with PcAS , the proportion of CD4+ T-cells co-expressing CD11a and CD49d was modestly higher in Ifnar1-/- mice compared to WT controls , although absolute numbers in the spleen were not different ( S3A Fig ) . We also studied Py17XNL-infected mice at day 6 p . i . after treatment with α-Ifnar1 . We noted again that CD4+ T-cells were more activated in α-Ifnar1-treated mice compared to isotype-treated controls ( S3B Fig ) . Taken together , our data suggested that IFNAR1-signalling limited early CD4+ T-cell activation and proliferation . Finally , we observed no substantial differences in the cellularity of the spleen , or bulk CD4+ T cell or B-cell numbers , in WT versus Ifnar1-/- mice during PcAS infection ( Fig 7G ) , Py17XNL infection ( Fig 7H ) , or in un-infected mice ( S3C Fig ) . These data support the idea that any increases in Th1 or Tfh cells observed in our studies were not due to generalized increases in spleen cellularity , but instead were associated with specific regulation of CD4+ T cell activation and proliferation . Taken together , our data revealed that IFNAR1-signalling simultaneously regulated Th1 and Tfh cell formation , which was associated with restricted CD4+ T-cell activation , proliferation and ICOS expression . We next determined cell types in which IFNAR1-signalling had occurred , and focused on cDCs given our previous work using lethal PbANKA infection [42] . We generated mixed bone marrow chimeric mice as before [42] , which harboured equal proportions of WT and Ifnar1-/- splenic cDCs ( Fig 8A ) . These mice were infected with Py17XNL , and expression of co-stimulatory molecules was assessed at the peak of the cDC response ( at 2 days p . i . ) on splenic WT and Ifnar1-/- cDC subsets ( Fig 8B ) . Up-regulation of CD86 in particular , which we previously found was dependent on IFNAR1-signalling in cDC [42] , was again substantially impaired in Ifnar1-/- CD8+ and CD8- cDC subsets compared to WT cDCs ( Fig 8C ) . A similar pattern of expression was seen for PD-L1 , while we observed minimal changes in ICOSL expression ( Fig 8C ) . Interestingly , we also observed cDC subset-specific effects , with PD-L2 restrained by IFNAR1-signalling in CD8- cDC subsets , and CD40 expression mediated by IFNAR1-signalling in CD8+ cDCs ( Fig 8C ) . Similar observations were also made for CD86 , CD40 and ICOSL during PcAS infection ( S4 Fig ) . Taken together , our data demonstrated that similar to lethal PbANKA infection [42] , IFNAR1-signalling in splenic cDC subsets influenced their upregulation of co-stimulatory molecules during non-lethal blood-stage Plasmodium infection . To study the possibility of IFNAR1-signalling in B-cells , we next examined early plasmablast responses in WT:Ifnar1-/- mixed bone marrow chimeric mice during PcAS infection . At day 7 p . i . , there was no consistent increase in plasmablast formation by Ifnar1-/- cells compared to WT counterparts ( Fig 8D ) , suggesting that IFNAR1-signalling in B-cells played no major role in regulating plasmablast differentiation . Moreover , in these same mice equal proportions of WT and Ifnar1-/- CD4+ T cells upregulated ICOS and CXCR5 ( Fig 8E ) . This suggested that restriction of ICOS expression in emerging Tfh cells was not mediated by IFNAR1-signalling directly to CD4+ T cells . Finally , we explored in vivo the effect of IFNAR1-signalling in cDC on emerging humoral responses in the spleen . CD11cCre+/- ifnar1f/f mice and CD11cCre-/- ifnar1f/f littermate controls were infected with Py17XNL , and splenic Tfh and GC B-cell differentiation assessed 6 days p . i . ( Fig 8F and 8G ) . We noted that IFNAR1-signalling in CD11chi cells limited both Tfh ( Fig 8F ) and early GC B-cell ( Fig 8G ) differentiation . Taken together , our data strongly suggested that regulation of humoral immune responses by IFNAR1-signalling was mediated through cDCs , not B-cells or CD4+ T cells . Finally , we sought to explore molecular mechanisms by which IFNAR1-deficiency accelerated the onset of humoral immunity , and hypothesized a role for ICOS-signalling . To test this , we adopted a recent approach in which α-ICOSL blocking antibody was used to reduce ICOS-signalling in mice exhibiting higher than WT levels of ICOS [28] . We treated PcAS-infected Ifnar1-/- and WT mice with a moderate dose of α-ICOSL blocking antibody ( 100μg ) and at day 8 p . i . examined antibody production and parasitemia . We saw no effect on early parasite-specific IgM production or parasitemias in WT mice ( Fig 9A & 9B ) , with some impairment of emerging parasite-specific IgG responses ( Fig 9A & 9B ) . In contrast , ICOS-signalling blockade in Ifnar1-/- mice abrogated any improvements in parasite-specific IgM or IgG production ( Fig 9A ) , and importantly , impaired parasite control compared to control-treated Ifnar1-/- mice ( Fig 9B ) . Consistent with these observations , we also noted that enhanced Tfh cell and GC B-cell formation in Ifnar1-/- mice was strongly dependent on ICOS-signalling ( Fig 9C & 9D ) . Finally , ICOS blockade in Ifnar1-/- mice significantly reduced serum IFNγ levels compared to control-treated Ifnar1-/- mice ( S5 Fig ) , consistent with enhanced ICOS-signalling during IFNAR1-deficiency being supportive of Th1 differentiation . Taken together , our data support a model in which IFNAR1-signalling limited humoral immune responses and parasite control by regulating ICOS-signalling .
Here , using two mouse models of non-lethal blood-stage malaria , we have provided evidence that the onset of protective humoral immunity to Plasmodium can be influenced by an innate cytokine signalling pathway , in this case Type I Interferon-signalling via IFNAR1 . Therefore , we have demonstrated for the first time that innate immune cytokines can limit the onset of antibody production and B-cell responses to Plasmodium . Moreover , we found that release from IFNAR1-mediated immune-regulation enhanced humoral immune responses and parasite control in a manner dependent upon ICOS-signalling . Although parasite-specific antibodies can control blood-stage Plasmodium parasite numbers in vivo , protective humoral immunity can take months or years to develop in humans . Reasons for this have focused on parasite mechanisms such as antigenic variation , rather than possible sub-optimal host immune responses . IFN-I responses have been well documented in malaria patients [38 , 52 , 53] . Polymorphisms in the Ifnar1 gene have been associated with reduced risk of severe malaria [38 , 52] . Although the location of polymorphisms did not indicate the direction of their effect on IFNAR1-signalling , the implication was that changes in IFNAR1-signalling could mediate improved parasite control . Indeed , our recent work using C57BL/6J mice suggested that IFNAR1-signalling via IRF7 but not IRF3 limited parasite control during PcAS infection [42 , 43] , although other recent work , using a different route of infection [54] or a different genetic background [55] suggested more modest roles for IFN-I-signalling in mice . These different outcomes suggest possible context-dependent effects for IFN-I-signalling during PcAS infection . It is likely that mouse models of malaria will be informative for studies of cytokine-mediated effects on humoral immunity , as recently epitomized in studies of T cell-derived IL-21 [6] . These reports and our new data complement studies of T-cell co-stimulatory molecules , such as PD-1 and LAG-3 , and strengthen the idea that multiple molecular targets could be harnessed to boost humoral immunity to malaria [4 , 17] . Whether such strategies are more applicable to natural exposure events or vaccination scenarios remains to be studied . In addition , while we found no overt evidence of increased immune-pathology in Ifnar1-/- or α-Ifnar1 treated mice , it will be important to better test whether manipulation of IFNAR1-signalling triggers unwanted adverse events , as previously reported for IL-10- or IL-27-deficiency [56 , 57] . Much of our current understanding of cytokine-mediated control of humoral immunity derives from studies of viral infection or experimental immunization in mice [33 , 58 , 59 , 60 , 61] . However , it remains difficult to infer from these studies how cytokine-signalling impacts upon antibody responses during parasitic infection . In a recent report , Perez-Mazliah et al . , demonstrated a pivotal role for T cell-derived IL-21 in mediating GC B-cell responses and IgG class switching but not for generating Tfh cells during blood-stage Plasmodium infection [6] . Our demonstration that IFNAR1-signalling restricts the onset of humoral immunity to malaria is the first description of cytokine-mediated suppression of Tfh , GC B-cell and plasmablast formation during parasitic infection . More generally , while the majority of viral and experimental immunization studies highlight a positive role for IFNAR1-signalling in driving humoral immunity , our data emphasizes that depending on the experimental context , IFNAR1-signalling can also limit humoral immune responses . Our previous studies using a lethal model of malaria demonstrated that IFNAR1- signalling occurred via cDC , which resulted in potent suppression of Th1-immunity , and was associated with effects on PDL1 , PDL2 and IL-10 expression by cDC subsets [42] . However , the lethal model was not suitable for studying humoral immunity , since mice became moribund within the first week of infection . Here , using non-lethal models , we similarly revealed that IFNAR1-signalling proceeding via cDCs , but not B- or T-cells , regulated the generation of Tfh and GC B-cell responses . Given that cDCs are critical for early Tfh differentiation and initiation of humoral immune responses [18 , 62 , 63] , we propose that IFNAR1-signalling within splenic cDC regulates CD4+ T-cell proliferation , ICOS expression , and Th1 and Tfh differentiation . One question that remains to be answered is how the interaction between cDC and CD4+ T cells is altered at a molecular level by IFNAR1-signalling . This work and our previous study using PbANKA infection [42] both revealed multiple changes in the co-stimulatory landscape on the surface of cDC , including a substantial shift in the ratio of PDL1:PDL2 expression on CD8- cDC . Given a recent report that PDL2-signalling can compete against the regulatory effects of PDL1 , and indeed , can protect against experimental malaria [64] , we speculate that IFNAR1-signalling in cDC regulates CD4+ T cell activation by favouring PDL1-signalling over protective PDL2 signals . It was recently reported during LCMV infection that IFN-I promoted Th1 responses , which then suppressed Tfh and GC B-cell responses via IFNγ [44] . A more recent report using a mouse model of malaria demonstrated that therapeutic release from PD-1 exhaustion , coupled with stimulation via OX40 dramatically increased IFNγ production by Th1 cells , which destabilized Bcl-6 in established Tfh cells and resulted in defective humoral immune responses [17] . Elsewhere , circulating ‘Th1-like’ Tfh cells were associated with impaired humoral responses in children living in malaria endemic areas [50] . Most recently , Ryg-Cornejo et al . , implicated a combined effect of IFNγ and TNF in driving sub-optimal humoral responses during severe malaria infections in mice [51] . Together , these data suggest that Th1 responses might interfere with Tfh responses . However , in our studies , we found that deficiency in IFNAR1-signalling triggered a concurrent increase in both Th1 and Tfh responses . This apparent discrepancy between our work and recent studies could be explained by the differential kinetics of the elevated Th1 responses in our respective studies . For instance , OX40- and PD1-targeted therapy was initiated from the second week of infection , a period of time after which Th1/Tfh priming would likely have occurred [17] . In our studies , elevated Th1 responses , as a result of IFNAR1-deficiency , occurred relatively transiently within the first week of infection . Therefore , we speculate that elevated Th1 responses in IFNAR1-deficient mice did not destabilize Tfh responses because they occurred early and were not prolonged . Data from this and our previous studies [42 , 43 , 49] support a model in which abrogation of IFNAR1-signalling has the dual effect of boosting Th1 responses and antibody-production . These effects associated with improved control of Py17XNL , PcAS and PbANKA parasites; yet crucially we have not yet demonstrated a causal link between IFNAR1-deficiency and improved antibody-mediated parasite control . Nevertheless , we propose that depending upon host genetic background and that of the infecting Plasmodium species , IFNAR1-signalling can obstruct parasite control via a number of mechanisms including the regulation of Th1 cells and/or antibody production . Numerous studies across a range of experimental systems have established a pivotal role for ICOS signalling in CD4+ T-cells in mediating T-cell dependent humoral immunity , via effects on Tfh generation , maintenance and trafficking [18 , 19 , 23 , 65] . The importance of ICOS-signalling is further highlighted by the existence of multiple layers of regulation within the T-cell for limiting its expression , for example via Roquin 1 , Roquin 2 and microRNA146a [26 , 27 , 28] . To date , however , evidence of T-cell extrinsic mechanisms for controlling ICOS levels on CD4+ T-cells has been limited . Our data reveals the existence of a cytokine signalling pathway , mediated by IFNAR1 which serves to limit the level of ICOS on CD4+ T-cells . Currently , the mechanism by which ICOS levels are modulated in our models by IFNAR1-signalling is unclear , but could theoretically involve Roquin1 , Roquin 2 or microRNA146a . Although we observed substantial early ICOS expression by activated CD4+ T-cells in our models , we noted minimal change in ICOSL levels on cDC ( Fig 8C ) . This might suggest that boosting ICOS expression by CD4+ T-cells did not encourage further interaction with splenic cDC . Instead , given that IFNAR1-deficiency increased the frequency of ICOS+ T-cells close to and within B-cell follicles , which were themselves essential for supporting Tfh responses ( S2 Fig ) , and since ICOSL-expressing B-cells are located at the periphery of B-cell follicles [22] , we propose that IFNAR1-signalling limits ICOS-mediated positioning of emerging Tfh cells adjacent to and within B-cell follicles during Plasmodium infection . However , further experiments will be required to examine the effect of ICOS-signalling on CD4+ T-cell trafficking in the spleen during experimental malaria . In this study , we discovered that IFNAR1-deficiency accelerated early production of parasite-specific IgM and IgG . If such mechanisms could be induced in humans , this might improve control of parasite numbers and prevention of clinical malaria . However , the duration of this effect would require scrutiny , since in our mouse models , antibody levels normalized between Ifnar1-/- and WT mice by the seventh week of infection . Furthermore , whether acceleration in early antibody production would increase the rate of acquisition of immunity to clinical malaria is also unclear , particularly given the phenomenon of antigenic variation . However , we speculate that if subsequent infections were sufficiently similar from an antigenic perspective to previously encountered parasites , short-term elevations in parasite-specific antibodies could be beneficial . Finally , although we focused on studying the magnitude of parasite-specific antibody responses within a given antibody sub-class , neither antibody affinity nor avidity was examined . Therefore , further experimentation will be needed to determine whether beneficial changes in antibody affinity and avidity can be brought about via cytokine modulation . In summary , we have demonstrated here that early cytokine-signalling during infection influences parasite-specific antibody production , and associated GC B-cell , plasmablast and Tfh differentiation in two models of non-lethal blood-stage malaria . Our work suggests that antibody-mediated immunity to malaria might be improved by targeting cytokine-signalling pathways , particularly in the context of natural infection .
All animal procedures were approved by the QIMR Berghofer Medical Research Institute Animal Ethics Committee , under approval numbers A02-633M and A1503-601M , in accordance with the “Australian Code of Practice for the Care and Use of Animals for Scientific Purposes” ( Australian National Health and Medical Research Council ) . Female C57BL/6J and congenic CD45 . 1+ C57BL/6J mice ( 6–12 weeks old ) were purchased from Australian Resource Centre ( Canning Vale , Western Australia ) and maintained under conventional conditions . C57BL/6J Ifnar1-/- mice were maintained in-house at QIMR Berghofer Medical Research Institute . Mixed bone marrow ( BM ) chimeric mice were prepared as previously described [42] . Briefly , 2x106 fresh syngeneic BM cells from femurs of CD45 . 1+ wild type and CD45 . 2+ Ifnar1-/- mice , mixed at a 50:50 ratio were intravenously ( i . v . ) transferred into lethally irradiated [11Gy ( 137Cs source ) ] C57BL/6J Rag1-/- recipient mice . These mice were then treated for 14 days with Baytril ( Provet ) in drinking water . Engraftment was assessed after 8–12 weeks by flow cytometry . BM Chimeric mice were infected 12 weeks after bone marrow transplantation . Non-lethal Plasmodium yoelii 17XNL ( Py17XNL ) and Plasmodium chaubadi chaubadi AS ( PcAS ) parasites were used following one in vivo passage in wild type C57BL/6J mice . Using parasitized red blood cells ( pRBC ) that were obtained from frozen/thawed stabilates , mice were infected i . v . with either 104 pRBCs ( Py17XNL ) or 105 pRBCs ( PcAS ) . Blood parasitemia was measured in Diff-Quick ( Lab Aids , Narrabeen , NSW , Australia ) or giemsa-stained thin blood smears obtained from tail bleeds . Alternatively , a modified protocol of a previously established flow cytometric method was employed to measure parasitemia more rapidly [66] . Briefly , a single drop of blood , from a tail bleed or cardiac puncture , was diluted and mixed in 250μl RPMI containing 5U/ml heparin sulphate . Diluted blood was simultaneously stained with Syto84 ( 5μM; Life Technologies ) to detect RNA/DNA , and Hoechst33342 ( 10μg/ml; Sigma ) to detect DNA , for 30 minutes , in the dark at room temperature . Staining was quenched with 10x volume of ice cold RPMI , and samples were immediately analysed by flow cytometry , using a BD FACSCantoII analyser ( BD Biosciences ) and FlowJo software ( Treestar , CA , USA ) . pRBC were readily detected as being Hoechst33342+ Syto84+ , with white blood cells excluded on the basis of size , granularity and much higher Hoechst33342/Syto84 staining compared to pRBC . Crude antigen extract from Py17XNL or PcAS-infected RBC was prepared using an adapted version of a previously described protocol [67 , 68] . Briefly , mice were infected with Py17XNL or PcAS as described above . When parasitemias reached 20–30% , blood was collected by cardiac puncture into heparinized tubes . RBCs were washed once in RPMI at 1200rpm for seven minutes at room temperature , and then lysed using ultrapure water followed by four washes in ice-cold PBS at 16 , 000xg for 25 minutes at 4°C , as well as three cycles of freezing ( two hours at -80°C ) and thawing ( 30 minutes at room temperature ) . Extracts were also processed from RBCs of uninfected C57BL/6J mice , for use as negative controls in ELISA . The concentration of proteins in the purified extracts was determined by Bradford assay ( Thermo Scientific ) . All extracts were stored at -80°C until use . Costar EIA/RIA 96-well flat bottom plates were coated overnight at 4°C with 2 . 5μg of soluble antigen/ml in bicarbonate coating buffer ( pH9 . 6 ) . Wells were washed three times ( all washes in 0 . 005% Tween in PBS ) and then blocked for 1hr at 37°C with 1% BSA in PBS . Wells were washed three times , 100ul of sera diluted 1/400 , 1/800 , 1/1600 or 1/3200 was added and incubated for 1hr at 37°C . Following six washes , wells were incubated in the dark with biotinylated anti-IgM , total IgG , IgG1 , IgG2b and IgG3 ( Jackson ImmunoResearch ) for 1hr at room temperature . Unbound antibodies were washed off ( six times ) prior to incubating wells in the dark with streptavidin HRP ( BD pharmagen ) for 30 minutes at room temperature . Wells were washed six times prior to development ( 100μl , OPD; Sigma-Aldrich ) for five minutes in the dark before termination with an equal volume of 1M HCl . Absorbance was determined at 492nm using a Biotek synergy H4 ELISA plate reader ( Biotek , USA ) . Data were analysed using Gen5 software ( version 2 ) and GraphPad Prism ( version 6 ) . Spleen mononuclear cells were prepared as previously described [69] . For studies of cDCs , spleens were treated with deoxyribonuclease I ( 0 . 5mg/ml; Worthington Biochemical ) and collagenase type 4 ( 1mg/ml; Worthington Biochemical ) for 25 minutes at room temperature , in order to ensure maximal recovery of splenic cDCs . Fluorescently conjugated monoclonal antibodies , anti-mouse B220-Alexa Fluor 700 ( RA3-6B2 ) , B220-Pacific blue ( RA3-6B2 ) , CD19-FiTC ( 6D5 ) , CD138-BV605 ( 281–2 ) , IgD-APCCy7 ( 11-26c . 2a ) , IgM-PECy7 ( RMM-1 ) , TCRβ-Alexa Fluor 700 ( H57-597 ) , TCRβ-APC/Cy7 ( H57-597 ) , CD4-BV605 ( RM4-5 ) , IFNγ-BV421 ( XMG1 . 2 ) , ICOS-PE ( 7E . 17G9 ) , Streptavidin-PE/Cy7 , CD45 . 1-FiTC ( A20 ) , CD45 . 2-Alexa Fluor 700 ( 104 ) , CD11c-APC ( N418 ) , MHCII-Pacific blue ( M5/114 . 15 . 2 ) , CD8α-PE/Cy7 ( 53–6 . 7 ) , CD40-PE ( 1C10 ) , CD80-PE ( 16-10A1 ) , CD86-PE ( GL1 ) , ICOSL-PE ( B7-RP1 ) , PDL1-PE ( MIH5 ) , PDL2-PE ( TY25 ) , CD49d-Biotin ( R1-2 ) , CD11a-FiTC ( M17/4 ) , Ki-67-PE ( 16A8 ) and Zombie Aqua fixable viability dye were purchased from Biolegend ( San Diego , CA ) . Anti-mouse CD95/Fas-BV421 ( Jo2 ) , CXCR5-biotin ( 2G8 ) , and Bcl6-PerCP/Cy5 . 5 ( K112-91 ) were purchased from BD Biosciences ( Franklin Lakes , NJ ) . Anti-mouse T-bet-APC ( eBio4B10 ) , GL-7-APC ( GL-7 ) and PD1-APC/Cy7 ( J43 ) were purchased from eBioscience . Cell surface and intracellular IFNγ , T-bet and Bcl6 staining was performed as previously described [69 , 70] . Anti-CD4 depleting monoclonal antibody ( clone GK5 . 1 ) and its isotype control were administered in 0 . 1mg doses , via intravenous ( i . v . ) injection in 200μl 0 . 9% NaCl ( Baxter ) one day before infection . CD4+ T-cell depletion was confirmed in PBMC over the first four days of infection , and absolute numbers subsequently remained >99% depleted in the spleen at day 10 p . i . compared to isotype-treated controls . For ICOSL blockade in Fig 2A–2F , α-ICOSL ( clone HK5 . 3 , BioXCell ) and its isotype control ( IgG2a , clone 2A3 , BioXCell ) were administered in 0 . 2mg doses , via i . v . injection in 200μl 0 . 9% NaCl ( Baxter ) one day prior to infection , and then every three days for up to 15 days p . i . ( and for up to 21 days in Fig 2G ) . For ICOSL blockade experiments in Fig 9 and S5 Fig , α-ICOSL ( clone MIL5733 ) and its isotype control ( IgG2a , clone 1D10 ) were generated in-house and administered in 0 . 1mg doses , via i . v . injection in 200μl 0 . 9% NaCl ( Baxter ) one day prior to infection , and then every two days for up to 6 days p . i . ( In Fig 9 , the 100μg dose was employed since preliminary experiments indicated that 200μg reduced Tfh cell responses by ~65% , 100μg reduced Tfh responses by 50% , and 25μg had no effect by day 8 p . i . with PcAS ) . For B-cell depletion , anti-CD20 ( clone 5D2 ) or its isotype control antibody were produced and kindly provided by Genentech and administered in a single 0 . 5mg dose via i . p . injection in 200μl 0 . 9% NaCl ( Baxter ) , five days prior to infection . For Ifnar1 blockade , α-Ifnar1 blocking monoclonal antibody ( clone MAR1-5A3; Leinco Technologies Inc ) and its isotype control mAb were administered in 0 . 1mg doses , via i . p . injection in 200μl 0 . 9% NaCl ( Baxter ) on the day of infection , and subsequently on days 2 , 4 and 6 p . i . Confocal microscopy was performed on 10–20 μm frozen spleen sections as previously described [71 , 72] . Briefly , tissues from infected and un-infected mouse spleens were snap frozen in embedding optimal cutting temperature ( OCT ) medium ( Sakura ) and stored at -80°C until use . Sections were fixed in ice-cold acetone for 10 minutes prior to labelling with antibodies against CD3-Biotin ( clone-17A2 ) , B220-PE ( clone-RA3-6B2 ) and ICOS-APC ( clone-C398 . 4A ) . Anti-CD3 was detected by streptavidin conjugated to Alexa Fluor 594 . All antibodies were obtained from Biolegend ( San Diego , CA ) . DAPI was used to aid visualization of white pulp areas . Samples were imaged on a Zeiss 780-NLO laser-scanning confocal microscope ( Carl Zeiss Microimaging ) and data analysed using Imaris image analysis software , version 8 . 1 . 2 ( Bitplane ) . Cells were identified using the spots function in Imaris , with thresholds <10μm and intensities <150 . T-B borders were defined by the region between CD3+ cells closest to the B cell follicle and B220+ cells furthest into the T-cell zone . All objects were manually inspected for accuracy before data were plotted and analyzed in GraphPad Prism ( version 6 ) Comparisons between two groups were performed using non-parametric Mann-Whitney ( unpaired datasets ) or Wilcoxon ( paired datasets ) tests . Where depicted , one-way or two-way ANOVA and Tukey’s post-test were employed for multiple comparisons among three or more groups . p< 0 . 05 was considered significant ( p<0 . 05 = *; p<0 . 01 = **; p<0 . 001 = ***; P<0 . 0001 = **** ) . Survival graphs were assessed using Log-rank ( Mantel-Cox ) tests . Graphs depict mean values ± SEM , except where individual mouse data points are depicted , in which case median values are shown . All statistical analyses were performed using GraphPad Prism v6 or v7 software . | Plasmodium parasites cause malaria by invading , replicating within , and rupturing out of red blood cells . Natural immunity to malaria , which depends on generating Plasmodium-specific antibodies , often takes years to develop . Explanations for this focus on antigenic variation by the parasite , but consider less whether antibody responses themselves may be sub-optimal . Surprisingly little is known about how Plasmodium-specific antibody responses are generated in the host , and whether these can be enhanced . Using mouse models , we found that cytokine-signalling via the receptor IFNAR1 delayed the production of Plasmodium-specific antibody responses . IFNAR1-signalling hindered the resolution of infection , and acted early via conventional dendritic cells to restrict CD4+ T-cell activation and their interactions with B-cells . Thus , we reveal that an innate cytokine response , which occurs during blood-stage Plasmodium infection in humans , obstructs the onset of antibody–mediated immunity during experimental malaria . | [
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] | 2016 | IFNAR1-Signalling Obstructs ICOS-mediated Humoral Immunity during Non-lethal Blood-Stage Plasmodium Infection |
Most human somatic cells express insufficient levels of telomerase , which can result in telomere shortening and eventually senescence , both of which are hallmarks of ageing . Homology-directed repair ( HDR ) is important for maintaining proper telomere function in yeast and mammals . In Saccharomyces cerevisiae , Rad52 is required for almost all HDR mechanisms , and telomerase-null cells senesce faster in the absence of Rad52 . However , its role in preventing accelerated senescence has been unclear . In this study , we make use of rad52 separation-of-function mutants to find that multiple Rad52-mediated HDR mechanisms are required to delay senescence , including break-induced replication and sister chromatid recombination . In addition , we show that misregulation of histone 3 lysine 56 acetylation , which is known to be defective in sister chromatid recombination , also causes accelerated senescence . We propose a model where Rad52 is needed to repair telomere attrition-induced replication stress .
Telomeres , nucleoprotein structures located at the ends of linear chromosomes , prevent natural chromosome ends from being recognized as DNA double-strand breaks ( DSBs ) [1] . Due to incomplete DNA replication and nucleolytic degradation , telomeres shorten with each round of replication , which can eventually lead to a growth arrest , known as replicative senescence , or to apoptosis . Telomere shortening can be counteracted by a specialized reverse transcriptase called telomerase , which is composed of a protein catalytic subunit and an RNA subunit [2 , 3] . Telomerase extends telomeres by iterative reverse transcription of a short sequence to the 3′ ends of telomeres , using the RNA subunit as a template [2 , 4 , 5] . Most human somatic cells do not express sufficient telomerase to prevent telomere shortening , which may be a contributing factor towards human ageing . This absence of telomere maintenance may have evolved as a barrier to tumorigenesis ( reviewed in [6] ) . Indeed , cancer cells need to activate a telomere maintenance mechanism , and in approximately 85–90% of cancers , this occurs through the upregulation of telomerase [7] . The remaining 10–15% of cancers employ telomerase-independent , recombination-based mechanisms , collectively termed alternative lengthening of telomeres ( ALT ) [8] . ALT mechanisms were first described in the budding yeast Saccharomyces cerevisiae , where cells using ALT are called “survivors” [9] . There are two main types of survivors: type I and type II . Both types of survivors require the major recombination protein Rad52 and the DNA polymerase δ subunit Pol32 [9 , 10] . Pol32 is essential for break-induced replication ( BIR ) [10] , while Rad52 is important for almost all recombination-related activities , including BIR ( reviewed in [11] ) . Type I survivors also require Rad51 , Rad54 , and Rad57 , and maintain telomeres by amplification of subtelomeric Y′ elements [9 , 12] . Formation of type II survivors , which exhibit amplification of the C1–3A/TG1–3 telomeric repeats , is Rad51-independent , but requires the MRX complex ( consisting of Mre11 , Rad50 and Xrs2 ) , Rad59 , and Sgs1 [12–15] . BIR can be Rad51-dependent or Rad51-independent , suggesting that type I and type II survivors maintain telomeres through Rad51-dependent BIR and Rad51-independent BIR , respectively [16 , 17] . While recombination is clearly important for the maintenance of telomeres in survivors , recombination proteins are also important in pre-senescent cells [18] . Rad52 can be detected at telomeres well before the appearance of survivors [19] . Furthermore , telomerase-negative cells lacking Rad51 , Rad52 , Rad54 , Rad57 , Rad59 , Pol32 , or Sgs1 senesce very rapidly [9 , 14 , 15 , 20–22] . With the exception of Sgs1 , the enhanced senescence does not appear to cause a change in bulk telomere shortening [9 , 12 , 20 , 23] , although rare telomere loss events may be occurring . tlc1Δ sgs1Δ strains fail to resolve recombination intermediates at telomeres in pre-senescent cells , which may explain their accelerated senescence [24] . Rad52 mediates the exchange of RPA for Rad51 on single-stranded DNA to promote Rad51-catalyzed strand invasion [25 , 26] . While this Rad51 pathway , which also requires Rad54 , Rad55 , and Rad57 , is important for the majority of homology-directed repair ( HDR ) , Rad52 also has Rad51-independent functions . These functions involve its DNA annealing activity , which is augmented by Rad59 [27–29] . The Rad51-mediator and the DNA annealing functions of Rad52 are separable . An alanine scan mutation study identified a class of rad52 mutants ( class C mutants ) that can still promote recruitment of Rad51 but is deficient in DNA annealing [30 , 31] . These mutants are defective in repairing DSBs and in sister chromatid recombination ( SCR ) but perform BIR with only slightly reduced efficiency [30 , 32 , 33] . The mechanism by which HDR prevents accelerated senescence has been poorly characterized . This is in part due to the multiple Rad52 subpathways within HDR . Rad51-dependent BIR and Rad51-independent BIR have been previously implicated in delaying senescence [21–23] . In this study , we make use of rad52 class C mutants to show that SCR is also important during senescence . We also demonstrate that proper regulation of the acetylation of lysine 56 of histone 3 ( H3K56 ) is important during replicative senescence , and we propose a model where Rad52 is repairing damage at telomeres in the absence of telomerase .
Previous studies have used telomere sequencing to detect recombination events in senescing S . cerevisiae cells [24 , 34–42] . This assay takes advantage of the fact that yeast telomerase adds imperfect , degenerate repeats [43] . Sequencing multiple copies of the same telomere derived from a clonal population of cells reveals a centromere-proximal region of stable sequence and a distal region with differing degenerate repeats [44 , 45] . The variation in the sequence of the distal region is largely abolished in the absence of telomerase [45] , but rare sequence divergence events can be detected and have been presumed to be caused by recombination [34] . More precisely , since equal SCR generates repair products without changes in DNA sequence , the assay detects sequence divergence due to unequal SCR , intertelomere recombination , or BIR that does not result from perfect alignment with a sister telomere . These recombination events may be directly important in delaying senescence , or they may be a byproduct of other recombination-mediated activities that delay senescence . To determine the nature of these events , we sequenced telomere VI-R from est2Δ strains—EST2 encodes the protein catalytic subunit of telomerase [3]—that are also deleted for either RAD52 , POL32 , or RAD59 . All three of these genes are required for recombination of telomeric repeats in type II survivors [9 , 10 , 12] . In est2Δ cells , 8 . 6% of the telomeres exhibit sequence divergence , similar to what has previously been reported [37] . Surprisingly , even though rad52Δ , pol32Δ , and rad59Δ telomerase-null strains senesce rapidly [9 , 21 , 22] , we find that the divergence events do not decrease in the absence of Rad52 , Pol32 , or Rad59 ( Fig 1 ) , indicating that these events are not involved in the recombination-mediated delay of senescence . In fact , divergence events increase in the absence of Pol32 . pol32Δ rad52Δ double mutants are synthetic lethal [46 , 47] . One interpretation of this genetic interaction is that in the absence of Pol32 , DNA replication is compromised , resulting in damage that is repaired by Rad52-dependent HDR . Indeed , we see elevated levels of Rad52 focus formation in pol32Δ cells ( S1 Fig ) . The increased divergence seen in est2Δ pol32Δ telomere sequences could be due to an increase in Rad52-dependent recombination at telomeres . Consistent with this hypothesis , we observe a further increase in Rad52 focus formation in est2Δ pol32Δ cells . To determine the source of the Rad52-independent divergence events , we performed two controls . First , we took two plasmids with cloned and sequenced telomeres ( one of 166 bp and the other 213 bp in length ) , amplified the telomeres by PCR , re-cloned them into the same vector , and sequenced multiple clones . We found that 3 . 8% of the clones exhibited sequence divergence ( S2 Fig ) . The divergence events can be due to sequence alterations caused during PCR amplification , propagation in bacteria , and/or DNA sequencing . Second , we integrated two telomeres , 166 bp and 230 bp in length , into the URA3 locus in wild-type and rad52Δ strains , which were then clonally propagated for ~30 population doublings . We amplified these internal telomeres by PCR , cloned the PCR products , and sequenced multiple clones . We found that 4 . 2% and 7 . 3% of the clones from wild type and rad52Δ , respectively , exhibit sequence divergence ( S2 Fig ) . The higher percentage in strains lacking Rad52 likely reflects a role for Rad52 in suppressing the accumulation of mutations [48] . While an internally-integrated telomere is not equivalent to a natural telomere , our data suggest that a significant fraction of sequence divergence events at natural telomeres in telomerase-null cells occur because of technical reasons related to amplification , cloning , and/or sequencing of telomeres . Our findings have implications with regard to using this assay to study recombination at telomeres ( see Discussion ) , and show that the function of Rad52 in delaying senescence is unrelated to the sequence divergence events observed in senescing telomerase-negative cells . In addition , our data indicate that any Rad52-mediated HDR events during senescence most likely involves perfectly aligned sister telomeres , which would not alter the sequences of recombining telomeres , and would therefore not be detected using this assay . Although absence of Rad52 does not alter the rate of bulk telomere shortening , truncation of a small number of telomeres may be occurring that results in accelerated senescence . It was previously determined that telomeres less than 125 bp in length are highly unlikely to arise due to the standard end-replication problem [49 , 50] , which shorten telomeres by 3–4 bp per generation in yeast [51] , so such telomeres would mostly likely have undergone a truncation event . To determine whether Rad52 prevents such truncation events , we sequenced telomeres from two wild-type and two rad52Δ telomerase-positive strains , which were derived from the meiosis of a single rad52Δ/RAD52 diploid cell , after ~35 generations of clonal expansion . In telomerase-positive strains , most sequence divergence events are due to telomerase-mediated telomere extension , and not the telomerase- and Rad52-independent divergence events discussed above . The length of the undivergent region of each telomere indicates how short the telomere became before being extended by telomerase . It has previously been shown that telomeres with undivergent regions less than 125 bp in length do occur even in wild-type cells [49] . We confirm this observation and also find no change in the frequency of these truncation events in the absence of Rad52 ( Fig 2 ) . This suggests that Rad52 does not prevent telomere truncation events , although it may have a role in repairing such truncations . Telomerase-negative strains lacking Pol32 have previously been shown to exhibit an accelerated rate of senescence [22] , indicating the importance of BIR during senescence . Thus , the function of Rad52 in preventing accelerated senescence may be to promote repair of truncated telomeres through Pol32-mediated BIR . If the accelerated senescence of an est2Δ rad52Δ mutant is due to the role of Rad52 in BIR , then est2Δ rad52Δ and est2Δ pol32Δ mutants , derived from the same parental diploid , should have similar rates of senescence . Interestingly , we find that est2Δ rad52Δ mutants senesce faster than est2Δ pol32Δ mutants ( Fig 3 ) , indicating that although BIR is important to prevent accelerated senescence , other Rad52-mediated activities are also required . To further dissect the function of Rad52 at telomeres in the absence of telomerase , we used a specific class of rad52 mutants ( class C mutants , specifically rad52-Y66A and rad52-R70A ) that are proficient for mitotic recombination but defective in DNA strand annealing and the repair of DSBs [30–32] . The efficiency of BIR is reduced only 2 . 7-fold in class C mutants [32] , whereas rad51Δ mutants exhibit a ~140-fold reduction using the same assay [16] , suggesting that class C mutants can perform Rad51-dependent BIR . We find that est2Δ rad52-R70A and est2Δ rad52-Y66A double mutants senesce faster than est2Δ single mutants ( Fig 4A , est2Δ vs . est2Δ rad52-R70A , p < 10−6; Fig 4B , est2Δ vs . est2Δ rad52-Y66A , p = 0 . 003; Fig 4C , est2Δ vs . est2Δ rad52-Y66A , p = 0 . 001 ) . Interestingly , survivors generated from est2Δ rad52-R70A and est2Δ rad52-Y66A strains are all type I ( Fig 4D and Fig 5D ) . Since type II survivors grow better than type I survivors , survivors generated from a liquid culture senescence assay , as done here , should all be type II [52] unless the strain in question has a defect in forming type II survivors . In our experiments , 9 out of 9 survivors generated from est2Δ mutants , examined 6 population doublings ( PDs ) after they had recovered from the point of maximum senescence ( for each est2Δ mutant , this means the first time point after the point of maximum senescence in the liquid culture senescence assays ) , were type II . Deleting RAD51 in telomerase-null cells blocks the formation of type I survivors [12] . We find that est2Δ rad51Δ rad52-Y66A triple mutants cannot form any survivors ( Fig 4B ) , supporting that the strand annealing activity of Rad52 is needed to perform Rad51-independent BIR and to form type II survivors . The rad52 class C mutants behave similarly to rad59Δ , which is also defective for Rad51-independent BIR and causes telomerase-negative strains to senesce fast and to be unable to form type II survivors [12 , 17 , 21] . We find that est2Δ rad52-Y66A senesces faster than est2Δ rad59Δ ( p = 0 . 02 ) , and deletion of RAD59 does not enhance the senescence of est2Δ rad52-Y66A ( Fig 4C ) , indicating that rad52-Y66A is epistatic to rad59Δ during senescence . However , rad52-Y66A has a greater effect on senescence than rad59Δ , suggesting that the accelerated senescence of telomerase-null rad52 class C mutants is not solely due to a loss of Rad51-independent BIR . Interestingly , est2Δ rad52-Y66A rad59Δ triple mutants show a defect in the formation of survivors ( Fig 4C and S3 Fig ) . Of the four est2Δ rad52-Y66A rad59Δ followed , three showed a prolonged delay before survivors arose and one never formed survivors at all during the duration of the experiment . This observation implies that , while Rad52 and Rad59 function in the same pathway during senescence , they have nonoverlapping functions with regard to survivor formation , and is consistent with other reports suggesting that Rad59 has Rad52-independent functions [53–55] . Having established that Rad52 has non-BIR-related functions in preventing accelerated senescence , we asked whether Rad52 participates in error-free post-replication repair ( PRR ) at telomeres during senescence . Error-free PRR is thought to utilize the newly synthesized sister chromatid as a template for DNA synthesis to bypass DNA lesions ( reviewed in [56] ) . It was shown that error-free PRR utilizes recombination proteins in the repair of MMS- and UV-induced DNA damage [57] . Rad5 is a key component of the error-free PRR pathway and absence of Rad5 accelerates senescence in a telomerase-negative strain [22] . We analyzed the rate of senescence of est2Δ , est2Δ rad5Δ , est2Δ rad52-Y66A , and est2Δ rad5Δ rad52-Y66A strains ( S4 Fig ) . We find that est2Δ rad5Δ exhibits accelerated senescence ( est2Δ vs . est2Δ rad5Δ , p = 0 . 001 ) , as previously reported , and est2Δ rad52-Y66A senesces faster than est2Δ rad5Δ ( p < 10−6 ) . The est2Δ rad5Δ rad52-Y66A triple mutant senesces the fastest ( est2Δ rad52-Y66A vs . est2Δ rad5Δ rad52-Y66A , p = 0 . 001 ) , appearing to have combined the effects of rad5Δ and rad52-Y66A in an additive manner , suggesting that these mutations affect separate pathways . Consistent with our data , it has been previously reported that rad5Δ rad52Δ mutants exhibit a strong synthetic growth defect that is exacerbated in the absence of telomerase [22] , and that Rad5 and Rad52 have independent functions during the bypass of thymine dimers [58] . These results indicate that Rad52-mediated HDR and Rad5-mediated error-free PRR act in at least partially non-overlapping pathways to prevent accelerated senescence . There are several other possible non-BIR mechanisms through which Rad52 may prevent accelerated senescence , including recombination involving sister chromatids . rad52 class C mutants have been previously reported to be defective in SCR [33] . This study also found that defective regulation of H3K56 acetylation also impairs SCR . H3K56 is acetylated by the histone acetyltransferase Rtt109 [59–61] and deacetylated by the histone deacetylases Hst3 and Hst4 [62 , 63] . Both hyper-acetylation ( e . g . hst3Δ hst4Δ and H3K56Q mutants ) and hypo-acetylation of H3K56 ( e . g . rtt109Δ and H3K56R mutants ) decrease SCR [33] . We hypothesized that defective SCR may explain the senescence and survivor phenotype of est2Δ rad52 class C mutants . If so , then mutants affecting H3K56 acetylation should behave similarly with respect to senescence and survivor formation . Consistent with this idea , both est2Δ hst3Δ hst4Δ and est2Δ rtt109Δ mutants exhibit accelerated senescence ( est2Δ vs . est2Δ hst3Δ hst4Δ , p < 10−6; est2Δ vs . est2Δ rtt109Δ , p = 0 . 02 ) and are defective in type II survivor formation ( Fig 5 ) . However , unlike est2Δ rad52 class C mutants , which do not form any type II survivors , est2Δ hst3Δ hst4Δ and est2Δ rtt109Δ mutants are defective , but still able to form type II survivors . As mentioned above , survivors generated from a liquid culture senescence assay are typically all type II . Two of seven est2Δ hst3Δ hst4Δ survivors ( Fig 5C ) , and five of ten est2Δ rtt109Δ survivors ( Fig 5D ) , were type II . hst3Δ hst4Δ rad52-Y66A strains are synthetic lethal ( S5A Fig ) , similar to hst3Δ hst4Δ rad52Δ [64] . In addition , rtt109Δ rad52-Y66A double mutants are synthetic sick ( S5A Fig ) , similar to rtt109Δ rad52Δ double mutants [65] . These results indicate that while Rad52-dependent strand annealing and H3K56 acetylation are both important for SCR , to delay senescence , and for type II survivor formation , they function in different pathways . It is also possible that acetylation of H3K56 is important to delay senescence and promote type II survivor formation independently of its role in SCR .
The importance of Rad52 in delaying senescence and for telomerase-independent telomere maintenance in post-senescence survivors was first described over twenty years ago [9] . While much is now known about the role of HDR in telomerase-independent telomere maintenance in yeast as well as other organisms , including humans , the function of HDR during senescence is less-well understood . Rad52-mediated BIR has previously been implicated in preventing accelerated senescence , and it is thought that both Rad51-dependent and Rad51-independent BIR are involved [21–23] . In this study , we show that non-BIR functions of Rad52 , involving recombination between sister chromatids , are also required to delay senescence . We also find that proper regulation of H3K56 acetylation is important in preventing accelerated senescence . We present a model where Rad52-mediated HDR mechanisms act at telomeres during telomere attrition-induced senescence . We first tried to study the role of Rad52 during senescence with a telomere sequencing assay that has been used to detect telomere recombination events , or more specifically , intertelomeric recombination events and unequal sister telomere recombination events . Using this assay , it was estimated that such recombination events occur at a rate of 0 . 3% per telomere per generation [34] . Surprisingly , we find that the frequency of these events is not reduced by the deletion of RAD52—in fact , the frequency is even slightly increased ( Fig 1 ) . We show that a significant fraction of these events are caused by errors introduced during PCR amplification , propagation in E . coli , and/or DNA sequencing . Our results suggest that intertelomeric and unequal sister telomere recombination events occur at a substantially lower rate than 0 . 3% per telomere per generation . Furthermore , our findings indicate that data obtained previously with this assay suggesting the preferential recombination of short telomeres in senescing cells may need to be re-examined [37 , 41] , especially considering that telomere sequencing did not detect an increase in divergence events at an artificially-induced very short telomere [35] . However , it is known that Rad52 is important to act on this very short telomere to delay senescence and Rad52 is preferentially recruited to short telomeres in telomerase-negative cells [19 , 35] . In addition , recombination intermediates accumulate as telomeres shorten in tlc1Δ cells [66] . Therefore , Rad52-mediated HDR does preferentially act at short telomeres . It is important to note that our findings do not invalidate the use of the telomere sequencing assay to assess telomere recombination , but one needs to keep in mind that there is a background level of sequence divergence events that are not a result of recombination in vivo . Our data also allow us to conclude that these sequence divergence events are unrelated to the function of Rad52 during replicative senescence . It is formally possible that in the presence of Rad52 , sequence divergence events ( that are not due to technical artefacts ) are the result of Rad52-mediated recombination events , while in the absence of Rad52 , the divergence events are the result of Rad52-independent mechanisms . However , we find this possibility unlikely given the remarkably similar telomere sequence divergence profiles of est2Δ and est2Δ rad52 strains . Replication forks have difficulty progressing through subtelomeric and telomeric sequences , causing forks to stall and collapse [67 , 68] . A collapsed replication fork at a telomere would lead to a truncated telomere , and it has previously been shown that such truncations do occur in vivo , and that they are rapidly extended by telomerase [49] . In the absence of telomerase , telomere truncation events are likely repaired by BIR using the untruncated sister telomere as a template ( Fig 6 ) . Eliminating BIR by the deletion of either POL32 or RAD52 in telomerase-null cells leads to a similar phenotype: accelerated senescence and an inability to form survivors [9 , 10] . However , our data show that deletion of RAD52 in the absence of telomerase is more severe than deletion of POL32 , suggesting that Rad52 has functions in addition to BIR during senescence ( Fig 3 ) . Double-strand break repair ( DSBR ) , synthesis-dependent strand annealing ( SDSA ) , and single-strand annealing ( SSA ) are all well-studied Rad52-mediated HDR mechanisms , but all of these are initiated to repair a two-ended DSB . In the absence of exogenous stress , most DSBs occur during DNA replication , likely via replication fork collapse . A fork collapse would lead to a one-ended DSB , which is converted to a two-ended DSB when a replication fork coming from the opposite direction reaches the site of the fork collapse . However , a replication fork that collapses while traversing a chromosome end would stay one-ended since there is no replication fork coming from the distal end of the telomere . The one-ended DSB can be repaired by BIR , but not DSBR , SDSA , or SSA . Thus , Rad52-mediated DSBR , SDSA , or SSA are unlikely to be involved in delaying senescence . We find that hst3Δ hst4Δ and rtt109Δ mutants , which cause hyper- and hypo-acetylation of H3K56 , respectively , also display rapid senescence in the absence of telomerase ( Fig 5 ) . Like rad52 class C mutants , hst3Δ hst4Δ and rtt109Δ strains have defects in SCR [33] . Thus , we hypothesize that , in addition to its function in BIR , Rad52 delays senescence through a mechanism involving recombination of sister chromatids . Error-free PRR utilizes a newly synthesized sister chromatid as a template to replicate past replication fork impediments , so it could be seen as a type of SCR . Rad52-mediated HDR activity has also been implicated in error-free PRR , in both a Rad5-dependent and a Rad5-independent manner [57 , 58] . Rad5 localizes to a subset of telomeres during S and G2 phases , even in the absence of exogenous stress , and deletion of RAD5 in telomerase-null cells leads to accelerated senescence [22] . However , we find that rad52-Y66A , which is still proficient in Rad51-dependent BIR , and rad5Δ have additive effects in terms of telomere attrition-induced senescence ( S4 Fig ) , suggesting that if Rad52 participates in error-free PRR to delay senescence , it does so via a mechanism separate from Rad5-dependent error-free PRR . Consistent with this view , we have reported that the Shu complex , which is required for efficient HDR and involved in Rad5-mediated error-free PRR [57] , is not important for delaying senescence [69] . It has been suggested that error-free PRR proceeds via a Rad5-mediated pathway when the lesion is on the leading strand template , and a Rad52-mediated pathway when the lesion is on the lagging strand template [58] . We propose that this situation may be occurring at telomeres during senescence ( Fig 6 ) . We believe that replication problems at chromosome ends are amplified as telomeres get shorter . est2Δ rad52Δ cells do not exhibit a growth defect immediately after the loss of telomerase ( S6 Fig ) , indicating that telomeres need to shorten before Rad52 becomes important . As mentioned above , Rad52 and recombination intermediates accumulate at telomeres as they shorten [19 , 66] . One possible explanation for increased replication problems at short telomeres is that telomere shortening triggers TERRA transcription [70] , which could impede replication because of the replication fork encountering either the RNA polymerase machinery or RNA-DNA hybrids . Increased TERRA transcription and telomeric RNA-DNA hybrids both stimulate recombination at telomeres [39 , 40] . Mammalian RAD51 and BRCA2 , which performs many of the functions of yeast Rad52 [71] , are also required for proper telomere maintenance [72 , 73] , indicating that the importance of HDR at telomeres is highly conserved throughout evolution .
Standard yeast media and growth conditions were used [74 , 75] . All yeast strains used in this study are RAD5 derivatives of W303 [76 , 77] and are listed in Table 1 . Telomeres of 166 bp , 213 bp , and 230 bp , amplified by telomere PCR , were cloned into the pCR-Blunt vector from the Zero Blunt PCR Cloning Kit ( Invitrogen ) to generate plasmids pCC3 , pCC6 , and pCC2 , respectively . pCC3 and pCC2 were cut with EcoRI , and the telomere-containing fragment in each was subcloned into EcoRI-cut pRS306 ( ATCC ) to generate plasmids pCC9 and pCC10 ( two isolates containing 166 bp-long telomere sequences ) , and pCC7 and pCC8 ( two isolates containing 230 bp-long telomere sequences ) . pCC9 , pCC10 , pCC7 , and pCC8 were cut with NcoI and transformed into yeast strain W9100-12C to make CCY36 , CCY37 , CCY34 , and CCY35 , respectively . RAD52 was then deleted in CCY36 and CCY35 to generate CCY47 and CCY46 , respectively . Telomere VI-R was amplified by PCR using Phusion High-Fidelity DNA Polymerase ( New England Biolabs ) , essentially as previously described [49] . Telomere PCR products were purified using a QIAquick Gel Extraction Kit ( Qiagen ) , cloned using a Zero Blunt PCR Cloning Kit or a Zero Blunt TOPO PCR Cloning Kit ( Invitrogen ) , and transformed into One Shot TOP10 Chemically Competent E . coli ( Invitrogen ) . Individual clones were sequenced by GATC Biotech ( except for Fig 2 , where sequencing was performed by GENEWIZ ) , and the resulting data were analyzed using Sequencher software ( Gene Codes ) . The Sequencher files are included as Supporting Information . Excel files recording telomere sequence divergence data are included as S1 Dataset ( for Fig 1 ) and S2 Dataset ( for Fig 2 ) . For each set of sequences , the longest telomere without divergent sequence was used as a reference telomere to which all other telomeres are compared to determine whether divergence has occurred . A sequence was determined to be non-divergent if it matches perfectly to the consensus , if it contains single point mutations , or if it contains insertions or deletions of 6 nucleotides or less . Cells used for live-cell imaging were cultured in synthetic complete media . Microscopy was performed using a DeltaVision Deconvolution Microscope ( Applied Precision ) with InsightSSI , an Olympus UPLS Apo 100x oil objective with 1 . 4 numerical aperture , and a CoolSNAP HQ2 camera . Liquid culture senescence assays were performed as previously described [37 , 79] . All senescence assays started with the sporulation of est2Δ/EST2 heterozygous diploids . With the exception of S3 Fig , senescence data were plotted with PDs on the x-axis , not time ( i . e . days ) , because telomere shortening is a function of cell division and not time . Moreover , using PDs as a metric prevents slow growth associated with a particular mutation to be mistakenly interpreted as having an effect on senescence . For clarity , telomerase-positive control strains for each experiment are shown in separate graphs ( S5B and S7 Figs ) . We performed an unpaired two-tailed t-test to evaluate the difference in PDs at maximum senescence between two strains . Genomic DNA was isolated using a Wizard Genomic DNA Purification Kit ( Promega ) , digested with XhoI restriction endonuclease , separated by agarose gel electrophoresis , transferred to a Hybond-N+ membrane ( GE Healthcare ) , and hybridized to a telomere-specific ( 5′-CACCACACCCACACACCACACCCACA-3′ ) digoxigenin-labeled probe . | Telomeres are essential structures located at the ends of chromosomes . The canonical DNA replication machinery is unable to fully replicate DNA at chromosome ends , causing telomeres to shorten with every round of cell division . This shortening can be counteracted by an enzyme called telomerase , but in most human somatic cells , there is insufficient expression of telomerase to prevent telomere shortening . Cells with critically short telomeres can enter an arrested state known as senescence . Telomere attrition has been identified as a hallmark of human ageing . Homologous recombination proteins are important for proper telomere function in yeast and mammals . Yeast lacking both telomerase and Rad52 , required for almost all recombination , exhibits accelerated senescence , yet no apparent increase in the rate of telomere shortening . In this study , we explore the role of Rad52 during senescence by taking advantage of rad52 separation-of-function mutants . We find that Rad52 acts in multiple ways to overcome DNA replication problems at telomeres . Impediments to telomere replication can be dealt with by post-replication repair mechanisms , which use a newly synthesized sister chromatid as a template to replicate past the impediment , while telomere truncations , likely caused by the collapse of replication forks , can be extended by break-induced replication . | [
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] | 2016 | Multiple Rad52-Mediated Homology-Directed Repair Mechanisms Are Required to Prevent Telomere Attrition-Induced Senescence in Saccharomyces cerevisiae |
Rice ( Oryza sativa ) is a staple food for more than half the world and a model for studies of monocotyledonous species , which include cereal crops and candidate bioenergy grasses . A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%–60% yield losses globally each year . To elucidate stress response signaling networks , we constructed an interactome of 100 proteins by yeast two-hybrid ( Y2H ) assays around key regulators of the rice biotic and abiotic stress responses . We validated the interactome using protein–protein interaction ( PPI ) assays , co-expression of transcripts , and phenotypic analyses . Using this interactome-guided prediction and phenotype validation , we identified ten novel regulators of stress tolerance , including two from protein classes not previously known to function in stress responses . Several lines of evidence support cross-talk between biotic and abiotic stress responses . The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance .
A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%–60% yield losses globally each year [1] . The burgeoning field of systems biology provides new methodologies to make sense of plant stress responses , which are often controlled by highly complex signal transduction pathways that may involve tens or even thousands of proteins [2] . Complementary to large-scale approaches to delineate organisms' entire interactomes [3] , we have developed a focused , high-quality Y2H-based interactome around the following key proteins that control the rice responses to disease and flooding: XA21 [4] , NH1 ( NPR1 homolog1/OsNPR1 ) [5] , [6] , SUB1A and SUB1C ( submergence tolerance 1A , 1C ) [7] ( Figure 1A , Table S1 ) . XA21 is a host sensor ( also called a pattern recognition receptor ( PRR ) ) of conserved microbial signatures that confers resistance to the Gram-negative bacterium Xanthomonas oryzae pv . oryzae ( Xoo ) [4] , [8] , [9] . Overexpression of Nh1 in rice also enhances resistance to Xoo [5]; whereas reduced expression of Nh1 impairs benzothiadiazole-induced resistance to Pyricularia oryzae [10] . SUB1A and SUB1C are ethylene response transcription factors that regulate response to prolonged foliar submergence [7] . Much remains to be learned about the signaling pathways controlled by these pivotal stress response proteins . To identify components of these signaling pathways , we carried out yeast two hybrid screening to construct a rice response interactome . We then validated the robustness of the interactome using bimolecular fluorescence complementation [11] , yeast mating-based split ubiquitin system assays [12] , and phenotypic analysis . Transgenic analysis of genes encoding key proteins coupled with correlation analysis of transcriptomics data and protein-protein interactions revealed ten interactome members that function as positive or negative regulators of biotic or abiotic stress tolerance in rice . Fourteen additional members of the interactome have previously been reported to function in stress tolerance . The high-quality interactome and systems-level analyses described here represent significant progress toward elucidating the molecular basis of traits of agronomic importance .
We initially reconstructed four separate sub-interactomes for NH1 , the intracellular kinase domain of XA21 ( termed XA21K668 [13] ) , SUB1A , and SUB1C by screening a rice cDNA library pool . Subsequent rounds of screening with identified interactors , targeted assays with additional proteins identified based on sequence homology , and inclusion of connections from the rice kinase interactome [14] revealed that the NH1- , XA21- , and SUB1-anchored interactomes form a single rice stress interactome ( Figure 1A , Table S1 ) . The four sub-interactomes were constructed by using a high throughput yeast two hybrid ( Y2H ) approach to identify components of the XA21- , NH1- , and SUB1- signaling pathways . We identified a total of 8 unique XA21 binding proteins ( XBs , Table S1 ) . Five of these XBs , XB2 , XB10 ( hence forth called OsWRKY62 ) , XB11 , XB12 and XB22 , were chosen for further screening as baits in the Y2H to identify XB interacting proteins ( XBIPs ) . Using Arabidopsis NPR1 as bait , six interacting proteins ( NRR , NRRH1 , rTGA2 . 1 , rTGA2 . 2 , rTGA2 . 3 , and rLG2 ) were isolated by the same approach as described above . With NRR as bait , we isolated an additional six proteins ( NH1 , NH2 , NRRIP-1 , NRRIP-2 , and NRRIP-3 ) . With rTGA2 . 1 as bait , 4 interacting proteins were identified ( TGA2 . 1IP-1 , TGA2 . 1IP-2 , GRNL1 and GRNL2 ) . GRNL1 was used as bait to isolate nine interacting proteins ( rTGA2 . 1 , rTGA2 . 2 , GIP-1 , GIP-6 , GIP-9 , GIP-11 , GIP-13 , GIP-18 , GIP-20 , and GIP-23 ) . Using SUB1A and SUB1C as baits , we identified 20 SUB1A binding proteins ( SABs ) and 9 SUB1C binding proteins ( SCBs ) ( Table S1 ) . Two proteins , SAB8 ( SCB5 ) and SAB18 ( SCB9 ) , were identified using both SUB1A and SUB1C as baits . All identified proteins were repeatedly confirmed through secondary screenings were further characterized . Additional proteins were incorporated into the XA21 and NH1/NRR interaction based on literature curation and subsequent experimentation . For example , ten interactors identified through our previous rice kinase Y2H screen [14] , were incorporated into the the rice stress response interactome ( Figure 1A , Table S1 ) . We also demonstrated , through Y2H and co-immunoprecipitation assays , that OsRac1 ( rice small GTPase , previously shown to play an important role in the rice defense response ) interacts with RAR1 ( required for Mla12 resistance ) , HSP90 ( heat shock protein 90 ) , OsRBOHB ( rice respiratory burst oxidase homologB ) , and OsMPK1 [15] , [16] , [17] . We also showed that OsMPK12 ( blast- and wound-induced MAP kinase ( BWMK1 ) ) , which was previously demonstrated to be induced upon infection by Magnaporthe grisea ) , interacts with XB22IP-2 ( hereafter , called OsEREBP1 ( rice ethylene-responsive element-binding protein 1 , AP2 ) ) [18] . We tested additional interactions based on of the presence of predicted protein motifs . For example , a tetratricopeptide repeat domain found in XB22 is also found in SGT1 ( Suppressor of G-two allele of Skp1 ) . XB12 shows sequence similarity with p23 , a protein that modulates Hsp90-mediated folding of key molecules involved in diverse signal transduction pathways [19] . We therefore tested the protein interactions of these two XBs with components of the HSP90/SGT1/RAR1 chaperone complex [20] . Positive interactions were incorporated into the rice stress response interactome . Similarly , because NH1 interacts with NRR , we tested two predicted paralogs ( NRRH1 and NRRH2 ) with NH1 . While a genetic interaction between the NH1 and XA21 signaling pathways has previously been demonstrated [21] , signaling components shared between submergence tolerance and Xoo-resistance have not yet been described . The current network is composed of 100 proteins and shows significant enrichment ( by q<0 . 05 , Fisher exact test with multiple hypothesis adjustment [22] ) for several gene ontology ( GO ) terms related to both abiotic and biotic stress responses ( Figure 1B , Table S2 ) . Among molecular functions , the rice stress response interactome is particularly rich in transcription factors ( diamond nodes in Figure 1A , p-value = 7 . 1×10−5 , Fisher exact test ) , including 5 WRKY proteins , 4 TGA proteins , and 4 AP2 factors . Validation of subsets of protein-protein interactions ( PPIs ) with two additional in vivo assays provides evidence that the interactome is of high quality . Using a mating-based split ubiquitin system that measures interactions with transmembrane proteins [12] , we confirmed that 80% ( 8 out of 10 tested ) of the XA21-binding ( XB ) proteins are able to interact with the full-length , membrane-spanning XA21 ( the initial screen was conducted with the truncated XA21K668 protein ) ( Figure 1A , Figure S1 ) . To assess whether the observed Y2H protein-protein interactions occur in plant cells , we examined 30 candidate proteins pairs using bimolecular fluorescence complementation ( BiFC ) in rice protoplasts . To rule out false-positive interactions , we tested the interaction of each protein with negative control vectors consisting of half of the yellow fluorescent protein . We found that 14 of the 30 tested showed interactions as detected by fluorescence only in the presence of the interacting rice protein but not in the presence of the negative control . Four proteins fluoresced in the presence of the negative control but displayed greatly enhanced fluorescence intensity in the presence of the interacting rice protein indicating that the interaction could be reproduced in vivo . Together these results indicate that 60% ( 18/30 ) of the tested pairs of interactome members interact in rice protoplasts as revealed by BiFC assays ( Figure 1A , Figure S2 , Table S3 ) . Components showing a large number of interactions with other interactome members ( high degree ) have been hypothesized to be essential for survival of the organism [23] although this finding has been disputed [24] . To identify such key hub proteins , we identified components in the rice stress interactome that displayed high degrees of interactions and then subjected them to pair-wise PPI assays . We tested a 24×20 matrix of 27 biotic stress ( XA21 ) interactome components , a 14×14 matrix of 16 abiotic stress ( SUB1 ) interactome components , and a 24×16 matrix of biotic-abiotic interactome components ( Text S1 , Table S4 ) . An interaction was considered significant and reproducible if we observed it was replicated in two to three independent assays ( Table S4 ) . Pair-wise PPI assays among interactome members revealed large numbers of possible interactions within and between the biotic and abiotic sub-interactomes ( average degree 11±8 , Figure 1C , Table S4 ) . These interactomes have a high percentage ( 21 . 8% ) of interactions beween their components ( 232 interactions out of 1060 tested ) ( Table S4 ) . The biotic stress response interactome exhibits the highest level of interactions at 27 . 5% ( 132/480 ) . The abiotic stress response interactome and the union between the biotic-abiotic stress response interactomes are even more highly connected [18 . 9% ( 37/196 ) and 16 . 4% ( 63/384 ) , respectively] . The high number of interactions observed in the stress response interactome suggests that a large fraction of the components are capable of interacting with each other . These results also suggest that these components serve as members of large and/or changing complexes in vivo [25] . While the high number of interactions we observed is an order of magnitude greater than observed for studies of large-scale interactomes [3] , it is comparable to smaller scale , more focused studies , such as that carried out for Arabidopsis MADS box transcription factors . In the MADS-box factor study , an average of only 5 . 4% of the components showed interactions ( 272/4998 ) . However , when transcription factors predicted to function in the same biological process were examined , they displayed an increased number of interactions . For example , MADS-box factors predicted to be involved in floral development showed >15% interactions [26] . Consistent with their demonstrated key roles in response to stress , XA21 , SUB1A , and SUB1C exhibit a high degree of interactions . In the matrix-based PPI tests , each of these interacted with over 10 additional proteins not initially identified as interactors in the original screen ( Table S4 ) . Other proteins with published roles in biotic stress signaling , including XB15 [13] , XB3 [27] , OsWRKY62 [28] , and XB24 [29] are also among those with an above average degree of interaction . Such hubs may have a higher chance of engaging in essential functions because they participate in more interactions [30] . Coexpression network analysis and stress-specific transcriptomics of the interactome components support the validity of the interactome as an integrated module and highlights specific nodes that may function in cross-talk between the abiotic and biotic stress responses ( Figure 2 ) . The interactome is highly enriched for genes with correlated or anticorrelated expression compared with the whole genome ( Figure 2A and 2C ) . For this analysis , we built rice biotic and abiotic stress gene transcript coexpression networks for the interactome members based on Pearson's correlation coefficients ( PCC ) calculated from publically available Affymetrix microarray data ( Table S5 ) . We define a correlated or anticorrelated interaction by PCC > |0 . 5| , a criterion under which 15% of interactome gene pairs interact , compared with ∼5 . 5% of pairs in the whole rice genome , and no pairs when the expression profiles are randomized ( Figure 2A and 2C , Table S5 ) . In both the coexpression networks derived from the abiotic and biotic microarray datasets , many components of the SUB1A ( abiotic stress ) and the XA21/NH1 ( biotic stress ) sub-interactomes display highly correlated or anticorrelated expression ( Figure 2B and 2D , Table S5 ) . This result further supports cross talk between the abiotic and biotic response networks . Contrasting the networks built from the different array sets , reveals that only a fraction of edges are conserved between the biotic and abiotic gene expression networks . This suggests that the expression of interactome members , and thus their availability to form PPIs with each other , varies depending on the stress regime , consistent with a model of dynamic complex formation [31] ( Figure 2B and 2D ) . We also generated microarray data to monitor transcriptional responses of Xa21-expressing and Nh1- and Nrr-overexpressing rice ( NRR binds NH1 and is a negative regulator of resistance [21] ) before and after Xoo infection . Analysis of this dataset as well as a previously reported Sub1a-specific response dataset [32] , reveals that interactome members are significantly enriched among differentially expressed genes ( p<0 . 05 , Fisher exact test , Figure 2E , Figure 3 , Table S6 , Figure S3 ) . The interactome includes fourteen components that have previously been shown to regulate resistance to Xoo , further supporting the high quality of the interactome ( Figure 1A , Table S7 ) . We measured the Xoo and/or submergence response phenotypes of mutant rice lines for twenty additional interactome members , focusing primarily on genes encoding proteins with a high degree of PPIs ( Table S7 ) . Note that because of this bias in our experimental design , we are unable to test for correlation between a high degree of PPIs and a functional role in rice stress tolerance . Our phenotypic results show that nine out of seventeen genes ( 53% ) that we assayed for a role in resistance to Xoo showed altered defense response phenotypes . Only one out of nine genotypes assayed showed altered tolerance to submergence , possibly due to the absence of SUB1A in the genotypes we examined ( Table 1 , Figure 3A–3H , Figures S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 ) . Importantly , our phenotypic analysis revealed roles for two protein classes that , to our knowledge , were previously unknown to function in the plant stress response . on sequence similarities , SAB18 is a SANT-domain transcription factor , and , SCB3 , is an enzyme involved in lysine biosynthesis ( Table 1 ) . SAB18 is a negative regulator of submergence tolerance suggesting that it may modulate the antagonistic activities of its two binding partners , SUB1A and SUB1C ( Figure 3G and 3H , Figure S13 ) . SCB3 serves as a positive regulator of resistance to Xoo ( Figure S8 ) . This result together with an earlier report showing that lysine levels increase in the Xoo-challenged Xa21 rice compared to mock treated controls [33] , suggests that lysine plays an important , although undefined , role in the rice innate immune response . The remaining eight proteins that we demonstrate to be involved in rice innate immunity have similarity to known stress-response factors ( Table 1 , Table S7 , Text S1 ) . Though many of these proteins were identified due to association with XA21 or an XB , modification of the expression of four of these genes gives altered resistance phenotypes in the absence of XA21 ( Table 1 ) , suggesting that they function in multiple biotic stress-response signaling pathways . Of particular significance , knockdown or knockout experiments show a role for three proteins , ( RAR1 , WAK 25 ( wall associated kinase 25 ) , and SnRK1 ( sucrose non-fermenting-related protein kinase 1 ) ) , in XA21-mediated immunity . The chaperone complex , HSP90/RAR1/SGT1 has been long known to play a positive role in intracellular NBS-LRR-mediated immunity [34] . RAR1 and HSP90 have also been shown to play a role in Arabidopsis FLS2-mediated signaling [35] and maturation of the rice chitin extracellular receptor OsCERK1 [36] , respectively . Our observation that RAR1 serves as a positive regulator of XA21-mediated immunity ( Figure 3A and 3B , Figure S6 ) further affirms that this complex contributes to host sensor-mediated immunity . Wak25 ( LOC_Os03g12470 ) , compromises XA21-mediated immunity ( Figure S10 ) , indicating that WAK25 is a positive regulator of this process . WAKs have previously been shown to function as positive regulators of plant defense responses [37] . Although we do not yet know how WAK25 serves to regulate XA21-mediated immunity , there is precedence for interaction of PRRs with other receptor kinases . For example , the Arabidopsis FLS2 PRR interacts with the BRI1-associated kinase ( BAK1 ) to transduce the immune response [38] . We also found that OsMPK5 , previously demonstrated to serve as a negative regulator of resistance to the fungus , Magnaporthe grisea , and the bacteria , Burkholderia glumae [39] , also negatively regulates resistance to Xoo ( Figure S4 ) . In contrast , the Arabidopsis protein with highest similarity to OsMPK5 , AtMPK3 , acts downstream of the Arabidopsis host sensor FLS2 and is a positive regulator of camalexin-mediated resistance to Botrytis cinera [40] , [41] . The opposite regulatory roles for these Arabidopsis and rice predicted MPK orthologs underlines the limitations of extrapolating function between plant species . OsMPK12 -and OsEREBP1 - are also positive regulators of resistance to Xoo ( Figure S5 , Figure S12 ) . OsMPK12 was previously shown to phosphorylate OsEREBP1 [18] . OsEREBP1 , as phosphorylated by OsMPK12 , exhibits enhanced binding to the GCC box element of pathogenicity-related ( PR ) gene promoters . Overexpression of OsMPK12 in tobacco enhances expression of PR genes and increases resistance to Pseudomonas syringae and Phytophthora parasitica infection [18] . Thus , our results together with previously published studies indicate that OsMPK12 and OsEREBP1 are positive regulators of resistance to many pathogens . We have also demonstrated a negative regulatory function for OsWRKY76 ( Figure 3E and 3F , Figure S11 ) , as has previously been shown for OsWRKY62 [28] . These two OsWRKYs are in the same WRKY subgroup ( IIA ) and are orthologs of barley HvWRKY1 and HvWRKY2 , which serve as negative regulators of resistance to Blumeria graminis [42] . Along with our observation that the OsWRKY IIA proteins interact with members of the XA21 and SUB1 sub-interactomes [28] , [43] , these data are consistent with the WRKYIIA proteins playing a key role in fine-tuning grass defense responses . SAB23 is a plant homeobox domain- ( PHD ) containing protein , which is known to function in development [44] and has been linked to response to pathogen stress [45] ( Table 1 ) . SAB23 serves as a negative regulator of resistance to Xoo ( Figure 3C and 3D , Figure S7 ) . This result supports previous observations that components regulating XA21-mediated resistance are also involved in developmental regulation [21] , [46] , [47] SnRK1A , a well-known regulator of sugar sensing [48] , was identified as a positive regulator in XA21-mediated immunity ( Figure S9 ) . Arabidopsis SnRK1 has been identified as a key regulator in sugar sensing and abscisic acid ( ABA ) signaling [49] . Though ABA has typically been found to act as a positive regulator of abiotic stress responses and a negative regulator of biotic stress responses [50] , several positive regulators of the rice biotic stress response including SnRK1A and OsMPK12 participate in ABA signaling . Genes with ABA-related GO annotations are also up-regulated in Nh1-overexpressing and Sub1a-expressing transgenic rice ( q = 1 . 3×10−2 and q = 5 . 3×10−10 , respectively , Fisher exact test , multiple hypothesis adjustment ) ( Table S9 ) . Together these observations support the hypothesis that ABA also has important functions in resistance to Xoo and tolerance to submergence in rice . Comparable to analyses that show a correlation between essentiality and network degree centrality for essential genes [51] and negative regulators of growth ( i . e . , tumor suppressors ) [52] , we found that the rice interactome proteins with a validated role in the stress response have a significantly higher degree centrality in the abiotic co-expression network compared with those for which we were unable to measure a phenotype ( Figure 3i , p = 3 . 7×10−2 , Wilcoxon signed rank test , Table S8 ) . Thus , interactome members that serve as central hubs as measured by co-expression analysis are more likely to function in the stress response than those members that do not serve as central hubs . This observation indicates the power of using the “guilt-by-association principle” to guide experiments based on co-expression maps [53] , [54] . Here , we constructed a rice stress response interactome composed of 100 proteins governing the rice response to biotic and abiotic stress . Integration of protein-protein interaction assays , co-expression studies , and phenotypic analyses allowed us to efficiently identify ten novel proteins regulating the rice stress response .
The XA21 kinase fragment K668 was cloned into the Y2H bait vector pMC86 . SUB1A and SUB1C were also cloned into pMC86 . Sequence information is provided in Table S1 . The Y2H screening experiments for SUB1A and SUB1C were conducted in the same manner as those for XA21 . Bait constructs were transformed into yeast strains HF7c MATa , plated on selective medium , and screened as described ( Clontech's Matchmaker Pretransformed Libraries User Manual ) . Colonies from the HF7c baits were grown to approximately 2×108 cfu/mL in 50 mL synthetic dextrose ( SD: 6 . 7 g Difco yeast nitrogen base w/o amino acids , 2% glucose , 1X drop out solution [supplemented with appropriate amino acids] , pH 5 . 8 ) lacking Tryptophan ( Trp ) media for use in the primary screens . Cells of HF7c baits were pelleted , washed once with sterile H2O and resuspended in 50 mL rich yeast media , YPAD ( 20 g Difco peptone , 10 g yeast extract , 40 mg Adenine hemisulfate , 2% glucose , pH 5 . 8 ) . Target yeast ( Y187 ) were transformed with cDNAs from a Hybrizap ( Stratagene ) Y2H library derived from seven-week-old IRBB21 ( Indica cultivar containing Xa21 ) leaf mRNA . One aliquot of the Y187 target yeast was mixed with the Hf7c bait yeast in 50 mL YPAD and poured into a tissue culture flask . Yeast strains were allowed to mate for 20 to 24 hrs at 28°C with slight shaking . Yeast were then isolated and washed twice with sterile water and plated on SD medium lacking Histidine ( His ) , Tryptophan ( Trp ) , Leucine ( Leu ) and supplemented with 2 mM 3-amino-1 , 2 , 4-triazole ( 3-AT ) . Putative positive diploids from the primary screens were isolated and plasmids extracted . Confirmation of interacting proteins through plasmid re-transformation eliminates many false positives; a step often dispensed of in high throughput Y2H studies due to the encumbrance of bacterial transformation and plasmid propagation [14] . Yeast plasmids were transformed into E . coli DH5α to amplify plasmids . Amplified plasmids were then re-transformed into the yeast strain AH109 ( Clonetech ) to confirm interactions . Transformed yeast for the secondary screens were first plated on selective medium lacking Leu and Trp . Once yeast colonies appeared , they were then streaked on selective medium lacking His , Leu , and Trp , plus 2 mM 3-AT and medium lacking Ade , Leu , and Trp . Prey plasmids were isolated and sequenced only after confirmation in secondary screens . The PPI datasets were submitted directly to DIP and assigned the International Molecular Exchange identifier IM-15311[55] . For mating based-split ubitquitin assays , we followed protocols and used vectors and yeast strains as described previously [12] . In brief , using Gateway LR Clonase ( Invitrogen ) we constructed the bait by transferring XA21cDNA from pENT/D into pMetYC_Gate and the preys through transfer of the corresponding cDNA from pENT/D into pNX_Gate32-3HA . Primers for these constructs are described in Table S10 . For identification of positive interaction via yeast mating , the bait and prey constructs were transformed to yeast strain THY . AP5 and THY . AP5 , respectively by using the yeast transformation kit , Frozen-EZ yeast transformation II ( Zymo Research ) . Positive interactions were selected by colony growth in minimal SD/Ade-/Leu-/Trp-/His- media ( Figure S1 ) . We conducted BiFC assays as described in Ding et al . [14] . As negative controls , we included the both empty vectors ( 735 ( YC ) -EV and 736 ( YN ) -EV ) for each pair-wise test . The BiFC assays are summarized in Table S3 and Figure S2 . We calculated Pearson correlation coefficient ( PCC ) scores to measure tendency of coexpression between genes based on two sets of publicly available Affymetrix microarray data—219 rice abiotic and 179 rice biotic category data—for 37 , 993 genes which have Affymetrix probe set matched , of which 34 , 016 have unique Affymetrix probe set available and only these genes were included in this database ( Table S5 ) . The raw Affymetrix data was downloaded from NCBI Gene Expression Omnibus [56] and EBI ArrayExpress [57] . We processed raw Affymetrix data using the MAS 5 . 0 R-package . The trimmed mean target intensity of each array was arbitrarily set to 500 , and the data were then log2 transformed . The Rice Multiple-platform Microarray Element Search was used to map the Affymetrix probesets to rice genes [58] . Distributions of PCC scores of 578 , 527 , 120 pairs of rice genes with processed microarrays or with randomized microarrays ( by random shuffling of arrays ) are summarized in Figure 2A and 2C and Table S5 . We grew TaiPei309 ( TP309 ) , Xa21::Xa21 106-17-3-37 , LiaoGeng ( LG ) , Ubi::Nh1 LG 11 , and Ubi::Nrr 64 LG plants for six weeks in the greenhouse . We then transferred the plants to a growth chamber set for a 14-h daytime period , a 28/26°C temperature cycle and 90% humidity . We employed the scissors dip method with multiple cuts to inoculate the plants using a suspension ( OD600 of 0 . 5 ) of PXO99 Xoo . One and two days after inoculation , mock-inoculated and inoculated leaves were harvested for gene expression profiling using the NSF45K array . The replicate mRNAs for the comparisons of Ubi::Xa21 TP309 vs TP309 , Ubi::Nh1 LG vs . LG , and Ubi::Nrr LG vs . LG were labeled with either Cy3 or Cy5 dyes , resulting in one technical replicate and three biological replicates per genotype pair . Gene expression data were processed as previously described [58] . The microarray data have been deposited to NCBI GEO and have the accession number GSE22112 . | A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%–60% yield losses globally each year . In this paper , we used a yeast-based approach to identify rice proteins that govern the rice stress response . We validated the role of these new proteins using additional analyses to evaluate the function of these genes in rice and assessed whether they serve to positively or negatively regulate the stress response . This approach allowed us to identify ten genes that control resistance to bacterial disease and tolerance to submergence . The combination of approaches described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"agriculture",
"biology"
] | 2011 | Towards Establishment of a Rice Stress Response Interactome |
Buruli Ulcer ( BU ) is a tropical infectious skin disease that is currently treated with 8 weeks of intramuscular streptomycin and oral rifampicin . As prolonged streptomycin administration can cause both oto- and nephrotoxicity , we evaluated its long term toxicity by following-up former BU patients that had received either 4 or 8 weeks of streptomycin in addition to other drugs between 2006 and 2008 , in the context of a randomized controlled trial . Former patients were retrieved in 2012 , and oto- and nephrotoxicity were determined by audiometry and serum creatinine levels . Data were compared with baseline and week 8 measurements during the drug trial . Of the total of 151 former patients , 127 ( 84% ) were retrieved . Ototoxicity was present in 29% of adults and 25% of children . Adults in the 8 week streptomycin group had significantly higher hearing thresholds in all frequencies at long term follow-up , and these differences were most prominent in the high frequencies . In children , no differences between the two treatment arms were found . Nephrotoxicity that had been detected in 14% of adults and in 13% of children during treatment , was present in only 2 . 4% of patients at long term follow-up . Prolonged streptomycin administration in the adult study subjects caused significant persistent hearing loss , especially in the high frequency range . Nephrotoxicity was also present in both adults and children but appeared to be transient . Streptomycin should be given with caution especially in patients aged 16 or older , and in individuals with concurrent risks for renal dysfunction or hearing loss .
Buruli ulcer ( BU ) is a Neglected Tropical Disease caused by infection with Mycobacterium ulcerans . Necrotic skin lesions characterize the disease , often with typical undermined edges . Treatment delay may result in disfigurement and functional limitations [1] . Though isolated cases or small outbreaks have been reported in over 30 countries , the disease is mainly found in endemic areas in rural West- and Central-Africa , with around 6 . 000 cases occurring annually [2]–[4] . Over the past decade the main mode of treatment has shifted from surgery to antimicrobial therapy . Several antibiotic regimens have been proposed and studied , and the current regimen advised by the World Health Organization consists of 8 weeks of oral rifampicin and intramuscular streptomycin [5]–[8] . Although surgery can be problematic in under-resourced developing countries due to high costs and low availability , prolonged streptomycin administration has complications of its own . These complications are partly related to the parenteral mode of administration of streptomycin , but also to its intrinsic toxicity , notably , ototoxicity and nephrotoxicity [9] . Streptomycin induced ototoxicity encompasses damage to both the cochlea and vestibulum [10] , although the effects of vestibular damage can to some extent be compensated for by the brain , and so are clinically less prominent [11] . The damage is caused by reactive oxygen species that are formed by a redox-active complex of parts of the aminoglycoside and biologically available iron ions [12] . The most frequently reported manifestations of ototoxicity are high-frequency sensorineural hearing loss , and vestibular dysfunction resulting in disequilibrium and loss of the vestibular ocular-reflex [12] . These side-effects are often transient , but chronic effects , especially hearing loss , have also been reported . The wide range in incidence varies between 5% and 25% , depending on the specific aminoglycoside drug , patient group ( older age groups are probably more vulnerable ) , and definition of ototoxicity including the method of detection [13] , [14] . The occurrence of ototoxicity does not appear to be related to the individual dosages or the frequency of administration per se , but rather to the cumulative dose , and the age of the patient [15] . Also , there appears to be a modest genetic susceptibility , caused by a point mutation in mitochondrial DNA [16] . The nephrotoxic effects of streptomycin are most prominent in the proximal tubule , and transient nephropathy , manifested by a slow rise in serum creatinine and a decreased glomerular filtration rate , is observed in 10–20% of therapeutic courses [17] . Unlike aminoglycoside-induced ototoxicity , long-term kidney damage is uncommon . It has long been hypothesized that aminoglycoside-induced nephrotoxicity would lead to increased serum concentrations of the drug , resulting in ototoxicity , or that some other link between aminoglycoside induced oto- and nephrotoxicity existed . Several studies have however been unable to demonstrate such a relationship , and it is believed that the two are largely independent [15] , [18] , [19] . With a total cumulative dose of up to 56 g ( 15 mg/kg daily with a 1 g daily maximum , 56 doses ) , the amount of streptomycin that is administered to treat BU is higher than in the treatment of brucellosis , endocarditis , and urinary tract infections , but similar to the treatment of Mycobacterium avium complex and second line treatment for tuberculosis . However , no data exist on the long-term toxicity of the currently recommended regimen of antimicrobial therapy in BU . Therefore , we chose to study former BU patients that participated earlier in a randomized controlled trial conducted between 2006 and 2008 [20] . In that trial patients were randomized to receive either 8 or 4 weeks of streptomycin , allowing a direct comparison between the two groups in terms of long-term streptomycin toxicity .
The study protocol was approved by the Committee on Human Research , Publication , and Ethics of the Kwame Nkrumah University of Science and Technology and the Komfo Anokye Teaching Hospital , Kumasi ( reference number CHRPE/AP/133/12 ) . Written informed consent was obtained from all participants aged ≥12 years , and consent from parents , or legal representatives of participants aged ≤18 years . We retrieved our study subjects among individuals that had earlier participated in the Burulico trial , conducted between 2006 and 2009 in Ghana , registered with number NCT00321178 at clinicaltrials . gov . For that trial , patients aged 5 years or older , clinically diagnosed with early ( duration <6 months ) , limited ( cross-sectional diameter <10 cm ) M ulcerans infection were included , and randomized to receive either 8 weeks of streptomycin at 15 mg/kg daily ( max 1000 mg daily ) and 8 weeks of rifampicin at 10 mg/kg daily ( max 600 mg daily ) , or 4 weeks of streptomycin and rifampicin , followed by 4 weeks of rifampicin and clarithromycin at 7 . 5 mg/kg daily . Pregnancy , drug intolerance , and renal , hepatic , and acoustic impairment were among the exclusion criteria , and co-medication with drugs other than the study drugs occurred in less than 5% of patients , and mainly consisted of NSAIDs . Patients had a median age of 12 and 30% were male . Patient characteristics are described in more detail in Nienhuis et al . [20] . For the present follow-up study , participants were retrieved between June and November 2012 by visiting their last known village or through telephone contact if available . If the former patient was no longer living at the last known village , neighbors , relatives , and community leaders were asked for additional information . When a former patient was located , he or she was informed about the study , given time to consider participation , and was asked for consent . The equipment and procedure followed for audiometry at follow-up were identical to those during the drug trial . Audiometry was performed in a quiet room in one of the two study hospitals of the BURULICO drug trial with portable Interacoustics AS208 audiometers , with circumaural earphones with noise reducing Peltor mute cushions . The audiometers were calibrated to ISO64 , both before the drug trial and again before the follow-up . Biological calibration performed weekly . During a biological calibration session , a person with known , stable hearing thresholds was tested . A persistent deviation of 10 dB or more would prompt acoustic calibration of the audiometer . However , both during the drug trial and the follow-up study , no acoustic calibration was necessary . As soundproof rooms were not available at the study sites , ambient noise ( AN ) levels were measured with a voltcraft SL-100 ambient noise meter in dBA filter weighting during each audiometry session . The person performing the follow-up audiometry was blinded to the treatment allocation during the Burulico study , and was therefore unaware of earlier streptomycin treatment duration . Both ears were tested , the right ear first . Frequencies were tested in the following order: 1 , 2 , 4 , 6 , 8 , 0 . 5 and 0 . 25 kHz . Testing started with presenting a loud tone . Then the intensity was decreased in steps of 5 dB until the tone was not heard anymore . The last tone still heard was the level of audibility of the first test run . Then the intensity was increased with 10 dB before being decreased again in steps of 5 dB , until the tone was no longer heard ( the second test run ) . If the results of the two test runs were not identical , the process was repeated until two identical levels of audibility were found . This level was considered the hearing threshold . The same procedure was followed during the drug trial , and so , audiograms were available for baseline , after 8 weeks of treatment , and at long-term follow-up . In addition to audiometry , each subject was asked at follow-up about complaints of hearing loss and dizziness after completion of treatment . To detect late nephrotoxic effects of the streptomycin administration 4–6 years earlier , serum creatinine was measured in a sample of venous blood ( approximately 2 ml ) at the same laboratory , where serum creatinine was measured during the drug trial , using the same analytic technique . The results were compared with the measurements at baseline , after 2 , 4 , 6 , and 8 weeks of treatment during the BURULICO study . Patients with suspected long-term nephrotoxicity were referred to the hospital for further management . The total cumulative dose of streptomycin administered was estimated to be 56 times the daily dose . Individual records of compliance were not available , but during the drug trial , streptomycin administration was only performed in participating hospitals and health centers and was recorded on tally sheets with compliance approaching 100% . As aminoglycoside toxicity is reported to occur less frequently in children [21] , [22] , we analyzed the results separately for those ≤16 and >16 years of age at the time of receiving streptomycin treatment during the Burulico trial . For each frequency , the mean of measurement results of audiometry of both ears was recorded . As the distribution of hearing thresholds is usually skewed to the left , differences between the two treatment arms were assessed with nonparametric Mann-Whitney-U tests . According to the criteria of the American Speech-Language-Hearing Association [23] , an increased threshold of 20 dB at any one frequency or an increase of 15 dB at two consecutive frequencies was classified as hearing loss . Nephrotoxicity was defined as a rise in serum creatinine at two consecutive assessments during treatment compared to baseline of ≥44 µmol/L ( 0 . 5 mg/dL ) or 50% [17] , [24] . As serum creatinine levels increase with age and body weight , especially muscle mass , it was not possible to make a meaningful direct comparison between the serum creatinine concentrations in the present study with those obtained 4–6 years earlier during the drug trial . Instead , a decrease of the estimated Glomerular Filtration Rate ( eGFR ) of >25% in this follow-up study compared to baseline was defined as long term nephrotoxicity [25] . In adults , the eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration ( CKD-EPI ) equation [26] , as this was earlier shown to be superior to other equations in a Ghanaian population [27] . In children the eGFR was computed using the revised Schwartz formula [28] .
Of the 151 former participants of the Burulico trial 127 individuals ( 84% ) were retrieved for follow up . Although the trial had taken place in the Ashanti region of Ghana , many former patients were not ethnically Ashanti , and had moved away from the study site , and patients were retrieved in 9 of Ghanas 11 regions , including the three Northern regions more than 700 kilometers and approximately 10 hours by road from the original study site . 68% of the retrieved former patients were female , and the median age at follow-up was 18 years . Sixty-eight percent of our study participants had been 16 or younger at the time they received treatment during the Burulico trial . Of the 24 former patients not retrieved , 4 were already lost to follow-up during the Burulico trial , 3 had moved abroad , 2 had deceased , and the fate of the remaining 15 was unknown . The patients that were lost to follow-up did not differ significantly from the patients that were retrieved in terms of baseline and 8 week audiometry and serum creatinine , age , sex or treatment arm . The median duration between drug administration and follow-up was 5 years . Overall , the patients reported to be doing well . 28% reported to have experienced another disease since their BU was cured , with most patients reporting malaria , headache , and stomach ache . One retrieved patient was HIV-positive . Three patients reported currently taking analgesics , and 2 patients reported currently taking anti-hypertensives . Twenty-nine percent of adults , and 25% of children were classified as having hearing loss compared to baseline audiometry after 8 weeks of treatment , and 31% of adults , and 27% of children were classified as having hearing loss at long term follow-up compared to baseline audiometry . Hearing loss after 8 weeks was significantly associated with hearing loss at long term follow-up in ( p = 0 . 017 by Χ2 ) , but not in children ( p = 0 . 102 by Χ2 ) . The average hearing thresholds at long-term follow up for both treatment arms for adults are shown in Figure 1 , and for children in Figure 2 . For adults , no differences between the two treatment arms existed in any of the frequencies at baseline , and after 8 weeks of treatment , there was a significant difference in hearing threshold between treatment arms at 6000 Hz ( p = 0 . 03 ) in adults , while those at 500 ( p = 0 . 07 ) , 1000 ( p = 0 . 09 ) , and 8000 Hz ( p = 0 . 09 ) approached significance by Mann-Whitney U test . For children , there were no significant differences between the treatment arms at any frequency at baseline or after 8 weeks of treatment . For both adults and children , univariate associations between long term ototoxicity and treatment arm , age , sex , total cumulative dose of streptomycin and complaints of hearing loss were determined . The results are shown in table 1 . The average ( SD ) level of ambient noise ( AN ) was 45 . 6 ( 6 . 5 ) dB . From self-report , 10% of adults reported experiencing hearing loss ( 5% in the 8 week streptomycin group vs 16% in the 4 week streptomycin group; p>0 . 3 by Χ2 ) and 12% of children reported experiencing hearing loss ( 9% in the 8 week streptomycin group vs 15% in the 4 week streptomycin group; p>0 . 3 by Χ2 ) after treatment was completed . Ten percent of adults reported experiencing dizzyness ( 5% in the 8 week streptomycin group vs 16% in the 4 week streptomycin group; p>0 . 3 by Χ2 ) and 12% of children reported experiencing dizzyness ( 9% in the 8 week streptomycin group vs 15% in the 4 week streptomycin group; p>0 . 3 by Χ2 ) after treatment was completed . There appeared to be no association between audiometrically classified and self-reported hearing loss , as 14% of the patients that were audiometrically classified as having hearing loss at long term follow-up also complained of hearing loss vs 9% of patients that were not audiometrically classified as having hearing loss ( p>0 . 3 by Χ2 ) . During treatment , 14% of adults , and 13% of children were classified as having nephrotoxicity . At long term follow-up 1 adult ( 2 . 4% ) and 2 children ( 2 . 4% ) were classified as having long-term nephrotoxicity . All 3 of these patients had received streptomycin for 8 weeks ( p<0 . 1 by Χ2 ) , but only 1 was also classified as having nephrotoxicity during treatment . The average serum creatinine concentrations for both treatment arms for adults are shown in Figure 3 , and for children in Figure 4 . For both adults and children , univariate associations between nephrotoxicity and treatment arm , age , weight , sex , and total cumulative dose of streptomycin were determined . The results are shown in table 2 .
In this study we followed-up individuals that were randomly assigned to either 4 or 8 weeks of streptomycin injections 4 to 6 years earlier , and that had similar results in audiometry at baseline . This provided a unique model to compare long-term toxicity incurred by doubling the cumulative dosage of streptomycin . In adults , we found an important and clinically relevant increase in hearing loss in the high frequencies in individuals that had received 8 weeks of streptomycin , in comparison to those that had received only 4 weeks of this aminoglycoside . In children , no differences between the two treatment arms were detectable . Although macrolides , especially , azithromycin have also been connected to hearing loss [29] , the ototoxicity associated with prolonged streptomycin use was significantly more pronounced than in individuals that had received clarithromycin instead . The ototoxic effects of prolonged duration of streptomycin therapy appeared to differ between adults and children . To our knowledge , no studies have evaluated the ototoxicity of an aminoglycoside in a study population consisting of both children and adults suffering from the same illness , exposed to the same treatment schedule . Low levels of ototoxicity in children have been reported before [21] , [22] , but the exact mechanisms for this interaction with age are not well understood [21] . The incidence of hearing loss in our study was slightly higher than in earlier reports , which may be due to the fact that we used the formal ASHA criteria that are designed to detect early changes , so as to avoid more severe damage with ongoing aminoglycoside administration . In children , there was only a weak relationship between hearing loss detected by audiometry and complaints of hearing loss by the patient , and in adults there was no relationship at all . Audiometric and subjective ototoxicity often correlate poorly [15] , [30] , [31] , because audiometric toxicity is usually – as in our study – present in the high frequencies . High frequency hearing loss does not affect primary speech hearing , but rather affects the ability to follow group conversations , and causes people to hear the wrong words without noticing it [32] . This may explain why many clinicians are not very concerned about ototoxicity in the treatment of BU , as their patients usually do not report hearing loss spontaneously . In our study , nephrotoxicity was present in both children and adults , occurred more often in the 8 week streptomycin group and appeared to be related to the total cumulative dose of streptomycin . However , long term nephrotoxicity was only detected in 3 study subjects . The occurrence of both transient and long-term streptomycin nephrotoxicity in our study might be a low estimate for the population at risk in general , as BU is a local infection , usually without systemic involvement , and most patients were young and did not have any significant comorbidities , medical history or co-medication , contrary to other patient categories that are administered aminoglycosides , e . g . ICU patients or those with cystic fibrosis . In addition , streptomycin was administered once daily , which is known to reduce the incidence of nephrotoxicity [33] . Our study had several limitations . First , after an intensive search , we only retrieved 127 ( 84% ) of the population that we intended to study . Of the 24 subjects that were lost to follow-up , we could not detect features that might have introduced bias , but we cannot completely rule out that some bias was introduced . Next , although the group that was earlier exposed to 4 weeks of streptomycin had ( near ) normal audiograms , we did not attempt to enroll a matched control group that had not had streptomycin injections . Also , variability between measurements is likely to be higher than desirable , due to high levels of AN , although average levels of AN were only slightly higher compared to accepted bedside testing levels in the USA [34] , [35] . This implies that the absolute numbers of hearing loss should be interpreted with caution , but as AN levels were similar for all measurements , it is not likely to have influenced the differences found between the two treatment arms . The strength of our findings is that we could detect a difference between groups that appeared well matched . With known baseline characteristics , with a well-documented increased exposure to streptomycin , we conclude that prolonged streptomycin administration in the adult study subjects caused significant hearing loss , especially in the high frequency range . In addition , we obtained ototoxicity data from a patient sample that included both adults and children suffering from the same illness and receiving the same treatment , with differential effects in terms of toxicity . Our study results support ongoing attempts to try replacing injected streptomycin by an alternative , e . g . clarithromycin . Currently a trial to compare standard care ( streptomycin plus rifampicin ) with oral treatment consisting of clarithromycin in extended release formulation combined with rifampicin is ongoing [36] . We conclude that streptomycin with cumulative dosages , especially in patients aged 16 or older should be given with caution , especially in individuals with concurrent risks for renal dysfunction or hearing loss . | Buruli Ulcer is an infectious skin disease , mainly occurring in West Africa . Previously , the disease was treated exclusively by surgery , but in the last decade , effective treatment with antibiotics has been established . The WHO recommended regimen consists of 8 weeks of oral rifampicin combined with intramuscular streptomycin . However , prolonged use of streptomycin is known to cause permanent ototoxicity and transient nephrotoxicity . To study this , we performed audiometry and measured the serum creatinine in 127 former Buruli ulcer patients who received either 4 or 8 weeks of streptomycin 4 to 6 years ago . Ototoxicity was present in 29% of adults and 25% of children . Adults who received 8 weeks of streptomycin had significantly worse hearing at long term follow-up , and this was most prominent in the high frequencies . In children , no differences between the two groups were found . Nephrotoxicity that had been detected in 14% of adults and in 13% of children during treatment , was present in only 2 . 4% of patients at long term follow-up . The findings indicate that caution should be exercised when prescribing streptomycin to adults for prolonged periods of time . Treatment regimens for Buruli ulcer that do not contain streptomycin are desirable and should be investigated . | [
"Abstract",
"Introduction",
"Materials",
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"Results",
"Discussion"
] | [
"medicine",
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"diseases",
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] | 2014 | Long Term Streptomycin Toxicity in the Treatment of Buruli Ulcer: Follow-up of Participants in the BURULICO Drug Trial |
Dengue infection is endemic in many regions throughout the world . While insecticide fogging targeting the vector mosquito Aedes aegypti is a major control measure against dengue epidemics , the impact of this method remains controversial . A previous mathematical simulation study indicated that insecticide fogging minimized cases when conducted soon after peak disease prevalence , although the impact was minimal , possibly because seasonality and population immunity were not considered . Periodic outbreak patterns are also highly influenced by seasonal climatic conditions . Thus , these factors are important considerations when assessing the effect of vector control against dengue . We used mathematical simulations to identify the appropriate timing of insecticide fogging , considering seasonal change of vector populations , and to evaluate its impact on reducing dengue cases with various levels of transmission intensity . We created the Susceptible-Exposed-Infectious-Recovered ( SEIR ) model of dengue virus transmission . Mosquito lifespan was assumed to change seasonally and the optimal timing of insecticide fogging to minimize dengue incidence under various lengths of the wet season was investigated . We also assessed whether insecticide fogging was equally effective at higher and lower endemic levels by running simulations over a 500-year period with various transmission intensities to produce an endemic state . In contrast to the previous study , the optimal application of insecticide fogging was between the onset of the wet season and the prevalence peak . Although it has less impact in areas that have higher endemicity and longer wet seasons , insecticide fogging can prevent a considerable number of dengue cases if applied at the optimal time . The optimal timing of insecticide fogging and its impact on reducing dengue cases were greatly influenced by seasonality and the level of transmission intensity . We suggest that these factors should be considered when planning a control strategy against dengue vectors .
Dengue virus ( DENV ) infection is a mosquito-borne viral disease of serious health concern in recent decades . More than two-fifths of the global population is considered to be at risk of dengue infection , principally in the tropics and sub-tropics [1] . Increases in dengue epidemics are likely due to the rapid and broad-ranging migration of people and urbanization , which is accompanied by expanded infestation of vector mosquito: Aedes aegypti [2] . Clinical manifestations of dengue infection range from a mild febrile form ( dengue fever: DF ) to severe and sometimes to fatal forms ( dengue hemorrhagic fever: DHF and dengue shock syndrome: DSS ) . Although the case fatality rate of DHF/DSS has been declining [3] , such severe forms always require intensive care and fluid management under hospitalization . Consequently , a major outbreak represents a serious burden on medical facilities . Tetravalent dengue vaccines are now under development and have the potential to effectively prevent disease [4]; however , these vaccines are not currently approved for clinical use . To date , vector control has been the only measure for dengue prevention . In contrast to the substantial progress observed for vaccine development , vector control strategies have shown limited improvement . The major vector control measures conducted in many dengue endemic areas include: 1 ) fogging ultra-low-volume insecticide particles ( insecticide fogging ) that target adult mosquitoes; 2 ) chemical and biological controls for mosquito larvae in the key containers; and 3 ) larval source reduction . Among those , insecticide fogging has been commonly implemented , but its impact on reducing dengue cases is still controversial [5] , [6] . Aedes aegypti is a highly domesticated species that tends to rest in locations hidden indoors , making it hard for insecticide to reach adult mosquitoes [6] . Appropriate timing for insecticide application is also under discussion . Fogging in and around the houses of detected dengue cases is recommended by the World Health Organization during the early phase of a disease outbreak , and is practiced in many endemic areas [7] . However , it has been suggested that fogging following case detection is not conducted early enough to prevent virus transmission occurring across a wider area [7] , [8] . In recent years , “in-advance” treatment has been proposed . Fogging is sometimes conducted very early in , or even before , the rainy season [9] , however the rationale for such in-advance treatment has yet to be established . Newton and Reiter ( N&R ) [10] reported that based on a mathematical simulation , the strongest effect of insecticide fogging in preventing dengue cases is expected when insecticides are applied several days after the prevalence peak; however , this method had little impact on disease prevention , with only 6 . 8% of the cases prevented . Many other researchers have referred to this study as evidence of the ineffectiveness of insecticide fogging [11]–[13] . However , the basic assumptions of the N&R model were oversimplified when compared with the real situation in dengue endemic areas . For example , the N&R model did not take into account seasonal fluctuations in climatic conditions , which influence vector population dynamics and viral development within vectors . In addition , the human population was assumed to be completely naïve to DENV , and the magnitude of the outbreak in their simulation was very large , which resulted in 7 , 651 people out of 10 , 000 being infected in an outbreak [11] . This phenomenon might be observed in specific situations , like the first dengue outbreak in Easter Island [14] , but would not apply to areas where dengue infections are already endemic . Population immunity is also likely to widely vary in endemic regions . For example , 100% of Nicaraguan children at the age of 16 are seropositive for at least one of the DENVs [15] , whereas only 6 . 5% of junior high school children in Singapore have been exposed to these viruses [16] . Although dengue is endemic in both countries , the transmission intensity appears to be much higher in Nicaragua , resulting in higher immunity levels compared with Singapore . When assessing the current dengue situation , seasonality and transmission intensity are critical determinants of epidemic patterns that should be taken into consideration when evaluating and optimizing the impact of insecticide fogging . Some studies suggested that the optimal timing and the impact of insecticide fogging might differ from results reported by N&R when also considering seasonality [12] , [17] . However , the most appropriate time for insecticide fogging to effectively prevent dengue incidence was not definitively provided in these studies . Thus , we aimed to identify the optimal timing for insecticide fogging and its impact on reducing cases of DENV infection by using a mathematical simulation model of dengue transmission dynamics that included various seasonal settings and transmission intensities .
We used the structure of the N&R model [10] and partly modified it to: 1 ) add seasonality and 2 ) produce the endemic state . Equations are presented below . Host population was divided into Sh ( susceptible ) , Eh ( exposed ) , Ih ( infectious ) and Rh ( recovered ) . Vector population was also divided into Sv ( susceptible ) , Ev ( exposed ) and Iv ( infectious ) . Host population ( 1 ) ( 2 ) ( 3 ) ( 4 ) Vector population ( 5 ) ( 6 ) ( 7 ) Parameters and parameter values are shown in Table 1 . Fogging was applied each day from the 1st to the 365th day of the year , during which time , the wet season was assumed to occur at the beginning of the year . The annual number of infected cases was calculated at each application and the day when fogging resulted in a maximum reduction of dengue cases was defined as the optimal day for fogging . The simulation was conducted numerically with a time-step of one hour using Microsoft Excel .
Our results were very similar to those obtained for N&R's simulation ( Table 2 ) . The maximum reduction in cases was observed when fogging was conducted 6 days after the prevalence peak; however this reduction only amounted to 6 . 7% of the total cases ( Fig . 1A ) . The epidemic magnitude was smaller when the wet season was shorter ( Table 2 ) . Dengue incidence generally increased exponentially during the wet season ( Fig . 1B ) , and started to decline rapidly within the few days after the onset of the dry season , during which time , climatic conditions for mosquitoes are unfavorable . Our results showed that the optimal day for fogging was earlier than in Simulation 1 for all wet season durations assessed . The proportion of prevented cases was greater during a shorter wet season ( Table 2 ) . In the endemic state , the yearly number of cases was much smaller than that observed in Simulation 1 and 2 ( Table 2 , Fig . 1C ) . Optimal timing of fogging shifted to a much earlier time than in Simulation 2 , and more than 40% of the cases were prevented during the wet season of any length . Population immunity level also increased with an increase in MPP and the length of wet season ( Table 1 ) . In the lower endemic situations ( MPP = 3 and 5 ) , a maximum reduction in cases was observed between 81 and 116 days before the prevalence peak , and over 40% of cases were mostly prevented during the wet season of any length . In the higher endemic situations ( MPP = 8 and 15 ) , a maximum reduction in cases was also observed earlier than the prevalence peak . The proportion of prevented cases was 35 . 9–39 . 6% , which was slightly lower than a MPP of 2 , 3 and 5 . Overall , the most effective time for insecticide fogging was early in the wet season , when over 35% of the cases were prevented at any transmission intensity level . The greatest impact of fogging was observed during shorter wet seasons and for lower transmission intensities . The proportion of cases prevented by fogging on each day of the year is shown in Fig . 2 , and the green area indicates the greatest proportion of cases prevented ( >40% ) . The proportion of prevented cases at and after the prevalence peak was not optimal in any settings for an endemic situation .
We successfully developed a model for predicting the most optimal time for insecticide fogging against dengue mosquitoes , which will potentially help reduce the number of dengue cases in endemic regions of the world . By including additional parameters , such as seasonality and disease transmission rates , our model more accurately depicted epidemic outbreaks when compared with the previously published model . Our simulation results for a naïve population with no seasonal setting were similar to those obtained with the N&R model [10] . The greatest reduction of dengue cases was observed when fogging was conducted several days after the prevalence peak , but the impact was minimal . When climatic conditions are favorable for mosquitoes throughout the year , insecticide fogging only slows down the epidemic curve temporarily even if implemented intensively . After fogging , mosquito populations recover rapidly and transmit DENV to susceptible people . Dengue incidence subsequently continues to increase until population immunity reaches a level at which the recovery rate exceeds the new infection rate . In such a situation , fogging reduced dengue cases when conducted after the prevalence peak by accelerating the natural decline of the epidemic . When we considered seasonality , the results were completely different . The optimum timing for insecticide fogging shifted earlier than the prevalence peak; because it interferes with the exponential epidemic growth at a certain point and prevents the prevalence peak from reaching the original level by the end of the wet season ( Fig . 1B ) . Furthermore , when considering both endemicity and seasonality , the optimum timing for insecticide fogging shifted to an earlier time and the proportion of prevented cases was greater . The period of greatest prevention was observed relatively early in the wet season ( Fig . 2 , in green ) . DENV has four different serotypes that simultaneously circulate in most dengue endemic countries . Such co-circulation of multiple serotypes greatly influences long-term epidemic patterns . We additionally evaluated the optimal timing of insecticide fogging by including the co-circulation of four serotypes in endemic situation . The results indicated that the optimal application was also between the onset of the wet season and the prevalence peak ( results are shown in Text S1 ) . Therefore , we suggest that hyperendemicity did not affect our findings . Our model however , does have some limitations when applying simulations to actual dengue endemic areas , due to the simplification of parameters to understand the overall effects of insecticide fogging . First , our assumption of seasonal change was represented by two different values of mosquito lifespan , which was too simple to describe real seasonal dynamics . However , as we aimed to provide a practical strategy for determining optimal insecticide fogging in general , we prioritized the model simplicity and clearly distinguished the on and off-dengue seasons . Various biological features may fluctuate seasonally and affect dengue epidemics . However , when a year can be divided into the on and off-dengue seasons , temporary reduction of adult mosquito population by fogging in the middle of the on-dengue season would delay epidemic growth and prevent cases ( Fig . 1B ) . Thus , we considered that our simple setting for seasonality can typically represent more complex dynamics in the real world . Second , since our model was derived from the N&R model [10] , and because we aimed to directly compare our simulations with their conclusion , we set the mosquito lifespan assumption to be identical to this previous study . This was originally obtained from a field study carried out in Thailand ( four days in the wet season ) [21] . In general , the lifespan of Ae . aegypti in the field is estimated to be slightly longer than our assumption: 5 . 3–9 . 1 days [22] . However , the low vector survival rate in our model did not affect our conclusion because when we simulated with a 10 day lifespan in the wet season and 7 . 5 day lifespan in the dry season , the optimal timing of fogging was also between the beginning of the wet season and the prevalence peak ( results not shown ) . Third , our model did not consider spatial heterogeneity . In our settings , the “in advance” treatment did not appear to be the most effective strategy if implemented too early ( Fig . 2 ) . However , incase vector populations survived the dry season in limited areas and expanded the distribution range gradually in the wet season , in-advance focal fogging targeting those areas might be the optimal strategy to reduce the first generation of mosquitoes in the season . Our study also analyzed the effect of insecticide fogging on preventing total cases in a single year , but not the effect on longer-term total cases . When insecticide fogging prevented many cases , it also reduced immunity in the host population . Consequently , the susceptible population would potentially cause even larger epidemics in subsequent years . We should therefore carefully foresee and take action between epidemics after applying insecticide fogging . Furthermore , when insecticide treatment was routinely conducted every year , we should have also considered the development of insecticide-resistance in the vector population [23] , which was not included in our model . As insecticide resistance has already become a serious problem in many dengue endemic countries [24] , [25] , it is important to carefully consider which insecticides can effectively reduce mosquitoes in the target areas on the basis of biological evidence . The spraying method used to allow the insecticides to reach mosquitoes also requires further investigation . Although our results may not show the best strategy for the long-term prevention of dengue epidemics , they should be interpreted as the optimal strategy for the non-regular emergency treatment during major epidemics . Despite these limitations , our model has a clear practical significance for dengue control in regions where this disease has been endemic for a long time and its epidemic pattern is affected by seasonal climate factors . The optimal timing of insecticide fogging to reduce dengue incidence most effectively is between the onset of the wet season and the prevalence peak , rather than waiting until the peak of a major outbreak occurs . | Dengue virus infection is a serious infectious disease transmitted by Aedes mosquitoes in the tropics and sub-tropics . Disease control often involves the use of insecticide fogging against mosquito vectors . However , the effectiveness of this method for reducing dengue cases , in addition to appropriate application procedures , is still debated . The previous mathematical simulation study reported that insecticide fogging reduces dengue cases most effectively when applied soon after the epidemic peak; however , the model did not take into account seasonality and population immunity , which strongly affect the epidemic pattern of dengue infection . Considering these important factors , we used a mathematical simulation model to explore the most effective time for insecticide fogging and to evaluate its impact on reducing dengue cases . Simulations were conducted with various lengths of the wet season and population immunity levels . We found that insecticide fogging substantially reduces dengue cases if conducted at an appropriate time . In contrast to the previously suggested application time during the peak of disease prevalence , the optimal timing is relatively early: between the beginning of the dengue season and the prevalence peak . | [
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] | 2011 | Optimal Timing of Insecticide Fogging to Minimize Dengue Cases: Modeling Dengue Transmission among Various Seasonalities and Transmission Intensities |
The mammalian ortholog of yeast Slx4 , BTBD12 , is an ATM substrate that functions as a scaffold for various DNA repair activities . Mutations of human BTBD12 have been reported in a new sub-type of Fanconi anemia patients . Recent studies have implicated the fly and worm orthologs , MUS312 and HIM-18 , in the regulation of meiotic crossovers arising from double-strand break ( DSB ) initiating events and also in genome stability prior to meiosis . Using a Btbd12 mutant mouse , we analyzed the role of BTBD12 in mammalian gametogenesis . BTBD12 localizes to pre-meiotic spermatogonia and to meiotic spermatocytes in wildtype males . Btbd12 mutant mice have less than 15% normal spermatozoa and are subfertile . Loss of BTBD12 during embryogenesis results in impaired primordial germ cell proliferation and increased apoptosis , which reduces the spermatogonial pool in the early postnatal testis . During prophase I , DSBs initiate normally in Btbd12 mutant animals . However , DSB repair is delayed or impeded , resulting in persistent γH2AX and RAD51 , and the choice of repair pathway may be altered , resulting in elevated MLH1/MLH3 focus numbers at pachynema . The result is an increase in apoptosis through prophase I and beyond . Unlike yeast Slx4 , therefore , BTBD12 appears to function in meiotic prophase I , possibly during the recombination events that lead to the production of crossovers . In line with its expected regulation by ATM kinase , BTBD12 protein is reduced in the testis of Atm−/− males , and Btbd12 mutant mice exhibit increased genomic instability in the form of elevated blood cell micronucleus formation similar to that seen in Atm−/− males . Taken together , these data indicate that BTBD12 functions throughout gametogenesis to maintain genome stability , possibly by co-ordinating repair processes and/or by linking DNA repair events to the cell cycle via ATM .
SLX1 and SLX4 were identified , together with MUS81 and MMS4 ( Eme1 in mammals ) , in a S . cerevisiae screen for genes required for the viability of sgs1-deficient cells [1] . Slx1 is the founding member of a family of proteins with a predicted URI nuclease domain whose activity is enhanced 500-fold by its interaction with Slx4 [2] . Slx4 can also form complexes with Rad1-Rad10 [3] , [4] to effect DSB repair during single-strand annealing in yeast [3] , [5] . However , Slx4 can also act independently of both Slx1 and Rad1-Rad10 [2] , [6] , and is phosphorylated by Mec1 and Tel1 , the yeast orthologs of ATR and ATM , respectively , in response to DNA damage [4] . Human , C . elegans and D . melanogaster orthologs of SLX4 were described recently [7]–[10] and named BTBD12 ( for BTB domain-containing protein-12 ) , Him-18 , and Mus312 , respectively . These proteins are considerably diverged from their yeast counterpart; BTBD12 encodes a 1834 amino acid protein , approximately 2 . 5-times larger than the yeast protein , and resembles its lower eukaryotic orthologs mostly in its C-terminal SAP and CCD domains [7] . Like the yeast ortholog , the human protein is a substrate of the ATM/ATR kinases [11] and its depletion also results in DNA damage sensitivity [8] . Recently , a subset of Fanconi anemia ( FA ) patients were found to have biallelic mutations in BTBD12 , making this gene a novel complementation group for this disorder [12] . A complex of BTBD12 and SLX1 displays robust Holliday Junction ( HJ ) resolvase and 5′ flap endonuclease activity in vitro , and mammalian BTBD12 also binds to , and enhances the activity of , several DNA repair proteins including MUS81 [7] , [10] and the MSH2-MSH3 heterodimer of the DNA mismatch repair ( MMR ) family [8] , suggesting a role for this protein as a docking platform for structure-specific endonucleases . Recent reports in D . melanogaster and C . elegans indicate that the Btbd12 orthologs , Mus312 and Him-18 , respectively are essential for normal meiotic progression [13] , [14] . In addition , Him-18 appears to function pre-meiotically in the germ line , being required for repair at stalled replication forks [13] , suggesting that Him-18 functions throughout germ cell development to maintain genomic integrity . Given these data , the primary goal of the current studies was to understand the function of BTBD12 in the germ line of mice , with the hypothesis that BTBD12 may be critical for the processing of homologous recombination intermediates , whether as the result of replication errors during pre-meiotic proliferation , or during the repair of double strand breaks ( DSBs ) that underlie meiotic recombination . For the latter , our studies were aimed at investigating the role of BTBD12 in the regulation of meiotic recombination events during prophase I in mammals , particularly those that ensure accurate segregation of maternal and paternal chromosomes at the first meiotic division . Principal amongst these is the formation of DNA crossovers between the homologs , as initiated by DSB induction through the activity of the SPO11 endonuclease [15] , [16] . The resolution of DSBs can be achieved through the recruitment of various repair pathway complexes , to produce crossovers ( CO ) or noncrossovers ( NCO ) . The fact that CO formation occurs with tight precision , coupled with the observation that only a small subset of total DSBs will become COs , suggests that orchestration of DSB repair events , and the various repair pathways that give rise to either CO or NCO events , is highly regulated at the molecular level ( reviewed by [17] ) . Two pathways have been defined for CO formation , the first involving the so-called Class I or “ZMM” pathway ( for ZIP3 , MSH4/5 and MER3 ) , and the second , Class II pathway , involving the Mus81 endonuclease , which functions as a heterodimer with EME1 ( Mms4 in yeast ) [18]–[21] . In S . cerevisiae , and possibly in the mouse , this Class II pathway appears to be restricted to a subset of DSBs that may be aberrant in structure and/or that may be processed initially by the RecQ helicase , Sgs1/BLM ( yeast/mouse ortholog; [20]–[23] ) . These aberrant DSB repair intermediates ( or joint molecules , JMs ) include a variety of structures that result from secondary strand invasion events , usually involving independent activities of each end of the DSB , and/or from closely spaced DSBs . These aberrant JMs have been demonstrated biochemically in S . cerevisiae [20]–[23] , but not yet in mammals . Under wildtype conditions in budding yeast , Sgs1 can disassemble and/or process many of these aberrant DSB repair intermediates towards NCO or Class I CO fates [20] , [21] . However , a small proportion of them cannot be processed in this manner and thus become the target of MUS81-driven crossing over . In the mouse , the class I pathway accounts for some 90–95% of COs , and the major intermediate marker for these events is the accumulation , in pachynema , of the MutL homolog heterodimer , MLH1 and MLH3 [24]–[27] . The remaining events are processed via the MUS81-dependent Class II pathway [27] , [28] . Interestingly , however , our studies in mouse have demonstrated that the loss of Mus81 results in the recruitment of 5–10% additional MLH1/MLH3 foci during pachynema , and that these additional foci may act to maintain CO rates at normal levels in the absence of MUS81 [28] . This suggests that a unique , possibly mammalian-specific , level of integration exists between the two crossover pathways . Importantly , BTBD12 interacts with many of the key players in both CO pathways , including BLM ( reviewed by [29] ) , leading us to hypothesize that BTBD12 may functionally integrate the different CO pathways during mammalian meiosis . We obtained a mouse line from the European Conditional Mouse Mutagenesis Program ( EUCOMM ) , harboring a Frt-flanked βGeo cassette upstream of LoxP-flanked exon 3 of Btbd12 gene , and as initially described by Crossan et al [30] . We call this conditional genetrap allele Btbd12βGeoFlox . In wildtype mice , BTBD12 protein localizes to spermatogonial and spermatocyte populations of the adult testis and is dramatically down-regulated at these sites in the mutant animals . Weak staining against BTBD12 persists , however , in the Btbd12βGeoFlox/βGeoFlox testes , but the protein fails to be recruited to meiotic chromosome cores to detectable levels . Despite residual protein , the mice are sub-fertile as a result of a reduced spermatogonial population coupled with failure to progress normally through meiotic prophase I resulting in less than 15% normal numbers of spermatozoa . Importantly , BTBD12 localization to meiotic chromosomes appears to be dependent on ATM , since the presence of BTBD12 protein is lost in Atm−/− males , and Btbd12βGeoFlox/βGeoFlox mice show genomic instability similar to that seen in the absence of ATM . Taken together , our data suggest that BTBD12 is a key substrate of ATM in the mammalian germ line , playing a role in the spermatogonial stages of gametogenesis , as well as in the entry into , and progression through , prophase I .
Wildtype testis sections were stained with a commercial anti-BTBD12 antibody from Novus Biologicals that was raised against amino acids 1650–1700 of human BTBD12 at the c-terminus of the protein ( Figure 1A , 1B ) . BTBD12 is expressed ubiquitously in the mouse , and is found in both adult testis and fetal ovary [8] . In adult testis of wildtype mice , the protein was found to localize strongly to spermatogonia and spermatocytes ( Figure 1A , black arrowheads and arrows , respectively ) . At earlier stages of spermatogenesis , prior to entry into meiosis , the signal for BTBD12 protein appears to localize through the nucleus , with increased intensity of signal around the nuclear periphery . Upon entry into prophase I , the BTBD12 signal becomes more punctate in nature , associating with the increasingly condensed chromatin , but continuing to occupy the majority of the nuclear space . Additionally , BTBD12 protein localized to bivalent chromosomes on the meiotic spindle during metaphase of meiosis I ( Figure 1A , white arrowheads and inset panel ) . BTBD12 localization was evaluated on chromosome spread preparations of prophase I spermatocytes . In all chromosome “spread” preparations presented herein , we utilize the major protein component of the axial element of the synaptonemal complex ( SC , the meiosis specific structure that assembles along and between each homologous chromosome pair ) , SYCP3 , to visualize meiotic chromosome cores . We utilized an affinity-purified antibody that targets amino acids 1–350 of murine BTBD12 ( antibody “NT” ) . The chromosome spreads from wildtype adult males ( Figure 1 Ci-vi ) showed accumulation of BTBD12 protein in a punctate staining pattern along the chromosome cores in zygonema ( Figure 1 Cii ) , becoming more intense by pachynema ( Figure 1Ciii ) . In some cases , increased staining was observed within the sex body ( 94% of wildtype cells having BTBD12 in this domain , compared to 0% in the Btbd12 mutants; n of 33 and 31 , respectively ) , in line with the fact that BTBD12 is a target of ATM , which plays a significant role in the formation of this sub-nuclear domain during mid-prophase I [31] , [32] . One autosomal chromosome from a pachytene cell of each genotype is shown , enlarged , with the BTBD12 staining offset from the SYCP3 staining to facilitate visualization of the punctate pattern of the Btbd12 signal ( Figure 1Cvi ) . By diplonema , BTBD12 was no longer present on the cores , but remained associated with the centromeres ( Figure 1Civ ) , and had disappeared from the nucleus by diakinesis ( Figure 1Cv ) . The Btbd12βGeoFlox allele contains an Frt-flanked βGeo insertion into the Btbd12 locus upstream of LoxP-flanked exon 3 . The expectation , therefore , is that this allele would act as a gene trap , reducing expression of wildtype Btbd12 mRNA . Consistent with this , in the Btbd12βGeoFlox/βGeoFlox mutant testis sections , we observe weak BTBD12 protein staining persisting in some cells , particularly those close to the basal membrane of the tubules , which are presumptive spermatogonia ( Figure 1B , red arrows ) . This persistent staining , however , was much fainter than that seen in the wildtype testis sections , indicating a significantly lower abundance of BTBD12 protein in the mutant animals , and suggesting a splicing event around the βGeo cassette to produce BTBD12 protein . Importantly , there was no staining evident on metaphase chromosomes in the Btbd12βGeoFlox/βGeoFlox testes . In spermatocytes from Btbd12βGeoFlox/βGeoFlox mutant littermates , BTBD12 did not appear to localize to chromosome cores with as intense a signal as in wildtype cells ( Figure 1Di-vi ) , although faint staining was observed at pachynema in the Btbd12βGeoFlox/βGeoFlox mutants . This staining , however , was barely detectable above background , even at higher magnifications ( Figure 1Diii and vi ) . Thus , the persistent BTBD12 signal observed in testis sections from Btbd12βGeoFlox/βGeoFlox males is not associated with prophase I chromosome cores , or is present at levels that are undetectable on chromosome spreads . To confirm the presence of BTBD12 protein in wild type testes , and to explore the status of BTBD12 protein in the homozygous mutant animals , western blots were performed using whole testis protein extracts from adult and juvenile animals ( Figure 2 ) . Two antibodies were utilized , one C-terminal and one N- ( not shown ) terminal , as described above . Both antibodies produced a band of similar size in protein extracts from wild type adult testis ( lane 1 ) , with decreasing amounts observed in Btbd12βGeoFlox/+ heterozygotes ( lane 2 ) and further reduced expression observed in Btbd12βGeoFlox/βGeoFlox mutant protein extracts ( lane 3 ) . Quantitation of BTBD12 protein levels in testis extracts from all three genotypes revealed a decrease from wildtype levels of 23 . 5% and 45 . 6% for heterozygotes and homozygous mutant samples , respectively ( Figure 2 graph ) . The presence of residual 130 kDa protein in the homozygous mutant animals is in line with the persistent protein signal observed in immunohistochemical staining of testis sections from these animals ( Figure 1B ) , and suggests that the mutant allele is transcribed at a reduced level compared to the wildtype allele . As expected , the lanes containing Btbd12βGeoFlox/+ and Btbd12βGeoFlox/βGeoFlox protein ( lanes 2 and 3 in Figure 2 ) also show positive staining for β-galactosidase at approximately the same size . No β-galactosidase band is observed in the wildtype testis protein lane . Taken together , these results show that the BTBD12 protein present in testis extracts from Btbd12βGeoFlox/+ and Btbd12βGeoFlox/βGeoFlox males encompasses some portions of both the N-terminus and C-terminus of the protein , and most likely arises as a result of splicing around the FRT-flanked cassette , but that expression of this protein is dramatically reduced by the presence of the βGeo cassette . Moreover , βGeo cassette itself is also transcribed from the targeted allele , either as a distinct protein and/or as a fusion with BTBD12 . We favor the former option given that the size of the BTBD12 and βGeo bands are similar . Btbd12βGeoFlox/βGeoFlox mutant mice are viable and are born at slightly lower than expected Mendelian rates . Out of a total of 395 pups born in 65 litters from heterozygote breedings , 66 of them were mutants , compared with an expected frequency of 98 . 75 ( Table S1 ) . These data are significantly different , at the p<0 . 05 level , from the expected percentages mutant or wildtype numbers ( P = 0 . 012 , χ2 test ) . This rate is slightly higher than that seen in the previous report describing these mice [30] . Occasionally we observe that mutants exhibit lower birth weights than their wildtype litter mates ( not shown ) but , by adulthood , these animals have regained weights comparable to their siblings . Thus , Btbd12βGeoFlox/βGeoFlox adult mice were not significantly smaller than wildtype littermates ( average weights 19 . 5 and 21 . 3 g , respectively P = 0 . 25 , unpaired T-test ) but exhibited varying degrees of anophthalmia and microphthalmia from birth . 33 out of 66 ( 50% ) mutants showed defects of one or both eyes . No such deformities were apparent in wildtype litter mates indicating a possible role for BTBD12 in eye development and in line with previous reports describing this EUCOMM mouse line [30] . It is well known that mammalian species with mutations in Atm show an increase in genomic instability ( GIN ) , including defects in cell cycle regulation , sensitivity to DNA damage-inducing agents , and chromosomal aberrations ( reviewed by [33] ) . Since BTBD12 is believed to be a direct target for phosphorylation by ATM/ATR , we assessed both wild type and Btbd12βGeoFlox/βGeoFlox mutant mice for GIN . A micronucleus formation assay was performed on peripheral blood from both Btbd12βGeoFlox/βGeoFlox mutants and wild type littermate controls [34] , [35] . The Btbd12βGeoFlox/βGeoFlox mutant animals showed over a two-fold increase in micronucleus formation compared with wild type mice , which was statistically significant ( Figure S1; p<0 . 0001 ) . This result indicates that these mutants have a high level of GIN in their somatic cells approaching , though not as great , as that seen in Atm null mice [36] . Btbd12βGeoFlox/βGeoFlox mutant mice showed significantly decreased testis size when compared to wildtype littermates ( approximately 25% , Figure 3A , 3B , p<0 . 0001 ) . Sperm counts performed on the two cohorts of mice revealed that Btbd12βGeoFlox/βGeoFlox mice have only about 10% of the amount of epididymal sperm found in wildtype littermates ( Figure 3C ) , and exhibit dramatically reduced fertility , with only 3 litters born to two different Btbd12βGeoFlox/βGeoFlox males over the period of 9 months , with the youngest male to sire a litter being approximately 7 weeks old . Female mutants were sterile , with two mutant females bred to a fertile male , over a period of a year , yielding no pregnancies , while wildtype cage mates produced healthy offspring from the same male . H&E staining of testis sections from both wildtype and Btbd12βGeoFlox/βGeoFlox mutant mice at both 3 and 8 weeks of age ( Figure 3D , 3E , 3H , 3I ) showed that the seminiferous tubules of Btbd12βGeoFlox/βGeoFlox males are extremely variable in their cell density and also in the progression through meiosis . For example , in 8-week old Btbd12βGeoFlox/βGeoFlox mutant mice , neighboring tubules showed almost normal tubule morphology , juxtaposed to almost empty , abnormal tubules ( Figure 3I ) . Immunohistochemical staining with the spermatogonial and early spermatocyte cell marker , GCNA-1 , showed a severe depletion of early germ cells within the tubules of the Btbd12βGeoFlox/βGeoFlox compared with those within the wildtype litter mate mice , at both 3 week and 8 weeks of age ( Figure 3F , 3G , 3J , 3K ) . By contrast , the proliferating cell marker , PCNA , showed a similar staining pattern in the majority of tubules in both wildtype and Btbd12βGeoFlox/βGeoFlox mice ( Figure 3L , 3M ) suggestive of normal progression through spermatogonial divisions and in self-renewal capabilities . The maintainenance of PCNA signal in spite of reduced PGC pool is suggestive of prolonged S-phase . In line with this , we observed increased TUNEL labeling of apoptotic cells in testis sections of Btbd12βGeoFlox/βGeoFlox , mostly during meiosis I . The majority of these cells are undergoing apoptosis at around the time of exit from prophase I , but some also appear to be in mid-prophase ( Figure 3N , 3O , arrowheads ) . In a few instances , some TUNEL-positive cells appear to be in pre-meiotic stages ( Figure 3O , arrows ) , but these are clearly fewer in number than those apoptotic cells in prophase I . Collectively , these results demonstrate a loss of germ cells from the testis of Btbd12βGeoFlox/βGeoFlox males , starting as early as the first wave of meiotic entry within the first three weeks of postnatal life . Given the apparently normal proliferative capacity of spermatogonia in testes of 8-week old Btbd12βGeoFlox/βGeoFlox mice , we questioned why the tubules of 3 week-old mice were so heterogeneous with respect to cellular density . If a failure of spermatogonial proliferation is not the cause of the lack of cellularity of certain tubules in the Btbd12βGeoFlox/βGeoFlox males , then a second possibility is that the testis of these mice fail to be populated with appropriate numbers of spermatogonial precursors , known as pro-spermatogonia or gonocytes , during development . To investigate this option , testes were obtained for both wildtype and Btbd12βGeoFlox/βGeoFlox males between embryonic ( e ) day 18 and day 3 post-partum ( pp ) , and prospermatogonia were visualized with antiserum against GCNA-1 . In wildtype males , the G0-arrested prospermatogonia population is established around embryonic day 12 . 5–16 . 5 following migration of the primordial germ cells ( PGC ) to the genital ridge [37] , [38] , the exact timing being somewhat controversial , and remain quiescent until just prior to birth [39] . During the period between e18 and day 3pp , a large number of prospermatogonia are lost by apoptosis . During that time , approximately 1 to 7 prospermatogonia may be observed within the seminiferous cords of the developing testis , and these cells then start to proliferate from day 4 pp onwards [39] . In Btbd12+/+ males , these large round cells appeared separated from the basement membrane by the Sertoli cells , which are more columnated in appearance , and they stained readily with numerous markers including GCNA-1 ( Figure 4A–4C , brown cells ) and mouse Vasa homolog ( MVH; not shown ) from e16 onwards . By day 3 pp , every testis cord section contains on average 2 . 08±0 . 19 prospermatogonia per cord , representing a range of 1 to 10 cells ( Figure 4C , 4M; Table S2 ) , having declined dramatically at around the time of birth . In Btbd12βGeoFlox/βGeoFlox males at e16 , normal numbers of GCNA-1 positive prospermatogonia are observed ( Figure 4D ) , but by e18 , their numbers have declined significantly ( Figure 4E , 4M; Table S2 ) . By d3 pp , the majority of testis cords contain no prospermatogonia , with a mean prospermatogonia content of 0 . 92±0 . 28 ( Figure 4F , 4M; Table S2 ) . The Sertoli cell populations in both Btbd12+/+ and Btbd12βGeoFlox/βGeoFlox males appeared normal throughout ( Figure 4B , 4D , 4F ) . In line with this reduced cellularity within the testicular cords , we observed a marked increase in apoptosis , as measured by TUNEL labeling of testis sections from wildtype ( Figure 4G–4I , 4N ) and mutant ( Figure 4J–4L , 4N , arrows ) animals , particularly at e16 and e18 ( Figure 4J , 4K , 4N; Table S2 ) . These data demonstrate that the population of spermatogonia within the testes of Btbd12βGeoFlox/βGeoFlox males is markedly lower than that seen in wildtype as a result of early loss of these cells after arriving at the genital ridge , and suggesting that the proliferation of PGCs in the developing testis is dependent on BTBD12 . To examine meiotic prophase I progression , chromosome spreads from both Btbd12βGeoFlox/βGeoFlox and wildtype spermatocytes were stained with antibodies against SYCP3 and γH2AX ( Figure 5A–5H ) , as a marker for DNA DSBs during prophase I . γH2AX accumulated on leptotene spermatocytes similarly in both wildtype and Btbd12βGeoFlox/βGeoFlox cells ( Figure 5A , 5E ) , demonstrating the appearance and processing of DSB events . By zygonema , however , γH2AX localization began to diminish on the chromosome cores of wildtype spermatocytes ( Figure 5B ) , coincident with the onset of DSB repair processes . By pachynema , and into diplonema , γH2AX localization was only restricted to a strongly-stained domain coincident with the sex body ( Figure 5C , 5D and [40] , [41] ) . By contrast , γH2AX staining is apparent at zygonema in the Btbd12βGeoFlox/βGeoFlox spermatocytes ( Figure 5F ) , but persists along the autosomes well into pachynema , at which time this staining is limited to the sex body in wildtype cells ( Figure 5C , 5G; ) . γH2AX staining remains apparent well into diplonema in the Btbd12 mutants ( Figure 5D , 5H; Figure S2 ) . To investigate the progression of DSB repair , we assessed RAD51 distribution along SCs during prophase I . Specifically , we were interested in observing persistence of RAD51 signal in Btbd12βGeoFlox/βGeoFlox spermatocytes as an indication of unrepaired , or delay in repair of , DSBs . As expected , we observed progressive loss of RAD51 from SCs in wildtype spermatocytes entering pachynema ( Figure 5I; Table S3 ) , whereas RAD51 focus numbers remained elevated through pachynema in Btbd12βGeoFlox/βGeoFlox spermatocytes ( Figure 5M; Table S3 ) . This difference between wildtype and mutant spermatocytes in terms of pachytene RAD51 foci ( means of 26 . 2 and 45 . 1 , respectively ) was statistically significant . The BRCT domain-containing protein , TOPBP1 , functions in replication and DNA damage checkpoint processes , and in meiosis , it localizes to sites of DNA damage in response to DSBs [42] , [43] . TOPBP1 is also known to be required for ATR binding/activation in a number of organisms [44]–[46] and , along with ATM/ATR kinases , may be a part of the machinery that monitors recombination during prophase I and activates the meiotic checkpoint . Indeed , TOPBP1 localizes exclusively to sites of SPO11-induced DSB , as demonstrated by co-localization with γH2AX [47] . Despite the massive increase in γH2AX staining , Btbd12βGeoFlox/βGeoFlox spermatocytes showed no difference in the TOPBP1 localization pattern compared to that seen in chromosome spreads from wild type spermatocytes ( Figure 5J–5P ) . TOPBP1 accumulated on synapsed chromosomes during zygonema , and gradually decreased until it remained only at the sex chromosomes during pachynema , indicating that this signaling pathway is not affected by the loss of BTBD12 from the chromosome cores , in contrast to the persistent RAD51 observed on SCs from Btbd12βGeoFlox/βGeoFlox spermatocytes . MLH1 and MLH3 localization was used to examine the progression of DSB repair events via the “ZMM” , Class I CO pathway ( Figure 5Q–5T ) , which is overseen by key members of the DNA mismatch repair ( MMR ) family: MSH4 , MSH5 , MLH1 and MLH3 . MLH1 and MLH3 form a heterodimer that binds to the MSH4/MSH5 heterodimer in pachynema [48]–[52] . MSH4/MSH5 assemble on DSB repair sites in zygonema in numbers that , in mice at least , far exceed the final tally of chiasmata [53] , [54] . The number of these foci is then pared down through prophase I , but still maintains levels that are approximately two-fold higher than the final chiasmata count [53] , [54] . Association of MLH1/MLH3 with a subset of these sites is thought to stabilize these events , resulting in the resolution of these structures via the class I CO pathway [24]–[26] . In spermatocyte spreads from both wildtype and Btbd12βGeoFlox/βGeoFlox males , MLH1 and MLH3 foci arise at pachynema , at frequencies of 1–2 foci per chromosome , which is comparable to that seen previously in wildtype [25] , [26] . The temporal and spatial dynamics of MLH1 and MLH3 association with the SYCP3-positive chromosome core was similar for wild type and Btbd12βGeoFlox/βGeoFlox spermatocytes ( Figure 5Q–5T ) , suggesting similar progression of class I CO events . When foci were counted and compared between wildtype and mutants , however , we observed a slight , yet statistically significant , increase in foci number for both MLH1 and MLH3 in Btbd12βGeoFlox/βGeoFlox spermatocytes , equating to approximately 1 additional focus per nucleus for each ( Figure 5S , 5T , p = 0 . 0018 for MLH1 and p = 0 . 0389 for MLH3 , unpaired T test ) . For MLH1 , the mean number of foci was 23 . 28 and 25 . 56 for wildtype and Btbd12βGeoFlox/βGeoFlox males , respectively ( Table S3 ) . For MLH3 , the mean number of foci was 24 . 62 and 25 . 57 for wildtype and Btbd12βGeoFlox/βGeoFlox males , respectively . MLH1 foci were also significantly elevated in meiotic spreads from female day e19 embryos , with average MLH1 focus numbers of 22 . 29 and 24 . 00 in wildtype and Btbd12βGeoFlox/βGeoFlox , respectively ( p = 0 . 02 , data not shown ) . The earlier recombination intermediate MSH4 remained the same in both wildtype and Btbd12βGeoFlox/βGeoFlox spermatocytes ( data not shown ) . To assess synapsis , chromosome spreads from Btbd12βGeoFlox/βGeoFlox spermatocytes were stained with an antibody against the central element component , SYCP1 ( Figure 5U–5X ) . Interestingly , at pachynema , approximately 10% of cells harboring the mutant Btbd12 locus showed abnormal synapsis , compared to less than 1% for wildtype cells ( not shown ) , characterized by frequent pairing between more than two chromosomes , incomplete synapsis at pachynema , and synapsis between chromosomes of differing lengths ( Figure 5S–5U ) . In some cases , these synaptic errors appeared to persist into diplonema without first resulting in apoptosis ( Figure 5X ) . To assess the impact of loss of BTBD12 on the first meiotic division , we prepared air-dried diakinesis chromosome spreads and stained them with Giemsa . Chiasmata formation occurred in both wild type ( not shown ) and Btbd12βGeoFlox/βGeoFlox spermatocytes ( Figure 6A ) . The number of diakinesis stage cells was severely depleted in Btbd12βGeoFlox/βGeoFlox mice , suggesting loss of these cells prior to completing prophase I and/or delayed progression through to diplonema . Of the diakinesis cells that we obtained from Btbd12βGeoFlox/βGeoFlox males , however , none showed any changes in chiasmata counts compared to that seen in wildtype litter mates ( Figure 6B ) . To ask whether the number of chiasmata present in Btbd12βGeoFlox/βGeoFlox mutant animals is sufficient to cause appropriate separation of chromosomes during the first meiotic division , an examination of oocytes undergoing metaphase I to anaphase I progression was undertaken . Oocytes from wildtype and Btbd12βGeoFlox/βGeoFlox mice were stained with an antibody against β-tubulin to show the meiotic spindle and DAPI to stain the DNA ( Figure 6C ) . The arrangement of chromosomes on the meiotic spindle in Btbd12βGeoFlox/βGeoFlox mutant oocytes was similar to wild type controls , with 53 cells examined from each genotype . In both cases , occasional cells appear to show one or two misaligned chromosomes , but this occurs at similar rates in oocytes from wild type and Btbd12βGeoFlox/βGeoFlox females ( Figure 6C , arrow; 3/53 cells for both wildtype and mutant ) . Taken together , these results indicate that , despite elevated MLH1/MLH3 focus numbers , chiasmata counts are unaffected in Btbd12βGeoFlox/βGeoFlox mutant animals . Moreover , these chiasmata can , and do , result in normal metaphase I progression , resulting in appropriate chromosome segregation at the first meiotic division . However , the fact that very few diakinesis cells are obtained suggests either a delay in prophase I completion or loss of cells through prophase I prior to diakinesis . BTBD12 was first identified as a potential kinase target of ATM [11] . To investigate the functional interaction between these two proteins , we examined the localization of BTBD12 on meiotic chromosomes in the absence of ATM . When co-immunostaining for BTBD12 and SYCP3 was performed on spread preparations from Atm null spermatocytes , we observed a complete absence of BTBD12 protein on chromosome cores ( Figure 7A ) , compared to the punctate pattern of BTBD12 staining observed on chromosome cores wild type spermatocytes ( Figure 1C ) . TOPBP1 , however , localizes normally to the cores in Atm null cells ( Figure 7B ) in line with the demonstration in budding yeast that Mec1 ( ATR ) activation is dependent on Dpb11 ( TOPBP1 ) , rather than vice versa , and that Mec1 , in turn , mediates only the functional interaction between Slx4 ( BTBD12 ) and Dpb11 ( TOPBP1 ) , rather than regulating directly the localization of TOPBP1 [44] , [55] , [56] . BTBD12 protein is also down-regulated in Atm−/− males , when compared to the wildtype littermate controls , with Atm+/− males showing intermediate levels of BTBD12 protein ( Figure 2 , lanes 4–6 ) . Testis protein extracts from Atm null mice show a decrease in BTBD12 protein to 70 . 3% of that seen in wildtype males , compared to a decrease to 26 . 9% in Atm heterozygotes ( Figure 2 graph ) . Since ATM deletion results in pachytene meiotic failure in mice [57] , [58] , western blots were also performed on juvenile testis extracts at day 17 post-partum to ensure that all protein extracts from Atm+/+ , Atm+/− , and Atm−/− contained equivalent cell populations . Indeed , even with higher proportions of leptotene and zygotene cells present in the day 17 extracts , protein from Atm−/− males showed a depletion of BTBD12 by 67 . 5% compared to Atm+/+ males ( Figure 2 , lanes 7 and 8 ) , with Atm+/−protein extracts showing an intermediate decrease of 43% compared to wildtype levels ( lane 9 ) . Thus , even when taking into account the earlier meiotic failure of Atm−/− males , BTBD12 protein is severely reduced in the absence of ATM .
The results presented herein describe , for the first time , the role of BTBD12 ( SLX4 ) in mammalian gametogenesis . Our data show that BTBD12 plays dual roles in gametogenesis , firstly in facilitating primordial germ cell proliferation and establishment of the spermatogonial pool , possibly by ensuring genome stability , and secondly , in meiotic recombination events . These studies demonstrate that BTBD12 protein localizes to spermatogonia and spermatocytes of the testis . In the latter , BTBD12 is found along chromosome cores during prophase I , accumulating as early as zygonema and persisting through until late pachynema . To explore the role of BTBD12 in mammalian gametogenesis , we obtained the Btbd12βGeoFlox/βGeoFlox mutant mouse line from the European Conditional Mouse Mutagenesis program ( EUCOMM ) . The genetic disruption at the Btbd12 allele results in residual protein that appears on western blots and , to a lesser extent , on immunohistochemical sections . The detected protein could reflect either a truncated fusion protein consisting of BTBD12 and βGeo , or intact BTBD12 protein generated by a splicing event that removes the βGeo cassette , retaining separate and identiable β-galactosidase protein expression . We favor this latter possibility because the BTBD12 protein detected in the mutant animals can be detected with antibodies against either the N- or the C-terminus . Further , cDNA analysis reveals the presence of every exon of the mouse gene in reverse transcribed RNA from Btbd12βGeoFlox/βGeoFlox testis ( data not shown ) . The reduced intensity of BTBD12 signal in the Btbd12βGeoFlox/βGeoFlox testis extracts compared to wildtype extracts indicates that the presence of the βGeo cassette dramatically reduces the efficiency of BTBD12 protein production and/or reduces the stability of the protein . Importantly , the residual BTBD12 protein does not localize appropriately to meiotic chromosome cores , or localizes at levels that are undetectable using standard chromosome spreads , leading us to conclude that the functional activity of BTBD12 is abnormal in these mice , at least in the context of prophase I . Micronucleus formation in Btbd12βGeoFlox/βGeoFlox mice is very much elevated compared to wildtype litter mates , in line with a recent report that first described this mouse line [30] . Crossan et al report that the phenotype of Btbd12βGeoFlox/βGeoFlox mice bears some resemblance to human Fanconi anemia ( FA ) , including blood cell cytopenia and numerous developmental deformaties such as the anophthalmia reported herein . This report is in line with another recent publication demonstrating that human SLX4 mutations are also found in a subset of FA patients [12] . Crossan et al also describe gonadal defects and subfertility in Btbd12βGeoFlox/βGeoFlox mice [30] , but did not explore the origins of these phenotypes . They report that the histological appearance of the testes is consistent with a defect in meiosis , but they did not document pre-meiotic defects in these animals . They did , however , allude to similarities between the testicular phenotype of Btbd12βGeoFlox/βGeoFlox males and that of other FA-associated DNA repair proteins with which BTBD12 interacts , including Ercc1 , Fancd2 , Fancl , and Fanca [59]– . These latter mutations result in spermatogenic phenotypes ranging from spermatogonial proliferation defects , to meiotic defects , to defects in spermatozoa morphology . As will be discussed , results presented herein suggest that the phenotypic defects observed in the testes of Btbd12βGeoFlox/βGeoFlox males may be quite distinct from these other mutations . Given the dramatic loss of testis weight we observe in the adult Btbd12βGeoFlox/βGeoFlox males , coupled with the severe paucity of cells in many seminiferous tubules as early as the period of the first wave of meiosis ( days 13–26 pp ) , we reasoned that a large proportion of germ cells must be lost prior to entry into meiotic prophase I . Thus , these mice suffer from multiple germ cell defects , one involving spermatogonial proliferation and the other involving meiotic progression . The combined effect is a drop in testis size of 75% and a depletion of epididymal spermatozoa by about 90% of wildtype numbers . Given that we have observed only 3 viable pregnancies from females mated to Btbd12βGeoFlox/βGeoFlox males over a 9-month period , we conclude that these defects result in sub-fertility . Breeding data from Btbd12βGeoFlox/βGeoFlox females shows a more severe phenotype , with no pregnant females after 1 year of breeding . These data are more severe than the original description of these mice by the Crossan et al [30] , but it should be noted that their analysis included many more mice over a much longer time period . As predicted , histological examination of testis sections from e16 onwards revealed a dramatic decrease in the numbers of spermatogonial precursors from late gestation , known as prospermatogonia or gonocytes , within the developing seminiferous cords of Btbd12βGeoFlox/βGeoFlox male pups compared to their wildtype littermates . These data suggest that BTBD12 may function to promote DNA repair mechanisms during early proliferation of the PGC population within the developing gonad . PGCs originate at the posterior end of the primitive streak at e7 , numbering around 45 [62] . They then migrate to the genital ridge , during which time they undergo a rapid cell proliferation to achieve a cell number of 3000 by e11 . 5 [63] . They continue to proliferate within the developing gonad until e13 when the testis/seminiferous cord structures form , trapping the now-mitotically arrested prospermatogonia within them [63] . They remain arrested until just prior to birth when they resume proliferation to provide the full complement of prospermatogonia to the post-natal testis . This late gestational wave of proliferation is also associated with increased germ cell apoptosis , even in testes from wildtype males . While the molecular pathways responsible for maintenance of genome integrity during this period are largely un-documented , it is likely that surveillance mechanisms exist similar to those found in somatic cells . Indeed , analysis of Atm mutant males reveals a requirement for ATM function in pre-meiotic spermatogonia [64] . Given the severe consequences of genomic instability within the PGC population for propagation of genetic mutations to offspring , it is plausible that particularly stringent DNA repair mechanisms must exist in these cells . Our results demonstrate that the PGCs arriving in the embryonic gonad appear normal in distribution and number since the testis of e16 mutants is similar in cellularity to that of wildtype littermates . From this time onwards , however , the numbers of prospermatogonia begin to decline rapidly in the testes of Btbd12βGeoFlox/βGeoFlox males such that the final number of these germ cells is dramatically lower in mutant males at e18 and at day 3 pp compared to wildtype males . This wave of apoptosis appears to coincide with the wave of proliferation that occurs around the time of birth because a large number of mitotic figures are observed in the testes of both wildtype and mutant males at this time ( Figure 4 , arrowheads ) . Crossan et al have suggested that the germ cell defects present in Btbd12βGeoFlox/βGeoFlox mice may be similar to that of mutant mice for other key DNA repair genes , particularly those of various FA complementation groups [30] . Indeed , there are substantial similarities between these phenotypes described herein and those for proteins that have been shown to interact with BTBD12 . For example , a targeted nullizygous mutation of Fancl also results in limited PGC populations in the embryonic gonad , similar to that seen in Btbd12βGeoFlox/βGeoFlox male embryos [65] . Interestingly , however , the residual PGCs can repopulate the testis gradually in the postnatal mouse such that , by 12 weeks of age , fertility is restored [65] . Thus , while the initial PGC defect may be similar in Btbd12βGeoFlox/βGeoFlox and Fancl−/− male embryos [65] , the outcomes in terms of adult fertility are very distinct . In contrast to the age-related increase in fertility in Fancl nullizygous mice , and also to Fanca nullizygous mice , which show declining fertility with age [60] , we see limited fertility in the Btbd12βGeoFlox/βGeoFlox males from 7 weeks of age onwards , with no subsequent restoration . Thus , it appears that mouse mutants for the FA complementation groups , while all showing similar anemia phenotypes , represent a spectrum of defects with respect to germ cell migration , proliferation , and differentiation . These differences underscore the importance of this family in genome stabilization within the germ line at all points in their development . The pre-meiotic phenotype we observe in Btbd12βGeoFlox/βGeoFlox males is also temporally similar to that seen in Ercc1 nullizygous animals [66] in that both mutations result in significant loss of germ cells prior to entry into meiosis . Indeed , the testicular phenotype of Ercc1−/− males at day 3 pp is remarkably similar , if not identical , to that presented herein [66] . In the case of Ercc1 , however , no restoration of fertility with age has been reported for nullizygous animals and , in fact , the mice have reduced numbers of epididymal spermatozoa throughout reproductive life , all of which show distinct morphological defects [66] . The limited spermatozoa observed in Btbd12βGeoFlox/βGeoFlox males appear to be morphologically normal and capable of fertilization , and could be a result of the fact that these mice retain residual BTBD12 protein . Beyond spermatogonial stages , we present evidence of a role for BTBD12 in prophase I progression in the mouse , congruent with the localization of BTBD12 protein on synapsed meiotic chromosome cores . The early localization of BTBD12 at these sites implies an early function in recombination events . Accordingly , in the Btbd12βGeoFlox/βGeoFlox animals , there is persistence in the signal for γH2AX across the autosomes beyond pachynema in the absence of BTBD12 . While some 10% of cells show synapsis errors in Btbd12βGeoFlox/βGeoFlox spermatocytes , synapsis is largely unaffected in these mutants , which is in contrast to that seen for FancD2 mutants in which spermatocytes show high levels of asynapsis in a subset of spermatocytes [59] . These data suggest a failure to repair DSBs in a timely fashion , leading to the persistent γH2AX and RAD51 observed at pachynema . However , we cannot rule out the possibility that the additional γH2AX signal is associated with persistent DNA damage arising during pre-meiotic replication events . Indeed , we did observe , by TUNEL labeling , a small fraction of spermatogonia undergoing apoptosis prior to meiotic entry , and so it is possible that if excessive DNA damage persists in the absence of BTBD12 , then a proportion of these cells could avoid apoptosis and enter prophase I . Such a possibility can only be addressed through the use of prophase I-specific conditional knock-out strategies . Importantly , however , regardless of the timing of DNA damage induction ( pre-meiotic or meiotic ) , these cells do not succumb to the pachytene checkpoint , as would be predicted from other knockout studies of early prophase I genes , including Msh4/Msh5 and Dmc1 [53] , [54] , [67] . Moreover , we do not see an overt increase in localization of TOPBP1 , nor of MSH4 and MSH5 ( data not shown ) , suggesting that the persistent DSBs observed at pachynema arise out of SPO11-induced events in early prophase I , and not as a result of DNA repair errors prior to meiosis . Studies by the Sekelsky group were the first to indicate a role for vertebrate SLX4 orthologs in meiosis . These authors showed that the Drosophila Slx4 ortholog , MUS312 , is essential for ensuring genomic stability and interacts with the MEI-9 ( XPF ) /ERCC1 nuclease to produce meiotic COs [9] , [14] , [68] , [69] . Thus mus312 mutant flies exhibit >90% reduction in meiotic crossovers [14] , but this is unrelated to Mus81 events since Drosphila Mus81 does not participate in CO regulation during meiosis [70] . Similarly , Saito et al describe a role for the C . elegans Slx4 ortholog , named HIM-18 , in meiotic recombination , since him-18 mutants exhibit reductions in crossing over of the order of 30–50% , depending on the chromosomal context [13] . Importantly , while Mus-81 does not appear to play a critical role during meiosis in worms , double mus-81;him-18 mutants show a more severe reduction in crossing over than him-18 alone , suggesting co-operative roles for MUS-81 and HIM-18 in meiotic recombination in C . elegans . It is interesting to note that a pre-meiotic function was also described for him-18 mutants , analogous to our observations for mouse BTBD12 . The role of BTBD12 in meiotic recombination is unclear at the current time , and it is interesting to note that the yeast ortholog , Slx4 , does not appear to function in meiosis [71] . Given that BTBD12 interacts with components of both the Class I and Class II crossover pathways , as well as with BLM , it is well placed to play an important role in integrating CO decisions in mouse germ cells ( Figure 8A ) . The loss of functional BTBD12 on meiotic chromosomes results in an increase in MLH1/MLH3 foci at mid-pachynema ( Figure 5Q , 5R ) , similar to that seen in Mus81 nullizygous males [28] . Also similar to the Mus81 knockout mouse , we observe no increase in chiasmata at diakinesis in the Btbd12βGeoFlox/βGeoFlox males ( Figure 6A , 6B ) . For Mus81 nullizygous mice , we hypothesize that the additional MLH1/MLH3 foci can restore/maintain chiasmata numbers in the absence of a functional Class II pathway ( Figure 8C ) . By contrast , loss of MLH1 or MLH3 , the major Class I mediators at pachynema , cannot be compensated for by MUS81 or any other pathway ( Figure 8B ) . Given the similarity of the Btbd12 phenotype , described herein , to that of the Mus81 nullizygous phenotype , our data suggest that BTBD12 may drive recombination intermediates towards Class II events , thereby promoting MUS81-mediated crossing over . In the absence of either MUS81 or BTBD12 ( Figure 8D ) , however , CO numbers are maintained because of a compensatory increase of MLH1/MLH3 foci , possibly suggesting that BTBD12 does not mediate this switch between the two pathways , but can promote Class II pathway choices under certain conditions . In this regard , it is possible that BTBD12 acts in concert with BLM helicase . Studies in yeast have suggested that the BLM ortholog , Sgs1 , acts to limit the formation of aberrant JMs that arise from strand invasion events that involve both ends of the DSB ( as just one example ) , instead producing substrates for the ZMM crossover pathway or instead resulting in a NCO fate , whilst Mus81-Mms4 can process those events that are not efficiently processed by Sgs1 even in wildtype situations [20] , [21] . The absence of Sgs1 , therefore , results in an overload of substrates for the Mus81 pathway [20] , [21] . Given the model that emerges from the yeast data , together with the phenotypic characterization of Btbd12βGeoFlox/βGeoFlox mice described herein , we propose that the function of BTBD12 is to drive events towards Class II COs , possibly in a BLM-dependent fashion . This raises the question of the fate of those aberrant JMs that are not directed towards Class I or NCO pathways by the actions of BLM; those intermediates that , ordinarily , would be the substrates for MUS81 processing but which , in the absence of Class II-promoting BTBD12 , still require resolution . Since it is unlikely that these aberrant structures are responsible for the additional MLH1/MLH3 sites that ( presumably ) maintain the normal chiasmata count , this suggests two important points . Firstly , the additional MLH1/MLH3 sites must be generated through some other mechanism , perhaps taking advantage of the fact that there is already an excess pool of MSH4/MSH5 foci from which to select for subsequent MLH1/MLH3-driven maturation/stabilization ( see model Figure 8 ) . Secondly , those BLM-processed JMs that fail to be diverted towards other recombination fates may remain un-repaired into late pachynema , as suggested by the persistent γH2AX and RAD51 on meiotic SCs in Blm conditional knockouts [72] and in Btbd12βGeoFlox/βGeoFlox males , and could well account for the gradual loss of spermatocytes via apoptosis leading up to the first meiotic division . Conflicting with these suggestions are our previous data showing increased chiasmata-like structures in the absence of Blm , that appear to be MLH1/MLH3-independent [72] . In addition , it should be noted that the complex intermediate structures reported for budding yeast have not yet been demonstrated in mammalian meiosis and , as such , our models , by necessity , relies on extrapolation from a number of organisms , most notably budding yeast . BTBD12 and its orthologs have been shown to interact with a number of key players in the meiotic machinery , including Rad1-Rad10 [73] , ERCC-1 [9] , [14] , [30] , XPF ( and its ortholog in Drosophila , MEI-9 ) [7] , [9] , [10] , as well as with MUS81 and SLX1 [8] , [10] . Studies by Svendson et al further demonstrated interactions between human BTBD12 and components of the DNA mismatch repair ( MMR ) family , including MSH2 [8] . Thus , a model emerges by which BTBD12 and its orthologs can mediate DNA repair and/or modification by directing the activities of certain nucleases in a substrate and context-specific manner . While the precise role of BTBD12 in mammalian meiosis remains to be further clarified , therefore , and its interactions with these various partners in mammalian germ cells have yet to be described , it is tempting to speculate on the function of BTBD12 based on localization data presented herein , on the phenotype of these mice , and based on previous studies of BTBD12 orthologs in other species . For example , studies in vitro have revealed distinct cutting activities for mammalian BTBD12 that are specific to its interactions with MUS81-EME1 ( flaps and replication forks ) and with SLX1 ( HJ cleavage ) [8] , [10] , [29] . Interestingly , the cleavage of HJs in vitro by BTBD12-SLX1 occurs in a near-symmetrical fashion , and in a manner similar to that seen for GEN1 [74] , and it is plausible that such an activity may well confer at least limited HJ resolvase activity on the BTBD12-SLX1 complex in mammalian cells . This is in contrast to that seen for yeast Slx4 , which cleaves HJ structures asymmetrically [2] , [75] thereby reducing the likelihood of an in vivo resolvase role for the yeast protein . This difference in the cutting symmetry between the yeast and mammalian orthologs may explain why slx4 mutants in yeast have no meiotic phenotype , while we observe a distinct meiotic phenotype in the Btbd12βGeoFlox/βGeoFlox mice . BTBD12 was first identified as a candidate target of the ATM kinase [11] and , in line with this , Slx4 is a substrate of the yeast ATM/ATR orthologs , Mec1 and Tel1 [5] . The function of Slx4 in replication fork repair is dependent on this phosphorylation [55] . In line with this , our western blot analysis reveals a depletion of BTBD12 protein within the testis of adult and juvenile Atm−/− males compared to that seen in wildtype litter mates . BTBD12 protein is also lost from the chromosome cores of Atm−/− spermatocytes , while pre-meiotic spermatogonial proliferation appears to require both BTBD12 and ATM [64] . Thus , while it remains to be seen how ATM and BTBD12 co-ordinate their pre-meiotic functions in spermatogonia , we conclude that ATM activity is essential for the normal loading of BTBD12 onto meiotic chromosomes during prophase I and/or for the stabilization of BTBD12 at these sites . Interestingly , the increased loading of MLH1/MLH3 observed in Btbd12βGeoFlox/βGeoFlox spermatocytes mirrors the phenotype seen in Spo11+/−Atm−/− animals , the Spo11 heterozygosity in this case rescuing the zygotene loss of spermatocytes in Atm single nulls and allowing progression to metaphase [31] , [32] , again pointing to a functional interaction between the ATM and BTBD12 . Spo11+/−Atm−/− spermatocytes also show defects in the dynamics of sex chromosome synapsis and in the formation of the obligate crossover on the pseudoautosomal region ( PAR ) of the XY [31] . This phenotype is not shared with the Btbd12βGeoFlox/βGeoFlox males , suggesting that BTBD12 is not involved in this aspect of ATM function . The Btbd12βGeoFlox/βGeoFlox strain of mice we have used herein do not , in all likelihood , represent a complete null allele , due to the presence of residual BTBD12 protein , as measured by both western blot and by immunohistochemistry . However , given the failure of this residual protein to accumulate on meiotic chromosome cores , at least to detectable levels , we consider this mouse to represent a significant impairment in BTBD12 function in germ cells . The use of the Btbd12βGeoFlox/βGeoFlox mouse , while not removing all of the endogenous protein , and while not permitting a focus on meiotic events alone , has been fortuitous in this instance , as it has highlighted the primordial germ cell proliferation defect , which otherwise would not have been evident using a meiosis-specific conditional null mouse . Clearly , any future meiosis-specific research should utilize a conditional approach , which would ensure BTBD12 absence only in meiotic cells , and allow much easier characterization of any defects as a result of loss of BTBD12 protein . Such studies are currently ongoing but will , by necessity , be lengthy . As discussed , BTBD12 has many potential roles in DNA repair in mammals , from its function in somatic cell repair in human cell lines [7] , [8] , [10] , [29] , to its roles in pre-meiotic PGC proliferation and meiotic crossover formation demonstrated herein . The ability of BTBD12 to process not only HJs robustly , but other DNA structures , such as 3′ and 5′ flaps , as well [7] , [8] , [10] , [29] , indicates it has the potential to have numerous roles in DNA repair , both in mitosis and meiosis . Moreover , the evidence of functional interactions with a myriad of DNA repair proteins such as MUS81 , BLM , XPF-ERCC1 , MSH2-MSH3 , and the Fanconi anemia genes , along with its well established binding partner SLX1 , show that BTBD12 may integrate with several DNA repair complexes to effect its HJ ( and other ) processing abilities [7] , [8] , [10] , [29] . These interactions no doubt contribute to the similarities between phenotypes at discrete stages of spermatogenesis for Btbd12 , Fancl , Ercc1 , and other mutant mouse models . Thus a clearer understanding of the function of mammalian BTBD12 , both in the context of its multiple roles in gamete formation , and in its function in general genome stability/DNA repair will require more detailed knowledge of these key interactions .
All animals used in this work were handled under strict guidelines imposed by Cornell Veterinary staff and by the Institutional Care and Use Committee ( IACUC ) under an approved protocol . We obtained a line of Btbd12 mice with a cassette inserted into the Btbd12 gene , containing a β-galactosidase fused with a neomycin resistance gene , flanked by FRT sites , and LoxP sites flanking exon 3 of the Btbd12 gene from the European Conditional Mouse Mutagenesis ( EUCOMM ) program ( EPD0028_7_A08; Btbd12tm1a ( EUCOMM ) Wtsi ) . We termed these mice Btbd12βGeoFlox , to indicate that the FRT flanked and LoxP flanked regions are intact . Mice were genotyped using primers Btbd12_F 5′ CACTGAGCCATCTCACCAGC 3′ and Cas_R1 5′ TCGTGGTATCGTTATGCGCC 3′ to amplify the mutant allele ( Btbd12βGeoFlox ) , and Btbd12_F 5′ CACTGAGCCATCTCACCAGC 3′ and Btbd12_R2b 5′ GGAGCCCAGTCTGGGACTCTG 3′ to amplify the wildtype allele ( Btbd12+ ) . The anti Rabbit polyclonal BTBD12 antibodies were against recombinant His-tagged murine BTBD12 peptide comprising amino acid residues 1–350 ( “NT” ) and 750–1100 ( “CT” ) . For that , the corresponding cDNA fragment was cloned in pET-28 expression vector ( Novagen ) and recombinant proteins fused to a histidine tag were purified using Ni-NTA resin ( Qiagen ) following the manufacturer's instructions . Epidiymides were removed from either Btbd12βGeoFlox/βGeoFlox or Btbd12+/+ adult mice , placed in human tubule fluid ( HTF ) culture medium containing BSA ( Specialty Media , Millipore ) , ripped open using micro forceps and the contents squeezed into the medium . The spermatozoa were cultured for 20 minutes at 32°C , then a 20 µl aliquot was removed and fixed in 480 µl 10% formalin . The fixed cells were gently mixed then intact spermatozoa counted using a hemocytometer . Testes were removed from pre-pubertal or adult mice and fixed either in Bouins fixative or 10% buffered formalin for 2–12 hours . Paraffin-embedded tissue was sectioned at 4 µm and processed for Hematoxylin and Eosin staining or immunohistochemical analyses using standard methods . Testes were removed from adult Btbd12βGeoFlox/βGeoFlox or Btbd12+/+ mice for the meiotic spread analysis , as well as for the focus counts , and processed as previously described [26] . Briefly , testes were removed and decapsulated into hypotonic sucrose extraction buffer ( HEB , containing 1 . 7% sucrose ) and left on ice for 60 minutes . Tubules were macerated on glass depression slides in a bubble of 0 . 03% sucrose and added to slides coated in 1% paraformaldehyde . The slides were dried slowly in a humidified chamber for 3 hours and washed in PBS containing Photoflo ( Kodak , EMS ) . Ovaries were removed from day e19 female embryos and incubated in HEB for 20 minutes , before being macerated on a depression slide in 0 . 03% sucrose and added to a bubble of 1% PFA on a well slide , before drying as above . Slides were processed as described previously [76] using antibodies generated in this lab [26] , generously donated by colleagues and available commercially . Immunohistochemistry was performed on formalin-fixed sections using rat monoclonal hybridoma supernatant against germ cell nuclear antigen-1 ( GCNA-1 ) , 10D9G11 , for staining of germ cells [77] , rabbit anti-BTBD12 antibody , rabbit anti-β-galactosidase or TUNEL staining ( Chemicon ) to detect cells undergoing apoptosis . γH2AX staining was described as either “normal” or “abnormal” , in both pachytene and diplotene cells from Btbd12βGeoFlox/βGeoFlox or Btbd12+/+ spermatocytes . Abnormal cells were classified by >1 γH2AX focus per homologous chromosome core in pachynema , and 1 or more SC-associated γH2AX focus per nucleus in diplonema . Oocyte spindles were prepared using a modification of techniques described previously [78] , [79] and used subsequently in our laboratory [80] . Briefly , ovaries were removed by puncturing ovaries from unstimulated females at 24–26 days of age , and placed in Waymouth's media ( GIBCO , Invitrogen Corporation , Carlsbad , CA ) supplemented with 100 units of penicillin ( base ) and 10 µg of streptomycin ( base ) /ml , 10% fetal bovine serum , and 0 . 23 mmol/l sodium pyruvate . Primary oocytes at germinal vesicle ( GV ) stage were cultured in 20 µl drops of KSOM ( Millipore Corporation , Bedford , MA ) overlaid with mineral oil ( Chemicon , Millipore Corporation , Bedford , MA ) and incubated at 37°C in an atmosphere of 5% CO2 . After 2 . 5 hrs in culture , oocytes were transferred to fresh KSOM drops and scored for germinal vesicle break down ( GVBD ) . In order to observe meiotic division at metaphase I , oocytes were cultured in KSOM for 8∼10 h and >18 h , respectively , and fixed in fibrin clots ( below ) . Oocytes were fixed in fibrin clots , according to published methodology [78] , prior to staining for β-tubulin ( 1∶500 mouse monoclonal antibody; Sigma-Aldrich , St . Louis , MO ) . Tubulin staining was visualized using a FITC-conjugated goat anti-mouse IgG ( Jackson ImmunoResearch Laboratories , Inc . , West Grove , PA ) , and counterstaining for DNA was achieved using 4′ , 6-Diamidino-2-phenylindole ( DAPI ) . Testis weights , spermatozoa numbers , TUNEL analysis , immunofluorescent focus counts and diakinesis spread counts were all analyzed for statistical significance by using an unpaired t-test using Prism 4 . 0 software . Analysis of micronucleus formation in peripheral blood cells was performed as previously described [35] . Briefly , peripheral blood was collected from the retro-orbital sinus , fixed in methanol , and incubated in bicarbonate buffer containing RNase A and anti-CD71: FITC antibody ( Biodesign International ) . After washing and staining with propidium iodide , the cells were analyzed on a FACSCalibur flow cytometer ( Becton-Dickinson , San Jose , CA ) . | Mutations in genes essential for genome maintenance during meiosis can result in severe disruptions to spermatogenesis and subsequent low fertility and/or birth defects in mammals . The mammalian homolog of yeast Slx4 , BTBD12 , plays a critical role in somatic cell repair in mice . Here , we show that this critical function extends to mammalian germ cells , by examining the effects of a Btbd12 gene disruption in mice . Btbd12 mutant mice show severely reduced fertility , as a result of both pre-meiotic spermatogonial proliferation defects and impairment of proper meiotic progression . BTBD12 appears to be required for normal progression of double-strand break repair events that result in the formation of crossovers between maternal and paternal homologous chromosomes , with Btbd12 mutants displaying an increase in unrepaired breaks , impaired homologous chromosome interactions , and a slight increase in the number of crossover intermediates . BTBD12 protein is also down-regulated in the testes of Atm null mice , supporting previous studies showing that BTBD12 is a target of ATM kinase . These data provide new evidence about the role of BTBD12 in mammalian gametogenesis and are critical to furthering the understanding of the molecular processes involved in meiotic DNA repair . | [
"Abstract",
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] | 2011 | Mammalian BTBD12 (SLX4) Protects against Genomic Instability during Mammalian Spermatogenesis |
Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences , especially speech , is still very limited . One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal , microscopic level . Here , we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model , i . e . , the songbird , which faces the same challenge as humans: to learn and decode complex auditory input , in an online fashion . Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level , we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech . We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly , even in adverse conditions . In addition , we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail . The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments .
Can we learn something about how humans recognize speech from how birds recognize song ? The last common ancestor of humans and birds lived about 300 million years ago , nevertheless human and songbird communication share several striking features at the cognitive , neuronal and molecular level [1] , [2] . When we recognize speech , our brains map fast speech sound wave modulations to spectrotemporal auditory representations [3] , [4] . Similarly , songbirds map song sound wave modulations to specific internal representations [5] , [6] . In addition , similar to humans , songbirds gain their vocal abilities early in life by listening to adults , and memorizing and practicing their songs [2] . The similarities include anatomical and functional features that characterize the pathways for vocal production , auditory processing and learning [1] , [2] , [7] . For example , the auditory system in both humans and songbirds is organized hierarchically [8]–[10] where fast time scales are represented by lower levels and slow time scales by levels higher up in the hierarchy [11] , [12] . Much more is known experimentally about the exact neuronal mechanisms in songbirds than in humans , due to detailed electrophysiological studies which have shown that songbirds use a sequence of auditory dynamics to generate and recognize song in a highly effective manner [6] , [13] . These detailed findings in songbirds enabled us to derive a neurobiologically plausible , computational model of how songbirds recognize the songs of their conspecifics [14] . Our aim in the present paper is to attempt to translate this birdsong model to human speech by assuming that humans and birds use similar internal models for recognizing sounds . Such a translation would provide a unique opportunity to derive a mechanistic understanding and make predictions at both the microscopic and macroscopic neuronal level for the human speech learning and recognition system . The birdsong model described in [14] performs a Bayesian version of dynamical , predictive coding based on an internal generative model of how birdsong is produced [15] . The core of this generative model consists of a two-level hierarchy of nonlinear dynamical systems and is the proposed mechanistic basis of how songbirds extract online information from an ongoing song . We translated this birdsong model to human sound recognition by replacing songbird related parts with human-specific parts ( Figure 1 ) . This included processing the input with a human cochlea model , which maps sound waves to neuronal activity . The resulting model is able to learn and recognize any sequence of sounds such as speech or music . Here , we focus on the application of the model on speech learning and recognition . The contribution of this article is threefold: First , inspired by songbird circuitry , it proposes a mechanistic hypothesis about how humans recognize speech using nonlinear dynamical systems . Secondly , if the resulting speech recognition system shows good performance , even under adverse conditions , it may be used to optimize automatic speech recognition . Thirdly , the neurobiological plausibility of the model would allow it to be used to derive predictions for neurobiological experiments .
As a model , we employ a novel Bayesian recognition method of dynamical sensory input such as birdsong and speech . The Bayesian approach first requires building of a so-called generative ( internal ) model , which is then converted to a learning and recognition model . The key advantage of this approach , as opposed to standard models in both human speech recognition and automatic speech recognition , is that the generative model is formulated as hierarchically structured , nonlinear dynamical systems . This means that one can employ generative models specifically tailored to birdsong or speech recognition . As we show in the following , this feature is crucial for translating experimental birdsong results to a concrete recognition model . This translation would not be possible with generic models such as are standard and widely used in automatic speech recognition , e . g . the hidden Markov model and , very recently , deep belief networks and liquid state machines [16]–[18] . Our model has also several differences from the influential models such as TRACE [19] and Shortlist [20] , [21] and we provide a more detailed comparison in the Discussion . In the birdsong model , we used experimental insights about the firing patterns of the premotor area HVC ( formerly known as the high vocal center ) and the nucleus RA ( robust nucleus of the arcopallium ) to derive a hierarchical song generation model [14] . In the high level structure HVC , specific neurons called HVC ( RA ) , fire sequentially at temporally precise moments [13] , [22] , [23] where each neuron of this sequence fires only once during the song to provide input to a group of RA neurons . We translated these two levels to the human speech model in the present study ( Figure 1 ) . The second , higher level encodes a recurrent neural network producing a sequential activation of neurons in a winner-less competition setting ( stable heteroclinic channels [24] , see below ) . These dynamic sequences control dynamics at a first , lower level ( Hopfield attractor , see below ) , where we model amplitude variations in specific frequency bands . In comparison to the birdsong model , the generative model here does not explicitly model the vocal tract dynamics but rather the dynamics at the cochlea which would be elicited by the stimulus . Therefore , the second level dynamics act as a timing mechanism providing the temporal information and the first level dynamics represent the spectral content at different frequency bands . Such a separation of temporal and spectral processing is also suggested for the human auditory system [25] . We do not restrict the functionality of the second level ensembles to specific phonemes or syllables but rather use them as time markers for the represented spectrotemporal stimulus ( mostly words in this paper ) . By using this generative model ( Figure 1 ) , we can apply Bayesian inference to derive a mechanism , which can learn and recognize a single word . We call this mechanism for the remainder of this paper a module . Here , a module is essentially a sophisticated template matcher where the template is learned and stored in a hierarchically structured recurrent neural network and compared against a stimulus in an online fashion . Individual modules can be combined into an agent to achieve classification tasks as shown in the “Word Recognition Task” below , see Figure 2A for an overview . A crucial parameter in the model is called precision which is the inverse of the variance of an internal state . This is used in the model as a way to balance the ( top-down ) prior information and ( bottom-up ) sensory evidence . In the simulations , we show that the precision settings are crucial to learn new stimuli or to recognize sounds in noisy environments . We further discuss the biological plausibility of the resulting recognition model in the Discussion . For a given speech stimulus z ( preprocessed by the cochlear model ) and a model m , the model evidence or marginal likelihood of z is defined by the conditional probability where the model m consists of all differential equations described before and priors for model parameters . The task for the module is to infer the corresponding causal states v and hidden states x at all levels as well as the parameters , i . e . the 's that connect the levels , which we all together denote by . Therefore the goal is to estimate the posterior density , , which describes the mean distribution of the variables as well as the uncertainty about them . We approximate the posterior in an indirect way: The marginal likelihood of z is given by where is defined in terms of the likelihood and the prior . We approximate this intractable integral by introducing a free-energy term which is a lower bound for the marginal likelihood . It is straightforward to show that:where is the free-energy , is the Kullback-Leibler divergence and is the recognition density . Note that is an auxiliary function that we will use to approximate the posterior density . The divergence term D is nonnegative , , and if and only if . This means is a lower bound for , and if we can maximize , this will minimize providing an approximation for the posterior density . To find that maximizes , we make a Gaussian assumption about the form of , the so called Laplace approximation . Therefore we take where consists of the mode and the variance . Now , the question turns into a maximization problem of the free energy with respect to :which gives the approximation for the posterior density . Note that the above maximization process is a simplified description and is only suitable for the time-independent u parameters ( static case ) . When time-dependent states are involved , i . e . causal and hidden states , one needs to replace the free energy with free action which is the anti-derivative of free energy in time , i . e . . In this case , one aims at minimizing free action under the Laplace assumption . We note that time-dependent and independent variables can be handled concurrently and we refer the reader to [42] for details . For all simulations in this paper , we used fixed prior variances for all states and parameters . The variances for the corresponding simulations are usually described in terms of the precision , , which is defined as the inverse of the variance , i . e . . Therefore , a high prior precision for an internal state means that the dynamics are not allowed to deviate much from expectations provided by the generative model ( top-down influence ) whereas a low prior precision means the dynamics is relatively susceptible to ( bottom-up ) influences ( wider standard deviation ) . Throughout the Results section , we report the log-precision values; the corresponding standard deviations can be computed by the formula: standard deviation = exp ( −log precision/2 ) . The above maximization process can also be formulated in a hierarchical setting . Let us denote all hidden and causal states at level by and , respectively . We also write and to describe the dynamics of the hidden and causal states at the th level ( see Eqns . 1 and 2 ) : where denotes the normally distributed fluctuations at the th level . Note that the second level causal states provide input to the first level while the hidden states are intrinsic to each level . The preprocessed speech stimulus enters the system through the first level: . The optimization process described above , i . e . finding the optimum mode and variance for states and parameters , can be implemented in a message passing scheme [42] where the optimization problem turns into a gradient descent on precision-weighted prediction errors ( see also Figure 2B ) :where and are causal and hidden prediction errors at the i th level , weighted by the causal and hidden precisions and respectively; and denote the internal predictions of the corresponding level for and , respectively . Internal predictions set the states to the right trajectory for future input . Therefore , it can be seen that as prediction error is minimized , internal predictions fit better to the external input . Intuitively , high precision for a variable means the prediction error is amplified and therefore only small errors are tolerated whereas low precision means large errors are tolerated and therefore the approximation to the states is less precise .
In each module ( see Model ) , learning and recognition of speech are simultaneous processes of adapting internal connections and inferring the speech message dynamics of the speaker . As in the brain , learning changes parameters , such as the synaptic connectivity , of the modules relatively slowly , whereas recognition is based on rapidly changing states of the system , such as the membrane potentials and firing rate [45] , [46] . In all simulations below , there are two main tasks: ( i ) a learning task where the feedback parameters from second level to first are allowed to change and ( ii ) a recognition task where parameters are fixed and the model only reconstructs the hidden dynamics . In both cases , the model is given the appropriate precision settings from the beginning of the experiment and it either performs a learning task or a recognition task . A single learning step consists of learning one word by one module . Both recognition and learning in a module starts with sensation; a speech sound wave ( a single word for all but one simulations below ) , and after passing through the cochlea model this serves as a dynamic input to the module . The speech signal is preprocessed by the cochlear model and the dynamic output of the cochlear model , which we denote by a vector z ( t ) , reaches the first level of the module ( Figure 1; for mathematical details see Model ) . Given this time-dependent vector z ( t ) and the two-level generative model ( Equations 1 and 2 in Figure 1 , see Model ) , each module infers the states of the first and second levels ( recognition ) and learns the connection weights from the second to the first level ( I's ) , see first line of Equation 2 . To implement this , we used the Bayesian inference technique “Dynamic Expectation Maximization” [42] . Both levels of a module consist of neuronal populations that interact within and between levels . These populations encode expectations about the cochlea model dynamics , i . e . the sensory input , using the internal generative model described in the previous section . These expectations predict the neuronal activity ( i . e . , firing rates ) at the next lower level , i . e . , either at the cochleagram or the first level . The hierarchical inference uses top-down and bottom-up messages , which aim to minimize an error signal , the so-called prediction error . At any given time t , the input from the cochlear model , z ( t ) , is compared to the predictions at the first level which are produced by the generative model . During recognition , the prediction error is propagated to the second level where , again , prediction errors are computed using the generative model . Both levels adjust their internal predictions to minimize the prediction errors [42] . The module's expectation of how much an internal state will vary is a key parameter of the model: It is called “precision” . The precision determines how much error is tolerated at a specific level and we illustrate its relevance to speech learning and recognition in the next section . During recognition , the second level forms predictions that are transmitted to the first level . This is only possible if the parameters for the backward connections between these two levels are appropriate; each module has to learn these parameters . In contrast to recognition , learning is not accomplished online because the information about parameters is obtained at a slower time scale , i . e . , over the course of a complete stimulus ( word ) or repetitions of a stimulus . For learning , prediction errors are summed up for the whole stimulus duration and used after stimulus presentation to update the parameters . Therefore , as each module is exposed to repeated stimuli , the parameters are updated to minimize the prediction error accumulated over time , while states are updated in an online fashion to minimize temporally local prediction errors . In summary , learning and recognition are realized as parts of the same inference scheme and work together to minimize overall prediction error . The necessary computations can be described as the dynamics of a hierarchically structured recurrent neural network operating online on the continuous speech input [43] , [47] . For further details , see Model . In the following two sections , we describe how we tested the hypothesis that the prior precision setting of a module is fundamental for understanding the learning of speech . This hypothesis follows from the construction of the module where only two different interpretations of suboptimal speech recognition exist: ( i ) the sensed speech is noisy , or ( ii ) the module's internal model is not appropriate and needs to be adapted . This is why the precision ratio at the first level , i . e . , a module's expectation about how noisy speech dynamics are relative to its internal dynamics , is fundamental for learning . A precision setting as shown in Figure 3A will effectively exclude the module's assumption that speech is noisy; rather it will rely on the assumption that speech is sequential based on a high precision of the dynamics at the second level . This will prompt the module to adapt its internal speech model .
In songbird studies , temporally precise sequential activation of neurons in a high level structure , HVC , has been observed during singing [22] , [23] and the same area has also been shown to be involved during recognition of songs with similar precise activations [6] . It has been suggested that Broca's area in the inferior frontal gyrus ( pars opercularis ) in humans corresponds functionally to HVC in songbirds [2] , [72] . Similar to HVC , this area in the human inferior frontal gyrus is involved in recognition and production of speech . It has been implied in sequence perception and in providing top-down predictions to auditory speech processing areas ( for a review see [73] ) . We suggest that this is a candidate area for including precise sequential activation of neurons , as modeled by dynamic sequences at the second level of the present model . The existence of sequentially activated , temporally precise , neuronal ensembles in the cortex has been proposed previously [74] and provides an explanation for findings of a precise spike timing which have been observed in experiments in different species , e . g . [75]–[77] . There do not seem to be equivalent neuronal studies in humans; however , speech processing activity , as observed with magnetoencephalography , has been explained as large-scale sequential activity [78] . Based on the results of the current paper , we predict that such sequential activations in the human brain , expressed at a microscopic level , e . g . , in spike timing , are crucial in organizing the auditory information coming from the lower areas to form the dynamic percept of phonemes , syllables and words . Even though the second level ensembles in the proposed model are encoded as temporally regularly spaced sequences in the generative model , we showed that during recognition ( see Variations in Speech Rate simulation ) they have the flexibility to activate earlier or later according to the spectrotemporal features they are tuned to . This fits well with a recent study [79] where the authors presented evidence that HVC activity is timed to particular time points of motor gestures during song production . The current generative model does not include a vocal tract mechanism [80]; however such a mechanism could be readily incorporated with an extra level at the bottom of the hierarchy ( see [14] for an example ) . To model neurobiological findings in songbirds , we used an advanced Bayesian inference scheme using recurrent neuronal networks . To our knowledge , this type of model has not been used before , neither in human speech recognition nor automatic speech recognition . One advantage of this approach is that recognition is performed in a brain-like fashion on continuous sensory dynamics , in contrast to a standard hidden Markov model operating on discretized input [16] . In addition , the present model can be used , as we have demonstrated , to incorporate experimental birdsong findings by specifying a hierarchically structured , generative model based on nonlinear dynamical systems and translate the resulting model to human speech . Our approach is unique in the sense that we use a hierarchy of nonlinear dynamical systems as a generative model to provide an online Bayesian inversion mechanism of human speech . Many other computational speech and word recognition models have been proposed that are both neurobiologically plausible and can explain experimental results [19] , [20] , [81]–[87] . These models typically focus on the word selection process rather than on how relevant spectrotemporal features are extracted from the sound wave . For example , most of these models assume that relevant phonemic features have already been extracted from the sound wave and arrive in regular intervals . This is in distinction to the present approach which models the extraction of relevant speech features from a noisy , continuous sound wave with varying speech rate . An example for these word selection models is the hierarchical TRACE model [19] , [88] . There are three key differences between the TRACE and the present model . First , there is no learning in TRACE: the model parameters have to be manually set to enable recognition . Second , TRACE does not represent precision , which , as illustrated above , may be important to explain phenomena in both perception and learning . Third , TRACE is based on the competition of relatively simple processing units , and , therefore , is unable to identify local mistakes or mispronunciations; it returns the most probable word . In contrast , the present model can monitor such mismatches in an online fashion using the prediction error . This enables the processing of slight differences in pronunciations , as , for example , when the proposed model was used to adapt to speech with an unusual accent . Another widely known model is the Shortlist model and its Bayesian version Shortlist B [20] , [21] . Both models have most of the functionalities of the TRACE where information is processed in a feed-forward fashion . The Bayesian approach introduced in Shortlist B [21] illustrates a useful way to combine prior information such as word frequency with the likelihood function of the speech input . This demonstrates the interplay between the priors and the precision of the agent ( called reliability in [21] ) . This is similar to the present model , where a differential setting of the precision parameters causes either recognition or learning mode of the sensory input ( see Figure 3 ) . The main differences between the present model and Shortlist B are that ( i ) Shortlist B does not allow for speech learning , ( ii ) Shortlist B assumes that phonemic features have already been extracted by some preprocessing stage while we explicitly model this stage using the cochleagram , and ( iii ) Shortlist B has been formulated as a feed-forward model only while the present model explicitly uses top-down influence to improve recognition of noisy input . A different category of models has focused , like the present model , on the processing of auditory stimuli by single neurons or network of neurons [18] , . For recognition , these models typically have to wait until the end of the stimulus to obtain all required neuronal responses . This is different from human performance where recognition can be performed online while the stimulus is received . This online recognition using predictions is also a hallmark of the recognition model proposed here , where the accumulated prediction error can be used for recognition anytime during stimulus presentation . Recently , so called reservoir computing techniques using recurrent neural networks have been used for speech recognition [18] , [51] , [89]–[92] and provide excellent recognition results . Typically , these results are achieved with large networks of hundreds of neurons . This is different from the present study where we used few neurons for word recognition , i . e . just eight neurons at the second level and six neurons at the first , for each module . It would be worthwhile to consider recurrent networks as used in reservoir computing as a generative model in a Bayesian approach to better understand the mechanism underlying high recognition performances in reservoir computing . Using simulations , we have shown that the precisions of the states ( i . e . , how certain the agent is about its internal states and dynamics ) at different levels of the hierarchy are fundamental to learning and recognition of speech . Here , we fixed the prior precisions at each level to use appropriate precision settings during learning and recognition . The actual mechanisms in the brain for achieving such context-dependent optimum precision values are not known . Neurobiologically , cholinergic neurons ( whose main neurotransmitter is acetylcholine , ACh ) are known to be involved in the modulation of perceptual processes [93] , [94] . It has been proposed that ACh may have the role of reporting on uncertainties of internal estimates and that high levels of ACh should correspond to faster learning about the environment and enhancement of bottom-up processing [95] . Such claims fit well with the present study since we found that increased precision about sensory states is ideal for learning speech as it enhances the influence of sensory information; whereas , learning deteriorates with decreasing precision ratios ( Figure 3 and 7 ) . We predict that increased levels of ACh may enhance the learning of novel auditory stimuli by suppressing top-down effects caused by a relatively low precision of internal dynamics; however , this should , in parallel , also disrupt perception of noisy stimuli since top-down information is crucial in cocktail-party like situations , ( right column of Figure 6 ) . Such claims could be tested with a behavioral study while manipulating the neurotransmitter levels pharmacologically [96] . The proposed model makes a computational link between sensory input ( i . e . , the speech sound wave ) received by subjects and the dynamics of their hypothesized internal representation [97] . In particular , we found that the prediction error is a key quantity that can be used to achieve high performance in speech recognition . This quantity can be used in novel computational analysis techniques for speech recognition neuroimaging experiments: The idea is to use the dynamics of the module's internal prediction error when receiving speech input as a predictor for neuronal activity in human subjects receiving exactly the same stimuli ( see [98] for a similar study ) . This modeling approach would enable one to identify the exact computational role of specific areas in the well-established speech recognition system . In addition , this approach can be applied to speech learning studies ( accent adaptation and second language learning ) , where one would use the module's prediction error experienced during learning to predict subject's changing brain activity during learning and estimate the precision parameters which subjects use . This may be done using either a voxel-wise regressor-based approach , or a network analysis ( Dynamic Causal Modeling [99] , [100] ) . For example , one may estimate the changes in effective connection strength in a network including the inferior frontal gyrus and primary auditory areas during accent adaptation or for speech recognition under different levels of noise . It would also be revealing to include a variety of precision settings as an experimental condition in studies that specifically test the hierarchical predictive coding hypothesis in the auditory cortex [101] . Here , we only used six neuronal ensembles to represent the cochleagram in six frequency channels . This resolution is comparable to the low number of spectral channels used in cochlear implants [102] . Nevertheless , the model provided competitive recognition results ( Table 2 ) . We found that this performance drops if only four channels are used , but we did not explore this using more channels because the required computational power quickly increases with the current implementation ( with complexity ) . This computational issue could be resolved by parallel ensemble-specific computations , which would be another step towards biological reality and probably improving recognition rates further . It would also be worthwhile extending the cochlear features in the present model with other biologically plausible preprocessing steps , such as occurrence times , which encode the onsets and offsets of specific features [50] , [103] . It is important to notice that the current model is not entirely specific to speech but can also be used to recognize other sound sequences such as music . In a future project , we will therefore make the model more speech-specific and extend the current model by including a vocal tract model in addition to the cochlear processing . This would make the inference more sensitive to relevant features in human speech and thereby improve recognition . Moreover , such a vocal tract mechanism would be beneficial for recognizing speech from different speakers since speaker-specific parameters can be included in the vocal tract model and constrain the recognition dynamics . This would allow the model to identify the similarities between words even if they are spoken by differently sounding speakers and therefore have little acoustic overlap . Such a model can also be used to qualitatively model specific findings at a phonemic level [69] . It is also worth mentioning that we assumed a fixed second-level connectivity matrix in the model ( in Eqn . 1 ) which produces expectations about sequential dynamics by winnerless competition . We assumed here that such a structure already existed at the higher levels . It may also be possible to learn these specific connections from scratch; however , we expect that one would need relatively informative priors about these parameters to limit the search space . Moreover , the generative model could be extended by adding extra levels to the hierarchy of nonlinear dynamical systems . This would allow the modeling of sequences of phonemes and syllables [104] , or even sentences as sequence of words [105] . This can be done either using the technique proposed in the present paper or by using carefully designed nonlinear dynamical systems , as exemplified in [106] . Such detailed sentence level representations could be used to model syntactic experiments as shown in [107] . Using hierarchies , it would be useful to model the competition between possible alternative descriptions that emerge from partial stimuli where predictions provide constraints for the appropriate dynamics and therefore stable perception [108] . Such a hierarchical extension would be ideal to model the word selection process as exemplified in Shortlist B [21] while using real speech ( sound waves ) as input . Finally , the proposed learning and recognition technique could be extended to also estimate dynamically the precision values based on techniques as employed by [109] . This would allow the model to fine-tune the precision settings as a part of the optimization process . Currently , one still needs to provide the prior precision settings to inform the model about the context of the experiment , i . e . whether it is a learning task or recognition task . We proposed a computational model using a hierarchy of nonlinear dynamical systems and Bayesian online filtering for learning and recognizing sound sequences such as speech . This model was derived from a neuronal model for recognition of birdsong . It achieves high speech recognition performance and explains several auditory recognition phenomena , as well as behavioral data . This work has three implications . First , it shows that human speech and birdsong recognition systems may share similar computational components . Secondly , the competitive performance , even under adverse conditions , suggests that it may be used to optimize automatic speech recognition . Thirdly , the neurobiological plausibility of the model enables the generation of predictions for neurobiological , e . g . , neuroimaging , experiments . | Neuroscience still lacks a concrete explanation of how humans recognize speech . Even though neuroimaging techniques are helpful in determining the brain areas involved in speech recognition , there are rarely mechanistic explanations at a neuronal level . Here , we assume that songbirds and humans solve a very similar task: extracting information from sound wave modulations produced by a singing bird or a speaking human . Given strong evidence that both humans and songbirds , although genetically very distant , converged to a similar solution , we combined the vast amount of neurobiological findings for songbirds with nonlinear dynamical systems theory to develop a hierarchical , Bayesian model which explains fundamental functions in recognition of sound sequences . We found that the resulting model is good at learning and recognizing human speech . We suggest that this translated model can be used to qualitatively explain or predict experimental data , and the underlying mechanism can be used to construct improved automatic speech recognition algorithms . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] | [] | 2013 | From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems |
We develop a coarse-grained stochastic model for the influence of signal relay on the collective behavior of migrating Dictyostelium discoideum cells . In the experiment , cells display a range of collective migration patterns , including uncorrelated motion , formation of partially localized streams , and clumping , depending on the type of cell and the strength of the external , linear concentration gradient of the signaling molecule cyclic adenosine monophosphate ( cAMP ) . From our model , we find that the pattern of migration can be quantitatively described by the competition of two processes , the secretion rate of cAMP by the cells and the degradation rate of cAMP in the gradient chamber . Model simulations are compared to experiments for a wide range of strengths of an external linear-gradient signal . With degradation , the model secreting cells form streams and efficiently transverse the gradient , but without degradation , we find that model secreting cells form clumps without streaming . This indicates that the observed effective collective migration in streams requires not only signal relay but also degradation of the signal . In addition , our model allows us to detect and quantify precursors of correlated motion , even when cells do not exhibit obvious streaming .
Eukaryotic cells frequently transduce external chemical gradients into directed cell migration [1] , a phenomenon known as chemotaxis . Seminal work in the last few decades has identified components of the intracellular biochemical networks mediating cell response to external chemical gradients and found that responsive components such as the phosphoinositide lipids ( PIPs ) , PI3K , and PTEN are highly conserved across cell types . In these efforts , our model organism ( Dictyostelium discoideum ) has been a useful source for discovery of network components and the development of quantitative models exploring plausible mechanisms for mediating directional sensing . Despite the vast similarities in gradient detection among D . discoideum and mammalian cells including neutrophils and neurons , D . discoideum chemotaxis displays a striking collective phenomenon not often found in other cell types where D . discoideum cells responding to the extracellular chemical signal cyclic-AMP ( cAMP ) tend to migrate in a head-to-tail fashion termed streams . In response to an external cAMP cue , D . discoideum cells synthesize and secrete cAMP relaying the initial signal to nearby cells . Many cell types , including neutrophils , macrophages , and epithelial cells , have potential signal relay loops , but they do not tend to migrate in streams in a standard chemotaxis assay . Building on previous work [2]–[5] , we develop a minimalistic model for D . discoideum migration and signal relay in a linear gradient . Our model incorporates recent experimental measurements on cell migration persistence [6] , independence of signal strength [5] , and migration mechanism and lag in reorienting to signals [7] . We use the model to ask what aspects of the signal relay loop promote streaming . We find that a balance between fast secretion and degradation are needed to match experimental observations . To constrain the migration parameters , we measure the time autocorrelations and the fluctuations of the cell motion from our experimental systems and we suggest the possible use of these metrics to find evidence of signal relay in cells that do not display streams . Our efforts are motivated by recent experiments on D . discoideum , that show a notable visual distinction between cells that relay signals , and cells that both relay and degrade the signal . Wild-type cells , which emit cAMP and degrade cAMP , can form streams where cells are aligned head to tail , while mutant PDE1- cells that are unable to degrade cAMP form transient , aberrant streams that lead to clusters [8] . When food is plentiful , D . discoideum cells exist as single cells and chemotax towards the bacterial metabolic product folic acid . When food is removed , D . discoideum transitions from single cell to collective behavior - through the spontaneous secretion and detection of cAMP . The cooperative behavior of this spontaneous transition was found to follow Winfield synchronization and , the emergence of pulsatile , signaling centers is beautifully described in [5] . These pulses travel through a population of D . discoideum in spiral waves [9] , [10] . Secretion of the extracellular phosphodiesterase ( PDE1 ) is essential for the spontaneous transition [11] . Each pulse of external cAMP detected by cells results in an increase in gene expression promoting collective behavior [12] , and after 4–6 hours of cAMP mediated development , cells begin to aggregate . In order to determine the essentials for chemotaxis and streaming separate from those needed for development , researchers often provide exogenous pulses of cAMP [12] , [13] . From these studies , it has been found that cAMP secretion is essential for streaming , but not for chemotaxis . Cells lacking adenyl cyclase A , the enzyme primarily responsible for internal cAMP production during aggregation , will chemotax to cAMP without forming streams [14] . Development and chemotaxis to cAMP in cells lacking the gene for PDE1 can be rescued through periodic addition of partially purified PDE1 . Cells lacking PDE1 secretion will chemotax to cAMP and form transient streams to a central source of cAMP , though in linear gradients , such as the under agar assays , the streams appear thicker than wild type [8] . Spontaneous aggregation by developed PDE1 null cells can be recovered with the addition of a uniform bolus of exogenous PDE1 , though the bolus is insufficient to recover the spatial extent of the streams . Because we intend to examine a minimalistic model , we include continuous , local cAMP secretion and a constant background of cAMP degradation . The dynamics of the pre-aggregation stage of D . discoideum development was analyzed by Potel and Mackay [15] , where they observed the motion of cells and calculated various dynamic quantities , such as the mean speed and the mean square displacement of cells and used Furth's persistent motion model [16] , [17] to explain their observations . Futrelle et al . [18] investigated chemotactic response to an external signal for early , middle and late developed cells for different duration and frequencies of cAMP pulses . In particular , the chemotactic index and the speed of the cells during development were analyzed , and significant timescales that define the dynamics were extracted , including the response time to a change in cAMP gradient which they estimated to be on the order of seconds . Gingle [19] measured the smallest cell density , above which collective motion occurs . Gingle and Robertson [20] showed that this limit density depends on the development time of the cells . The spontaneous emergence of traveling waves in a population of D . discoideum cells has attracted interest of the mathematics and physics communities and lead to the development of several computational models to test hypothesis for mechanisms involving signal transduction , signal relay , and gradient sensing . Pioneering work by Martiel and Goldbeter used a differential equation approach based on the receptor activation and desensitization dynamics [21] to explain the pulses of cyclic AMP . In addition to modeling the receptor dynamics , following models studied mechanisms in D . discoideum chemotaxis including wave propagation of cAMP signals in an inhomogeneous excitable medium [9] , [22]–[25] , directional sensing via receptor activation followed by further intracellular signaling [26]–[28] , and physical forces that regulate cell-cell or cell-surface interactions [29]–[32] . Other models of chemotaxis focus on stochastic aspects of the cellular processes . These models discuss mechanisms including stochastic dynamics of directional sensing and speed control [2] , [33]–[36] , ″memory″ associated with membrane deformations [37]–[39] , extension of new pseudopods conditional on the locations of existing ones [40]–[41] . Recent models of chemotaxis study the effects of noise due to fluctuations in receptor binding as well as the noise arising from subsequent internal responses [4] , [42]–[46] . In the simplest models directional sensing is represented as stochastic dynamics of a single angular variable ( which represents the density asymmetry of both the occupied receptors and further downstream processes such as regulation ) . Schienbein et al . [33] showed that the dynamics of the stochastic angle agrees very well with the directional sensing dynamics of granulocytes during galvanotaxis . The stochastic angle model was also implemented for D . discoideum chemotaxis by including receptor kinetics and chemical gradient steepness [4] . In this work we choose to capture the stochastic effects by associating the stochasticity of the previously described angular variable with the measured fluctuations in the direction of motion . The focus of our study is on modeling , simulating , and analyzing collective motion arising from chemotaxis and signal relay . While collective motion , chemotaxis , and signal relay have all been investigated before , this work focuses on collective behavior in the presence of a linear gradient without fluid flow . The linear , no-flow gradient geometry has been used in conjunction with Zigmond chambers and underagar assays but was cumbersome and often replaced with point sources , such as a micropipette , which leads to convergent cell trajectories even in the absence of signal relay . A linear gradient has been recently incorporated into a microfluidic system which can simultaneously monitor multiple gradient conditions and cell lines ( using EZ-TAXIScan system ( ECI , Japan ) [47] ) . By monitoring many parallel conditions we are able to clearly analyze signal relay and differentiate different types of collective motion . It also allows us to validate metrics for detection of collective behavior that should be useful for the analysis of a number of other investigations of cell signaling that are starting to be carried out in this signal geometry . Linear gradients have been introduced for quantitative studies of gradient sensing , but recent work in microfluidics devices has been carried out in chambers with fluid flow which flushes out signal relay ( e . g . , in Refs . [45] , [46] ) . The controlled linear gradient allows us develop a quantitative phenotype for the onset of signal relay between cells . We are able to tune the relative strength of signal relay continuously , by varying the linear gradient strength . This allows us to measure collective behavior based on correlations between cell trajectories . We anticipate that our systematic studies will be valuable for a broad range of investigations of collective cell behavior . Indeed cell trajectories in such linear gradient chambers are starting to be collected to investigate signaling pathways that regulate chemotaxis in various types of cells ( e . g . , D . discoideum [48] , neutrophils [49] , [50] , eosinophils [51] , and osteoclasts [52] ) .
The EZ-TAXIScan system uses an etched silicon chip to form separate channels for chemotaxis experiments in a linear geometry [47] . Each channel contains two buffer wells on opposite sides of a thin , terraced gap ( microns long , mm wide and microns deep ) . Cells are gently pipetted into one well and allowed to settle to the glass surface . The opposite channel is filled with cAMP and diffusion sets a linear gradient in the channel within minutes . Cells , responding to the external signal enter the terraced region and travel microns towards the other side . Parallel to the edges of the terrace are small columns ( microns long , microns apart ) that set the vertical spacing , but provide little impedance to cell motion . If not modulated by cAMP or by PDE1 secreted by the cells , the imposed gradient stays constant at least for minutes [47] , [51] . This type of setup provides a good signaling geometry for separating the effect of intercellular communication and an imposed gradient . Fig . 1A and Fig . 1B show time lapse images of wild-type cells and mutant cells under the influence of a linear ( downward in the figures ) cAMP gradient . At cells placed in a reservoir without cAMP begin to move into the chamber ( at the top boundary in the figures ) . Although the cells are initially introduced uniformly in the horizontal direction ( min panel of Fig . 1A and Fig . 1B ) , wild-type cells are attracted to each other and form streams ( min panel of Fig . 1A ) , which in this example evolve to swirling groups ( min . panel of Fig . 1A ) . The mutual attraction of the cells is due to the enzyme adenyl cyclase A ( ACA ) localized at the back of the cells [14] . ACA synthesizes intracellular cAMP , which diffuses into the extracellular medium . As shown in Fig . 1B , mutant cells ( aca- ) , lacking ACA , do not exhibit collective motion and , throughout the time-course of the experiment , move without streaming or clumping in the direction of the external cAMP gradient . To analyze these observed migratory behaviors , we use a cell tracking algorithm to determine cell displacement vectors over a short time interval of the position of the center of the imaged intensity of each cell . We define a motion angle as the angle of a cell's displacement vector with respect to the imposed cAMP gradient . Fig . 1C shows representative tracks of cells during chemotaxis ( color coded according to real time ) . Fig . 1D shows the distributions of the angle for aca- cells , subject to four different external cAMP gradient strengths , increasing by a factor of from panel to panel . The spread of reflects the competition between noise and the ability of cells to sense and react to the gradient . Note that the width of the distributions first decreases with increasing gradient strength then decreases indicating an optimum . This finding agrees with observations of Fuller et al . [45] , which shows that the chemotactic response is limited by external noise ( noise due to receptor-ligand binding ) for small local cAMP concentration and by internal noise ( noise due to subsequent internal signaling ) for higher local cAMP concentration . The distributions in Fig . 1D show that the cells do not always orient in the direction of the extracellular gradient . As discussed in [53] the gradient-sensing mechanism is stochastic with many sources of noise that can cause random deviation from the direction of the external gradient . Our data for the angular distributions suggest that above a threshold gradient the cell orientation is independent of the gradient strength . Below this threshold ( e . g . , see the panel of Fig . 1D ) , the width of the distribution increases with decrease of the gradient [45] . In what follows we focus on the regime where the cell migration is less sensitive to the gradient strength . For several representative cells , Figs . 2A–C show the time autocorrelation of , where the angle brackets denote an average over time for cells that are located in the region between the cell exit plane and the mid plane of the gradient chamber ( lower half of the panels in Figs . 1A and B , ( the number of cells are , , and , respectively ) ) . The reason for restricting the averaging to the half of the chamber on the cell exit side is to eliminate any bias of the cell orientation angle distribution due to influence of the process of entry into the chamber . For small angles ( ) the autocorrelation is . The variance of , , is plotted as a function of the distance from the starting point of the cells in Fig . 2D for the three different gradient strengths . In the next section we develop a model which estimates the level of the fluctuations in the displacement ( dashed line in Fig . 2D ) . Previous studies on eukaryotic HaCaT cells highlight the dependence of velocity autocorrelations on two time scales [37] . Nevertheless , we see from Figs . 2A–C that can be well fitted to a dependence of the form parametrized by the single characteristic time . The fits for the average correlations for the individual gradient strengths are displayed in Figs . 2A–C . The single time scale , , is approximately constant over the two orders of magnitude in the external cAMP gradient strengths ( min , min and min for Fig . 2A , Fig . 2B , and Fig . 2C ) . This time scale is roughly consistent with the dynamics of contractions of cells [31] . The characteristic size of eukaryotic cells is an order of magnitude larger than that of bacterial cells , and , in contrast with the sensing by bacterial cells , eukaryotic cells can sense the difference in chemoattractant concentration between the front and the back of a cell , thus detecting spatial gradients without moving . For D . discoideum , gradient sensing is accomplished via a G-protein coupled receptor and downstream signaling pathways [54] . Models of chemotaxis treating the cAMP signal transduction mechanism , including the biochemical details such as receptor desensitization [21] and adaptation [55] , demonstrate the emergence of the experimentally observed cAMP waves . In the present paper our modeling approach will differ somewhat from past works ( e . g . , Refs . [9] , [22] , [24] , [56] ) in that we seek a model that is simple enough that its relatively few parameters can be inferred from experiments , yet is still capable of capturing the distinctions between streams and clumps seen in our experiments on D . discoideum . We model cells as self-propelled soft disks of radius . For each cell we specify the location of its center and its orientation by the two-dimensional vectors and ( by definition ) . We specify locations of the cells using a rectangular coordinate system , where the chamber in which the cells move is located in . In the experiment , the chamber boundaries , and , have perforations and are thus permeable to transport of cells and cAMP . The speed of each cell is assumed to be well-approximated as constant in time , independent of signal strength , in agreement with controlled chemotaxis experiments [6] . The cAMP concentration field is denoted . In the experiment the cells are deposited in a large reservoir ( corresponding to in the model ) where there is no externally injected cAMP . This experimental condition is modeled by a Dirichlet boundary condition on the cAMP concentration , at , and by introducing individual discrete cells at with a uniform flux cells per unit time per unit length in ( each newly introduced cell's orientation is initially in the ) . In addition , the experiment has an aqueous solution of cAMP in a large reservoir on the other side of the chamber ( corresponding to in the model ) , and the cAMP concentration in this reservoir stays constant during the course of the experiment . This is modeled by a Dirichlet boundary condition at , , along with the removal of cells when they reach . We applied periodic boundary conditions in , such that and each cell that leaves the chamber at a lateral boundary , or at , reenters the chamber at the other end . Using these definitions , we propose the following minimal , agent-based model for cell motion in our experimental setup , ( 1 ) ( 2 ) ( 3 ) The first equation corresponds to the constant speed assumption . The second equation dictates that the unit vector specifying the cell's orientation is attracted toward the direction of the vector , ( 4 ) with relaxation time . This relaxation time may be thought of as including both the chemically determined time for a cell to ‘perceive’ the gradient , as well as the time it takes the cell to mechanically turn its orientation . The first term in is a unit vector in the direction of the cAMP gradient . Note that , in accord with the observed similarity of the second , third , and fourth panels of Fig . 1D , this term is independent of the level of cAMP ( i . e . , invariant to the transformation ) . The second term in is white noise , ( 5 ) The third term in is a repulsive ‘force’ modeling a soft two-body contact interaction between neighboring cells , ( 6 ) where is the region . In Eq . ( 6 ) we have taken the form of the repulsive force to decrease linearly with distance from the center of the cell . We have also tried other forms for the dependence of the repulsive force and found that no qualitative differences occurred . Szabo et al . [57] and Chate et al . , [58] discuss the effect of adding cohesive ( i . e . , attractive ) forces in modeling tissue cells . The parameter determines the strength of the repulsion force . Eq . ( 3 ) is the diffusion equation governing the evolution of the distribution of the cAMP density , with constant diffusivity [59] . The parameter is the cAMP secretion rate of a cell . The cAMP decays at a rate which can be spatially nonuniform and is approximately proportional to the concentration of the degradation enzyme phosphodiesterase PDE1 [25] . We introduce a degradation inhomogeneity suitable for our experimental setup in the following section . The cAMP degradation rate in Eq . ( 3 ) is meant to account for the presence of the cAMP degrading enzyme PDE1 with assumed proportional to the enzyme density . Since PDE1 is secreted by the cells themselves and then diffuses , we can expect that and hence are time and space dependent quantities obeying an equation similar to Eq . ( 3 ) for the cAMP density , but with the term analogous to the degradation in Eq . ( 3 ) omitted . In the interest of simplicity , for our minimalist model , we wish to circumvent a full time-dependent diffusion equation model for . Instead , we assume that a time-independent steady state that is homogeneous in is established for the ( we show in Text S1 that this is justified for the conditions of our experimental setup ) . This corresponds to depending on but not and , . Furthermore , in steady state , the -averaged cell flux in the -direction must , by conservation of cell number , be independent of in the linear gradient chamber , and its value everywhere in the chamber must be the same as the cell injection flux at . In the simplest case , without clumps , the averaged density of cells in the external linear gradient region will thus be roughly uniform in and of the order of . Thus the averaged PDE1 density , satisfies a one-dimensional , time-independent diffusion equation of the form ( 7 ) Here we approximate as constant in and given by where is the production rate of the PDE1 per cell per unit time; is the diffusivity of the PDE1 and is approximately [60] . In addition , we will argue that the appropriate boundary conditions on the PDE1 density are at and . Solution of Eq . ( 7 ) with these boundary conditions leads to the model , ( 8 ) That is , varies parabolically in ; , and has its maximum value in the center of the chamber , . In our numerical explorations we mostly use the model Eq . ( 8 ) . We also note that in other experiments , depending on the experimental setup , may have different dependence on . For comparison , we repeated our numerical runs with the spatially constant form , where the numerical prefactor is chosen so that the total amount of PDE1 in is the same as for Eq . ( 8 ) ( i . e . , is the same ) . The spatially constant form for was used in other models of D . Discoideum chemotaxis [9] , [21] , [22] , [24] . The results ( shown in Text S1 ) are qualitatively similar to the results presented here . We now outline how we motivate the use of the boundary conditions ( more detailed quantitative justification is given in Text S1 ) . In our experiments , cells are placed in the reservoir located in . The cells then rapidly sink to the bottom of the reservoir ( ) . The reservoir has a vertical thickness that is more than times larger than the vertical thickness of the linear gradient chamber . The same dimensions apply for the reservoir in . The bottom glass surface ( ) of the reservoir in extends into , where it forms the bottom plane of the linear gradient chamber and of the reservoir in . Cells that are on the bottom of the reservoir supply a source of cells for entry at into the linear gradient chamber . The cAMP-degrading-enzyme PDE1 , secreted by cells in the reservoir are assumed to be transported vertically upward by small convection flows in the reservoir fluid into the vertically large region of the reservoir . In contrast , the distribution of the PDE1 emitted by the cells in is constrained to the much thinner vertical region defined by the chamber dimensions . Thus , in the linear gradient chamber the PDE1 density cannot be attenuated to low levels by spreading vertically . As quantitatively shown in Text S1 , based on this consideration , the enzyme density in and is much less than in the interior of the chamber . This leads to our previously stated approximate boundary conditions , , used in obtaining Eq . ( 8 ) . In order to systematically determine the essential dependence of the behavior of the model on its parameters , we introduce appropriate nondimensionalizations . We define the dimensionless spatial coordinates by and . The dimensionless time scale is defined as , and the dimensionless cAMP density is defined as . With the rescaled variables , the cAMP boundary conditions become , and . Additionally , the white noise is transformed to , where . The model equations with the rescaled variables and Eq . ( 8 ) for can now be written as ( 9 ) ( 10 ) ( 11 ) where , , , , and . The integral of the summation over the square , is the number of cells in the unnormalized square , and is roughly . In the situations we investigate is always large compared to unity . Thus the term roughly plays the role of a normalized density whose nominal value is one . With these normalizations , the parameters in our model are , , and . We wish to explore the variation of the system behavior as a function of parameters . This is clearly an impossible task to carry out for the full set of dimensionless parameters just listed . Thus we now seek to restrict our detailed considerations to just a few of these parameters whose influence is , we think , the most interesting . If we regard for the cells as fixed , then the parameter is dictated by the experimental setup . Experimentally , the typical cell speed and hence is observed to be roughly the same for wild type , and mutant cells [6] , and we therefore take as fixed . The noise term will be fixed by the experimental observations ( e . g . , Fig . 1D ) which imply that it does not vary significantly across the different experimental conditions investigated ( see Text S1 ) . Thus we will keep , and fixed at the appropriate estimated values . Furthermore , we suspect that the qualitative behavior of the system will be insensitive to the precise value of so long as ( the situation we are interested in ) . Thus our main numerical model explorations will focus on how the model behavior depends on and . We now further discuss our reason for interest in varying and . First , with respect to , in reference [8] a genetic perturbation to the cells results in mutants lacking the ability to produce the degradation enzyme PDE1 ( but still emitting cAMP ) . In our model this corresponds to setting . In our numerical experiments we will explore a continuous dependence on , partly because is not well determined , but also to understand the difference between mutant cells that do not emit PDE1 ( i . e . , pdsA-/PEC cells ) and wild-type cells . We also suggest that it may be useful for future experiments to explore continuous dependence on PDE1 secretion rate ( i . e . , ) which might be realized by introducing a mixture of wild-type and mutant PDE1- cells . Regarding variations of , we note that the secretion of cAMP from cells , is biologically inhibited for another type of mutant , the aca- cells . Also , in our experiments , we change the external concentration of cAMP , . The biological and chemical changing of the parameters , and , both yield change of . ( Also , could be tuned by changing the reservoir cell density and hence , but we have kept constant in our experiments . ) Aside from and the parameters we used in our simulations are summarized in Table 1 . We assume that the cell parameters in this table ( i . e . , , , , , , ) are the same for wild-type cells ( ) and mutant cells ( ) . In the absence of mutual attractions through cell's secretion of cAMP , a Fokker-Planck version of Eqs . ( 1–6 ) can be solved analytically ( see the Text S1 ) , and in Eq . ( 5 ) can be determined by matching the analytical result to experimental observations of mutant cells . Also , we estimate as being of the order of determined from our experimentally observed time-autocorrelation of the orientation vector ( Fig . 2A ) , where is defined at the end of the previous section . This time scale is comparable to the contraction rate of D . discoideum cells which in the work of Satulovsky et al . [31] is considered as the bulk relaxation time . We note that the real cells' secretion rates of cAMP and of PDE1 are not well quantified and can be varied by drug treatment or by the use of mutant cells . Thus we will regard and the PDE1-level-dependent parameter as variable parameters and investigate how the dependence of the collective cell dynamics depends on them . The model equations , Eqs . ( 1–6 ) are simulated numerically . Figs . 3A–3C show representative cell tracks for three different values of the normalized cAMP secretion rate . For all three of these cases is fixed at , which we estimate to be consistent with previous experimental measurements [21] . The color at a given point on a cell track in Figs . 3A–3C indicates the time that the cell making the track was at that point , where red corresponds to the beginning of the simulation and blue corresponds to the end of the simulation . Figs . 3D–3F show representative snapshots , where the position and the orientation of the cell is indicated by an ellipse ( at normalized time for D , E , and F ) . In the top panels of Fig . 3 ( Figs . 3A and 3D ) , the relative cAMP secretion rate is very small ( i . e . , ) . This regime mimics the aca- mutant cells , and our numerical results qualitatively agree with the experimental observations of aca- cells ( cf . , min panel of Fig . 1B ) . For larger values of , and depending on , our numerical results can be classified under two main categories , streams ( Fig . 3E ) and clumps ( Fig . 3F ) . At moderate ( Fig . 3E ) streams are evident . At higher , Fig . 3F shows that multiple clumps of cells form . From the corresponding tracks of cells shown in Fig . 3C , it is seen that the cells stay within the clumps and the clumps have almost no motion in the direction . To go beyond the visual comparison of our simulation results with our experimental observations , a quantitative description of the three modes of group cell motion described above ( i . e . , uncorrelated motion , streams , and clumps ) is desirable . We define the normalized mean progression , by , where the angle brackets denote an average of cells in the region between and , where ( cf . , [61] , [62] ) . We denote by the average of over , and we denote by the time average of taken over the last quarter of the simulation ( ) . Another useful measure is the normalized averaged cell density , computed by averaging over the region to with and normalized so that . First , Fig . 4A shows the ensemble average of , denoted by , for the aca- cell experiment ( in gray ) and for a single model simulation ( in black ) . The model parameters for the run are and , which correspond to the aca- mutant cells . To make a fair comparison , for the experimentally obtained we filtered out cells that move at a slower speed than what we considered in our model ( i . e . , ) . We calculate for a group of randomly selected cells in the region . Since our tracking algorithm cannot track all the cells available in this region , the experimentally obtained is represented by this ensemble average . To compare our experimental result to our numerical simulation results , we calculate from our simulation by sampling cells in the simulation so as to match the number of cells for which is experimentally calculated . We show in Figs . 4B and 4C how , and vary with the distance from the cell reservoir , , for the three values of used to obtain the cell tracks shown in Fig . 3 with fixed at the same value used for Fig . 3 . In these plots , , and are averaged over several runs ( this average is denoted by ) , where the error in the mean is shown by vertical error bars , which is calculated by the standard deviations of the runs divided by the square root of the number of runs . In the low regime ( solid curves in Figs . 4B and 4C ) , corresponding to Figs . 3A and 3D , Fig . 4A shows that , saturates to in the upper half of the gradient chamber , , while Fig . 4B shows that is approximately uniform . The density profiles measured from the time lapse images ( a rough estimate calculated from the image intensity ) fairly agree with those obtained from our simulations . For PDE1- cells , our model suggests that the cAMP secretion levels are small compared to the wild-type cells exposed to the same imposed gradient . The density profiles measured from the time lapse images ( a rough estimate calculated from the image intensity ) fairly agree with those obtained from our simulations . For PDE1- mutant cells , our model suggests that the cAMP secretion levels are small compared to the wild-type cells exposed to the same imposed gradient . In determining the cAMP secretion rate we assumed same noise level compared to the wild-type cells . Therefore , in conjunction with findings from our model , our experimental observations suggest that the lack of degradation of external cAMP results in either reduced signal relay or increased noise level in gradient sensing ( corresponding to receptor desensitization ) . The comparison and the details of the density estimate are shown in Text S1 . As shown in Figs . 3B and 3E , for , streams emerge in the regime of moderate ( plotted as the gray dashed curves in Figs . 4B and 4C ) . These streams start to aggregate in the upper half of the gradient chamber , and this results in a decrease in and a corresponding increase in . Compared to the low regime , the streams cause an increase in the cell density ( the peak at ) . In the high regime ( plotted as the black dashed curves in Figs . 4B and 4C ) , is even more peaked than in the moderate regime . This apparently leads to a peak in the cAMP density which leads cells to start aggregating in the lower half of the gradient chamber . Streams form close to the reservoir , where cells enter the gradient chamber . To form streams , newly entering cells acquire laterally ( -directed ) converging velocity components . Since the cell speeds are fixed at , this causes to decrease ( see the region in Fig . 4B ) and to increase . This apparently leads to a more localized secretion of cAMP , which overcomes the externally imposed cAMP concentration causing the clumping seen in Figs . 3C and 3F . In Fig . 4D the maximum in the region is plotted versus the corresponding . Each point in this figure is obtained from a single numerical run . The points are color coded with respect to the and used in the numerical run . Fig . 4D shows that points are clustered in two regions . The first region , where is large and is small [ ( ) , ] , corresponds to large clumps , while the second region , where is small and is large [ ( ) , ] , corresponds to the uncorrelated motion . The points between these two regions correspond to runs where cells form streams which either generate clumps ( i . e . , points closer to the first region ) or move through the region and leave the gradient chamber ( i . e . , points closer to the second region ) . In our model there are two time scales , and ( the cAMP degradation rate and the local cAMP production rate ) , and we explored their effects . Fig . 5 shows results for averaged over and ( i . e . , the last quarter of the simulation ) , as well as over a large number of model simulations ( ) . These averages are labeled in the figure . The top panel of Fig . 5A shows as a function of for . Fig . 5A shows that decreases as increases . In the region , where decreases fastest , streams occur , but clumps are rare ( e . g . , Figs . 3B and 3E ) . The bottom panel of Fig . 5A is for a very small value of ( ) , modeling mutant cells that cannot degrade cAMP . In this case we see that there is a sharp decrease in in the range . Below this range the simulations show roughly uniform cell density , while above this range clumps occur . Compared to the slow degradation regime , in the fast degradation regime ( top panel of Fig . 5A ) the streaming behavior is robust . In the slow degradation regime , the streams form for only a short period which is followed by formation of clumps . Recent experiments demonstrate that stream formation is impaired , if cells cannot degrade external cAMP [8] . Fig . 5B summarizes results for our simulations ( color coded ) , as a function of ( plotted on the horizontal axis ) and ( plotted on the vertical axis ) . The data in the top ( bottom ) panel of Fig . 5A corresponds to a horizontal cut through Fig . 5B at the arrow , ( ) , on the vertical axis of Fig . 5B . Fig . 5B shows that the width of the range of , where streams occur , decreases as is lowered . Additionally , the onset of stream generation with respect to becomes smaller with decreasing .
Our model explains different observed modes of collective motion of motile cells . Our main new finding is that signal relay alone is not enough to arrange migrating cells into collectively moving streams . However , when the signal is not only relayed but also degraded , stable streams form . Our model is minimal , involving a relatively small number of potentially experimentally deducible parameters . Based on our numerical results , we suggest experiments where the transition between streaming and clumping can be experimentally tested by changing the effective values of our model parameters . One suggestion is that the value of can be effectively reduced by either mixing wild-type and PDE1- mutants or by changing the amount of PDE1 added during the PDE1- mutant cell development . The relaxation time , obtained from our experimental observations , is associated with the membrane retraction time scale . In addition , the time scale corresponding to the noise amplitude is associated with the formation time of pseudopods [63] . These parameters could be altered by adding drugs or changing the developmental procedures . For example , introducing a drug that inhibits the PI3 kinase severely reduces the pseudopod generation frequency [63] and hence both and . Additionally , recent studies show drastic change in the collective motion behavior of wild type cells when they are prepared over a longer development time [64] . In this case and are reduced in agreement with the observed reduction of stream formation [64] . Thus , we believe that our model can be utilized to quantify changes in the collective motion in response to modifications of cell characteristics . In our model , we have only focused on the extracellular cAMP dynamics given in Eq . ( 3 ) with the objective of reproducing the patterns in Fig . 1 with as few physical processes as possible . We modeled the motion of the cells according to the the dynamics of sensing the signal with the phenomenological equation Eq . ( 2 ) . Models that include additional processes ( not included in our model ) are capable of explaining additional phenomena . E . g . , models of cAMP signal transduction including receptor desensitization [21] and adaptation [55] show the generation of experimentally observed cAMP waves including spiral waves [3] , [9] , [56] . In addition , the observed rotating vortex structure of the aggregates can be explained by other self-propelled particle models which allow cells to adjust their propulsive force [65] . In the future we plan to modifying our model to investigate the effect of dynamic cell-cell adhesion in stabilizing stream formation , and aggregation . Our model can be extended to include competition between the gradient steepness , , and the local cAMP concentration , , by modifying Eq . ( 4 ) and introducing a competition between the noise intensity and the concentration of the cAMP . A simple approach is to impose the following limits: For small local cAMP concentration , the noise ( second term in Eq . ( 4 ) ) will have a higher effect in the directionality ( i . e . independent random motion ) . In contrast , for high local cAMP concentration , the contribution from the noise to local cAMP concentration ratio should be small compared to the gradient steepness to local cAMP concentration ratio . When the model is extended to include this competition , we can define an organization time scale as a measure of cellular organization . Thus , we can measure the efficiency of stream formation not only with respect to signal relay but also with respect to the efficiency of directional sensing . We believe that our simplified approach , used here for D . discoideum can be extended to more complex cells exhibiting signal relay , such as neutrophils [49] , [66] . For neutrophils , signal relay is less well understood [49] . However , our numerical simulations can be utilized to distinguish uncorrelated motion from weak signal relay: Using our simulations in conjunction with linear gradient experiments where cells do not converge naturally to an external signal , we can calculate the effect of signal relay on the mean progression speed , as well as the development of an inhomogeneous density due to cell-cell attraction , even in the case of very small signal relay that is not sufficient to lead to discernible clumps or streams . Moreover , our model can be potentially extended to include the dependence of signal relay on cell density , in order to compare the dynamics to those observed in Ref . [67] , which proposes a quorum sensing mechanism that can quantify the persistent random walk of D . discoideum at different phases of development as well as different densities . Another potential use of our model is to model migration when subpopulations of cells have different signal sensing , and signal relay capabilities . A prominent example of such collective migration is the motion of neural crest cells , a collective process during embryonic development . Recent experiments suggest that mathematical models of the neural crest migration require subpopulations having different chemotactic responses [68] .
To examine the chemotactic dose response , cell migration was recorded at second intervals for 1 hour in the EZ-TAXIScan chamber ( Effector Cell Institute , Tokyo , Japan ) . In the absence of wild-type cells the device establishes a well-defined , stable cAMP gradient during the course of the experiment [47] . Dictyostelium discoideum cells , wild-type cells ( ax3 ) and its ACA null mutant cells ( aca- ) were prepared as described previously in Ref . [6] . PDE1- cells were prepared as described previously in Ref . [8] . There are two modules in our numerical simulation code , the first module consists of the equations of motion given in Eqs . ( 1 ) – ( 3 ) which defines the position and the direction of motion of cells based on the local gradient in the neighborhood of each cell . The second module calculates the diffusive time evolution of cAMP due to the external signal and dynamic local intercellular signals and provides the updated gradient vector field for use in the first module . Simultaneous evaluation of these two modules generates cell tracks . The diffusion equation ( Eq . ( 3 ) ) for the cAMP is solved explicitly on a square grid with spacing m using a forward time and central space Euler method . In the numerical simulations the time step is seconds , which is well in the stable range of the numerical algorithm . For implementing the numerical evaluation of the diffusion equation is discretized with and . The Laplace operator can be replaced by the discretized Laplace operator and the Dirac- function is discretized in one dimension as , where is the Kronecker- function , it is zero except for . Thus , the value of the cAMP field at and , where and are integers , is updated according to ( 12 ) with . In Eq . ( 12 ) , rounds its argument to the nearest integer . The same is used in evaluating the equations of motion ( Eqs . ( 1 ) and ( 2 ) ) . Table 1 shows the definitions and values of the parameters used in the numerical simulations . | Collective cell migration is observed in various biological processes including angiogenesis , gastrulation , fruiting body formation , and wound healing . Dictyostelium discoideum , for example , exhibits highly dynamic patterns such as streams and clumps during its early phases of collective motion and has served as a model organism for the study of collective migration . In this study , facilitated by experiments , we develop a conceptual , minimalistic , computational model to analyze the dynamical processes leading to the emergence of collective patterns and the associated dependence on the external injection of a cAMP signal , the intercellular cAMP secretion rate , and the cAMP degradation rate . We demonstrate that degradation is necessary to reproduce the experimentally observed collective migration patterns , and show how our model can be utilized to uncover basic dependences of migration modes on cell characteristics . Our numerical observations elucidate the different possible types of motion and quantify the onset of collective motion . Thus , the model allows us to distinguish noisy motion guided by the external signal from weakly correlated motion . | [
"Abstract",
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"Results",
"Discussion",
"Methods"
] | [
"physics",
"developmental",
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] | 2013 | Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups |
The genetic basis of morphological differences among species is still poorly understood . We investigated the genetic basis of sex-specific differences in wing size between two closely related species of Nasonia by positional cloning a major male-specific locus , wing-size1 ( ws1 ) . Male wing size increases by 45% through cell size and cell number changes when the ws1 allele from N . giraulti is backcrossed into a N . vitripennis genetic background . A positional cloning approach was used to fine-scale map the ws1 locus to a 13 . 5 kilobase region . This region falls between prospero ( a transcription factor involved in neurogenesis ) and the master sex-determining gene doublesex . It contains the 5′-UTR and cis-regulatory domain of doublesex , and no coding sequence . Wing size reduction correlates with an increase in doublesex expression level that is specific to developing male wings . Our results indicate that non-coding changes are responsible for recent divergence in sex-specific morphology between two closely related species . We have not yet resolved whether wing size evolution at the ws1 locus is caused by regulatory alterations of dsx or prospero , or by another mechanism . This study demonstrates the feasibility of efficient positional cloning of quantitative trait loci ( QTL ) involved in a broad array of phenotypic differences among Nasonia species .
Somatic sexual differentiation is an ancient feature of animals , yet sex differences in morphological traits can evolve rapidly . Because of this , between-species genetic analysis of recently evolved sexual differences has been proposed as a way of identifying the genes and genetic changes that underlie morphological diversification [1] . For example , Kopp et al . [2] have found that a sex-specific abdominal pigmentation difference that recently evolved between Drosophila species is caused by non-coding cis-regulatory changes in the bric-a-brac gene , changes which involve binding sites for conserved transcription factors doublesex and ABD-B [3] . The study of recently evolved sex differences can therefore reveal changes in tissue- and sex-specific gene regulatory networks . Nevertheless , there have been few studies investigating the genetic and molecular basis of the recent evolution of morphological differences between species , due in part to the difficulty of conducting genetic analyses in diverged species that are often reproductively incompatible . An active debate concerns whether the evolution of differences between species are due primarily to cis-regulatory or protein coding changes ( e . g . , [4]–[8] ) . While protein-coding changes have been the focus of most historical studies of phenotypic evolution , it has been argued that changes to non-coding cis-regulatory elements may be more important , as they are crucial to the spatiotemporal control of gene expression in development and can change with potentially fewer pleiotropic effects on other processes [4] , [5] . However , empirical support for this claim is limited , largely by the difficulty of determining the genetic basis of phenotypic changes to a fine enough level to distinguish between cis and protein-coding changes [6] . An additional issue concerns whether the standing genetic variation for phenotypes within populations represent the same spectrum of mutations that go to fixation and become involved in species differences in phenotype [7] . Therefore , additional genetic studies of phenotypic evolution in recently diverged species are needed to help reveal the processes by which new morphologies evolve and the relative roles of cis-regulatory versus protein-coding changes in morphological evolution . Here we investigate the genetic basis of male-specific differences between two species of Nasonia , N . vitripennis and N . giraulti . Nasonia is a complex of four closely related parasitic wasp species that is rapidly emerging as a model for evolutionary and developmental genetics [9] , [10] . Nasonia males are haploid , and therefore can be readily genotyped for visible and molecular markers regardless of marker dominance . Furthermore , unlike most organisms , Nasonia species can be made inter-fertile in the lab by removing bacterial symbionts ( Wolbachia ) that cause sperm-egg incompatibilities among the species [11] , [12] . This permits movement of genes involved in phenotypic differences between the species by backcrossing [13]–[15] . Utilizing flanking visible and recessive lethal mutations and genetic and genomic tools in Nasonia , positional cloning of genes involved in species differences can then be accomplished [9] . N . giraulti males have large wings ( Figure 1 ) and are capable of flight , whereas N . vitripennis males have vestigial wings and do not fly , although they use them in courtship and agonistic displays [16] . A major portion of the male-specific wing-size difference is due to two loci , wing-size1 ( ws1 ) and widerwing ( wdw ) [13] , [15] . Both ws1 and wdw increase wing size in a sex specific fashion , as seen when introgressed from N . giraulti by backcrossing into an N . vitripennis background . In this study , we positionally clone the ws1 locus to a 13 . 5 Kb non-coding region , which falls near the sex determining locus doublesex [17] , [18] and includes its 5′ UTR . This is the first positional cloning of a gene in Nasonia , and the study illustrates methods for utilizing haplodiploidy for efficient cloning of interspecies QTL in this genetic system .
Nasonia wings are composed of a larger forewing and smaller hindwing . Here we focus our attention on the forewing , although more subtle differences in the hindwing are also found between the species and sexes . N . giraulti male forewings are 2 . 16 fold larger in area than N . vitripennis male forewings , although female wings of both species are large and more similar in size ( Figure 1; Table 1; [13] , [15] ) . Weston et al . [13] previously identified a major locus affecting the interspecies male wing size difference , called wing-size-1 ( ws1 ) . The giraulti allele at this locus ( ws1g ) was shown to increase wing size by approximately 60% when introgressed from N . giraulti into a N . vitripennis background , accounting for 44% of the species difference . To positional clone this major sex-specific wing QTL and to more precisely describe its phenotypic effects we ( a ) reduced the size of the introgressed sequence flanking the ws1 locus to a 40kb segment ( see fine-scale mapping and cloning below ) and ( b ) backcrossed the introgressed ws1g segment into a standard N . vitripennis strain ( AsymCX ) genetic background for >10 generations . This strain is referred to as ws1gV_40kb , and is used to more precisely assess the effects of the ws1g allele on wing size in comparison to the ws1v allele in the same genetic background . Overall male forewing area of ws1gV_40kb is 45% larger than the wild-type ws1vV allele ( Figure 1; Table 1; Tukey's HSD test , p<0 . 001 ) , and the locus accounts for 39% of the species difference in male wing area . Male forewing length and width are similarly increased ( Table 1; HSD tests , p<0 . 001 ) . In contrast , female wing length , width and area are unaffected by the ws1 allele ( Table 1; HSD tests; p>0 . 05 ) , confirming the sex-specific effects of this locus . A more detailed analysis of phenotype was conducted using setae ( wing cell hairs ) to estimate cell size and cell number effects of ws1g . Setae have also been used in Drosophila to estimate the relative contribution of cell size and cell number to wing size ( e . g . , [19] ) . In Nasonia , setae cover the distal portion of the wing , but are sparse in the proximal portion ( Figure 1 ) . Most of the size increase due to ws1g is in the distal portion of the wing as well ( 73% increase distal to the costal cell versus 21% increase proximal ) . We therefore estimated seta densities in the distal portion of the adult wing after first establishing that there was a relationship between cell number and seta number . Cell density per seta in pupal wings was estimated by DAPI and phalloidin staining ( Figure S1 ) . The average number of cells per seta in N . vitripennis male forewings is 3 . 2±0 . 4 SD , compared to 4 . 6±0 . 4 SD in N . giraulti ( Mann-Whitney U-test , p<0 . 05 , n = 12 ) . In contrast , the ws1gV introgression shows the same density of cells per seta as N . vitripennis ( 3 . 2±0 . 3 SD for each; U-test , p>0 . 05 , n = 12 ) , indicating that this species difference is not under the genetic control of the ws1 locus . We then estimated cell number by counting total seta numbers on the distal portion of the adult wing and estimated cell size by calculating the distance to each seta's nearest neighbors . Based on these calculations , the wslg allele increases overall cell size by 21%±3% ( SD ) and cell number by 45%±5% , resulting in a 73%±10% increase in area of the distal half of the wing ( Table 2; HSD tests , p<0 . 05 ) . It would be useful to know whether large or small male wing-size is ancestral in the Nasonia lineage . Other closely related species ( e . g . Trichomalopsis sarcophagae , T . dubia , and Urolepis rufipes ) have males with large functional wings , suggesting that this state is ancestral . However , the situation is complicated by the fact that the most basal diverging Nasonia species , N . vitripennis , has small wings ( Figure 1 ) , whereas the other species form a monophyletic clade [20] that contains both species with intermediate ( N . longicornis ) and large winged males ( N . giraulti and N . oneida ) [15] , [20] . Thus , we can postulate that either ( a ) male wing reduction began in the common ancestor to all four species , with subsequent increase in the lineage leading to N . giraulti and N . oneida , ( b ) smaller male wing size has independently evolved in N . vitripennis and N . longicornis , or ( c ) there has been introgression of one or more small-wing alleles between N . vitripennis and N . longicornis . Resolution of these alternatives will require more detailed phenotypic and sequence evaluation of the QTL involved in sex specific wing evolution . Positional cloning of the ws1 locus involved the following steps: ( a ) recessive lethals flanking ws1 were generated using already identified linked visible mutants , ( b ) these were then used to sequentially generate a set of recombinants on both sides of ws1 for fine-scale mapping and cloning of the gene , ( c ) a molecular ( AFLP ) marker tightly linked to ws1 was identified by genotyping recombinants , ( d ) this marker was then used to identify a set of BACs covering the region , which were assembled into contigs [21] , ( e ) PCR based markers were developed for determining recombination intervals within the region using sequences from the BAC containing the AFLP marker , end sequences of flanking BACs , and corresponding vitripennis and/or giraulti markers ( Table S1 ) , ( f ) a set of increasingly finer-scale recombinants were screened to delineate the ws1 region and finally ( g ) additional sequence analysis within the cloned region was conducted to identify features within the region and differences between N . vitripennis and N . giraulti . The latter effort was enhanced by the availability of genome sequences for N . vitripennis ( Genbank AAZX00000000 ) and N . giraulti ( Genbank ADAO00000000 ) [10] which became available during the course of this project . The method of assembly of BAC contigs is described in [21] . The approach for generating linked lethals and using these for cloning of QTL is described in methods and shown in Figure 2 . Due to male haploidy , this method can be used to efficiently screen for recessive lethals linked to any gene of interest within the genome . Briefly , new lethal mutations linked to ws1 were generated by EMS mutagenesis followed by screening for linkage of the lethal to ws1 . Use of custom-made lethals in this approach was effective because non-recombinant haploid males with the genotype lethal ws1g die and , therefore , the only surviving males carrying the ws1g allele are recombinants between the lethal and wing size locus ( + ws1g ) ( Figure 2 ) . These tightly linked lethals increased the “effective” discovery rate of recombinants within the region by 100–200 fold ( Figure 2 ) , greatly enhancing efficiency of the positional cloning effort . Thus , we were able to positionally clone ws1 despite a 10-fold lower recombination rate in this region , 0 . 10–0 . 14 centimorgan/megabase ( cM/Mb ) relative to the genome-wide average of 1 . 4–1 . 5 cM/Mb [22] . We first isolated the giraulti allele of ws1 by backcrossing into a vitripennis background and mapping the locus using visible , lethal and molecular markers ( Figure 3A , see also Materials and Methods ) . Using one line ( Rec1 , Figure 3C ) generated by recombination to the flanking recessive lethal D4 , we then generated recombinants on the opposite side between ws1 and the flanking visible marker bl13 , resulting in a recombinant line showing the ws1g phenotype with only 40kb of introgressed giraulti sequence . This large-winged recombinant strain ( ws1gV_40kb ) is used for our detailed phenotypic and gene expression analyses . Screening of additional recombinant males further reduced the size of the region known to cause the ws1g large-wing phenotype to 13 . 5kb ( Figure 3C ) . Positional cloning ws1 shows it to fall adjacent to the protein coding region of doublesex ( dsx , GeneID 100302336 ) , a master sex determination gene found from nematodes to mammals [17] , [18] . The 13 . 5kb ws1 region contains the dsx 5′UTR , promoter and presumed cis-regulatory region but notably excludes the protein coding regions of dsx or any other gene ( Figure 3B ) . This gene was confirmed as the Nasonia ortholog of doublesex based on protein domains , expression pattern , sex specific splice forms , and experimental demonstration that splice forms are associated with sex in a gynandromorph producing line [18] . This previous work on Nasonia dsx [18] confirmed the coding regions , 5′UTR , 3′UTR , exons and introns for both male and female specific splice-forms ( shown in Figure S2 ) in both N . vitripennis and N . giraulti . Upstream of the ws1 region , the nearest gene was identified by BLAST to be a homolog of the D . melanogaster gene prospero ( pros , GeneID 100118692 , [18] ) , 24 . 5kb away from the ws1 region ( Figure 3B ) . In Drosophila , prospero is a transcription factor that specifies cell fate and cell growth in the nervous system [23] . We also identified a single EST ( Genbank EV431998 ) within the 13 . 5kb region from the Nasonia EST dataset [10] . The EV431998 EST contains 5–8 stop codons in each frame and is not spliced , and therefore does not appear to be a protein-coding gene . RT-PCR of EV431998 failed to detect this transcript from wing , leg or whole prepupa cDNA , whereas the primers did amplify from genomic DNA controls . The evidence therefore indicates that the ws1 phenotype is due to non-coding DNA within the 13 . 5kb region . A number of sequence differences occur between giraulti and vitripennis in the 13 . 5kb ws1 region ( Figure 3D ) , including single nucleotide polymorphisms ( SNPs ) , insertions/deletions ( indels ) and the insertion of a foldback transposable element into vitripennis . We sequenced the full foldback element and found that it does not contain protein coding sequences , but two inverted repeats of approximately 1 . 5kb and two small “stem loop” regions . Investigation of intraspecific variation in the 13 . 5kb region and ultra-fine scale mapping is now being undertaken to narrow the region that is involved in the ws1 phenotype . Based on our findings revealing ws1 to contain the 5′ non-coding region of doublesex , we next determined whether this region affects dsx expression . Nasonia doublesex has male and female splice-forms , and experimental evidence supports its role in sex determination in Nasonia [18] . As expected , the male splice-form of dsx ( dsxM ) is present in developing male wings of vitripennis ( ws1vV ) and ws1gV_40kb ( Figure S3 ) . However , quantitative RT-PCR reveals an estimated 2 . 4X higher level of dsxM transcript in developing prepupal male wings of ws1v relative to ws1g in the same vitripennis genetic background ( Figure 4; U-test , p = 0 . 04 , n = 7 biological replicates ) . In contrast , there is no significant difference between the two genotypes in dsxM transcript levels in male legs or whole pre-pupae ( Figure 4; U-tests , p>0 . 05 , n = 5 ) . We also measured expression of dsx in female wings using non-sex-specific primers and observed no clear difference due to ws1g ( median 1 . 01x expression difference , n = 3; Table 3 ) . No expression difference was found in male wings for the flanking gene prospero ( Table 3; median 1 . 07x expression difference between ws1vV and ws1gV_40kb ) . We note that while the introgression appeared to have no effect on pros expression in wings , higher pros transcript levels were observed in ws1vV_40kb whole prepupae ( Table 3 ) . A likely explanation for this whole-body effect is that the larger 40kb of giraulti sequence in the tested strain extends over part of the pros gene , possibly affecting its regulation in whole body . These gene expression data provide a sex- and tissue-specific correlation between dsx expression level and wing size . As with other positional cloning studies , our data do not rule out alternative scenarios that could link causative sequence changes in the 13 . 5kb region to wing size , such as ( undetected ) effects on prospero or long-distance regulation of a different gene . However , we do note that we started with a sex-specific phenotype and “walked” to a region adjacent to a gene known to be involved in somatic sex determination . Future work will be geared towards determining how ws1 affects sex-specific changes in wing development , and specifically whether changes in dsx expression level causally influence male wing size in Nasonia . Our results show that non-coding changes are responsible for the ws1 male-specific wing phenotype . Unlike studies of candidate genes involved in sex differentiation , the positional cloning approach is candidate-blind , so it is intriguing that the region we identified as causing a sex specific increase in wing size ( ws1 ) also contains the 5′ UTR of the sex-signaling gene doublesex . Nevertheless a causal relationship between ws1 and dsx has not yet been established . Previous studies [3] , [24] have implicated dsx in the evolution of sex-specific morphology . But rather than changes in doublesex itself , these studies revealed changes in downstream targets of dsx , via changes to specific DNA sequences to which DSX protein binds in the cis-regulatory regions of the bric-a-brac and desatF genes and affecting sex differences in abdominal pigmentation and pheromone production . In this study , we observed tissue-specific changes in dsx level , possibly due to cis-regulation . Dsx expression level manipulation has been found to affect cell number of a sex-specific cell type in the Drosophila brain [25] . If dsx is indeed the mechanism behind ws1 , it would be the second case of dsx regulating sex-specific cell proliferation . Further , it would suggest that sex-specific morphology can evolve by spatially regulated changes in expression within the sex-determining pathway without disrupting other sex-determination functions . Other molecular mechanisms linking the cloned 13 . 5kb ws1 region to wing size evolution could also occur , including cis-regulation of prospero , changes in non-coding RNAs , or long-distance regulation of another gene . Prospero is of particular interest because it is a transcription factor known to regulate cell proliferation in the Drosophila nervous system [23] . Cell size and cell number regulation are crucial elements of both organ size determination and control of human diseases such as cancer and diabetes [26] . Understanding how growth regulation can evolve therefore has the potential to broaden our knowledge of the operation of these gene networks . One notable example of organ size evolution is fw2 . 2 , which regulates tomato fruit size via cell number changes [27] . This gene was positionally cloned [27] and found to be a cell cycle regulator in plants [28] . In Nasonia , non-coding cis-regulatory evolution at the ws1 locus causes changes in both cell size and cell number . The two genes flanking the ws1 region , doublesex and prospero , have both been found to regulate neuronal cell numbers in Drosophila , and doublesex does so sex-specifically [23] , [25] . How a 45% change in organ size might be achieved by either of these genes , each of which has a conserved homologue in the human genome , remains to be determined . This study demonstrates the feasibility of positional cloning genes in Nasonia . A number of biologically important phenotypic differences occur between Nasonia species , which are ripe for genetic investigation using this approach , such as wing and antennal morphology [20] , [29]–[30] , host preference [14] , pheromones and cuticular hydrocarbons [31] , diapause [32] , hybrid incompatibility [33]–[35] , male courtship behavior [36] and female mate preference [20] , [37] . The four known Nasonia species are all inter-fertile in the laboratory , facilitating the isolation of genes involved in complex trait differences between each species [15] , [30] . The availability of genome sequences [10] combined with the haplodiploid positional cloning methods described here now make it possible to determine the evolution of these complex traits on a molecular level in this emerging model organism [9] .
Wing measurements were conducted using the inbred N . vitripennis strain AsymC and inbred N . giraulti strain R16A; these data are also reported in [15] . Gene expression experiments were conducted with the N . vitripennis AsymCX strain used for genome sequencing [10] , which was derived from AsymC by multiple generations of sib-mating . All wing size and gene expression experiments used the minimal-introgression ws1gV strain ws1gV_40kb , produced by backcrossing and selection for recombinants between ws1 and linked visible and lethal mutants ( see Positional Cloning below ) . This strain contains ∼40Kb of introgressed giraulti DNA containing ws1g in a vitripennis genetic background . It was constructed by backcrossing males from minimal-recombinant strain wm114 ( Rec 4 in Figure 3C ) into AsymCX for 10 generations to produce a homogeneous genetic background . Wild-type N . vitripennis and N . giraulti are also referred to as ws1vV and ws1gG in the text . Wing measurements were performed as in [15] . Briefly , individual females were given two Sarcophaga bullata hosts for 48 hours at 25C after host-feeding for 24 hours on two hosts ( which were discarded ) . Male wing samples were collected from the offspring of single virgin females , while female wing samples were collected from the offspring of single mated females . Adult wings were dissected at the hinge adjoining the thorax and dry mounted on microscope slides under coverslips . Five individuals per family for 5–8 families were mounted; occasional damaged or misshapen wings meant that four individuals per family were measured . Wings were photographed on a Zeiss AxioImager Z1 compound scope at 10X as mosaic images . Measurements were performed on the wing images using AxioVision 4 . 6 software ( Zeiss ) . Wing length , width , area , and head width ( inter-ocular distance , a measure of body size ) were measured as in [15] . Briefly , wing length is the distance between a notch at the proximal anterior end of the costal cell and the distal tip of the forewing . Wing width is the distance perpendicular to the length axis between the most anterior and most posterior points on the wing . Wing area is defined by outlining the wing starting at the proximal anterior notch . Measurements of wild-type N . vitripennis and N . giraulti shown here ( Figure 1 ) are also reported in [15] . Images of ws1gG and ws1vV male wings shown in Figure 1 were cropped to remove other mounted wings which appear in frame but are not related to the displayed image . Setae , hair-like structures produced by cells on the wing blade , were used to infer changes to cell size and number . This approach has been used to estimate cell size in other insects , particularly Drosophila [19] . To determine if seta number is a reasonable estimator of cell number in Nasonia , the number of cells per seta was determined at the red-orange-eye pupal stage , where setae are most distinguishable before the wing sclerotizes and cell nuclei disappear . Pupal male forewings were clipped and dissected from the cuticle in 1x TBST ( 6g Tris , 8 . 76g NaCl , 1mL Tween , 0 . 2g NaN3 , 1L H2O , pH 7 . 5 ) then fixed on lysine-coated slides in 3 . 7% formaldehyde . Slides were stained with DAPI and Alexa Fluor488-Phalloidin ( Invitrogen , Carlsbad , CA , USA ) and mounted in ProLong Gold ( Invitrogen ) . The wing is not completely expanded at this stage and has some three-dimensional structure ( Figure S1 ) . Therefore , wings were imaged at 20x as mosaics under multiple focal planes , so that setae on both wing surfaces and all nuclei could be detected . All setae and nuclei were then counted within a 30 µm radius circle placed between the stigma and the distal tip of the forewing . Cell size and cell number estimates were derived from seta measurements on adult wings . Seta number and area per seta were counted in the distal half of the wing , where setae occur , following [15] . Specifically , a subset of the mounted adult male forewings was re-imaged at 20x through multiple focal planes . Each seta on the dorsal surface of the wing was counted and the area of the seta-containing part of the wing was measured ( defined as the area distal to the costal cell , based on the length ( proximal-distal ) axis described above; [15] ) . Cell number was inferred from the total seta number . Cell size was inferred by estimating the mean area occupied by each seta based on nearest neighbor distances using a custom perl script . Specifically , the average distance to each seta's four nearest neighbors ( nnd4i ) was calculated and then average area per seta across all i setae was estimated as the mean of pi * ( nnd4i /2 ) 2 . Pairwise comparisons of wing measurements between genotypes ( strains ) were conducted using Tukey's Honestly Significant Difference ( HSD test , [38] ) , based on ANOVAs using family as a nesting factor within genotype . Because several morphological variables were measured per genotype , we used the conservative Bonferroni correction for multiple tests . P-values shown were corrected by multiplying by the number of tests conducted in each analysis ( Table 1: 8 tests ( 4 variables and 2 sexes per genotype ) . Table 2: 3 tests ( 3 variables per genotype ) . Positional cloning efforts were begun by identifying visible mutants linked to ws1 . Using the original ws1 introgression from giraulti into vitripennis ( INTw1 . 1 , [13] ) , it was ascertained that the visible eye color mutant or123 and body color mutant bl13 map near to ws1 . A second introgression of ws1g into vitripennis containing a large giraulti flanking region was used for most of the fine-scale mapping and positional cloning work ( strain INT_bkbw , described in [14] ) . This introgression contains a naturally occurring giraulti black eye color allele , bkg , linked to ws1 . We found that bkg fails to complement the N . vitripennis mutant bk576 in heterozygous females . bkg produces oyster-gray eyes in the pe333 ( peach eye ) mutant background , which is easier to see than the black eyes of the mutant in wild-type background . A recombination map of these visible markers is shown in Figure 3A . To further assist in the positional cloning , recessive lethal mutations linked to ws1 were generated in the INT_bkbw strain by ethyl methanesulfonate ( EMS ) treatment of males carrying ws1g . Ten bkbw ( ws1g bkg; pe333 ) males were placed in 25mm Drosophila vials containing filter paper soaked 10% sucrose solution containing 0 . 25–0 . 5% EMS ( Sigma Chemical ) . After 7–10h , males were transferred to a vial containing clean filter paper overnight . Mutagenized males were then crossed to linked visible mutant strain bl13; pe333 . F1 virgin females were collected , transferred to individual cells of plastic 24-well culture plates ( various manufacturers ) and given a single fly host to lay eggs . Plates were sealed with a double layer of Micropore tape ( product number 1530–03 , 3M Corporation ) and incubated at 25C . After 48h , females were transferred to a new plate containing a pe333 male black-stage pupa and a spot of honey water . After mating , the wasps were anaesthetized at 4C and on ice , 2 fly hosts were added to each well , and then incubated at 21C . Newly-created linked recessive lethal markers were identified by distortions in F2 ratios of the visible markers ( ws1g , bkg and bl13 ) among the haploid F2 males of the virgin hosting ( as in Figure 2 ) and then linkage relationships were determined . Lethal lines were maintained using heterozygous female offspring . One lethal line , lethalD4 , was primarily used for mapping in this study , due to its close linkage to ws1 and its position on the side opposite the visible markers ( Figure 3A ) . To collect recombinant males for positional cloning , females heterozygous for ws1g and a flanking visible or lethal marker were set as virgins and resulting haploid male progeny were screened for recombination between the marker locus and ws1 by phenotype ( Figure 2 ) . Use of lethals in this approach was especially effective because non-recombinant haploid lethal ws1g males die; the only surviving males carrying the ws1g allele were recombinants between the lethal and wing size locus . Penetrance of the lethal genes used was found to be 100% . These tightly linked markers increased the “effective” discovery rate of recombinants within the region by 100–200 fold , greatly enhancing efficiency of the positional cloning efforts . A crucial step in the cloning effort was identification of molecular markers within the ws1 region to assist in fine-scale recombinant mapping . Initially this was accomplished by screening the original ws1gV introgression line and recombinants between it and flanking visible markers for linked Amplified Fragment Length Polymorphism ( AFLP ) markers . This was done using methods previously reported in [39] . A marker termed “AF1” was identified and found to be tightly linked to ws1 . The marker was cloned and sequenced . PCR products from primers designed to AF1 were used to screen a BAC library to N . vitripennis . Ends of a subset of BACs were sequenced , the library re-screened , and then RFLP typed to assemble a set of contig BACs [21] . BAC-end sequences were then used to generate a set of molecular markers distinguishing vitripennis and giraulti by PCR and RFLP ( Table S1; [21] ) . Subsequent sequencing of a giraulti BAC ( Genbank accession AC185330 ) which included the entire ws1g region and alignment of vitripennis and giraulti trace reads from the Nasonia genome project were used to identify additional PCR markers for ultra fine-scale mapping , and identification of recombination breakpoints by sequencing . Genotyping primers and PCR conditions are shown in Table S1 . Over 2000 recombinant haploid males were identified between ws1 and either the visible or lethal marker . These were screened with molecular markers to identify the location of recombination within the region around ws1 . The most informative recombinants are shown in Figure 3C . These include recombinants produced from the INT_w1 . 1 introgression ( Rec 5 ) and the INT_bkbw introgression ( Recs 1–4 , 6–7 ) . To define the ws1 region further , a strain was established from a recombinant between the flanking lethalD4 and ws1 that contained a relatively small region of giraulti introgression but retained the large wing phenotype ( Rec1 in Figure 3C ) . This strain was used for subsequent recombination to the other flanking region ( bl13 side ) . A recombinant from this second set containing only 40kb of giraulti sequence ( Rec4 , Figure 3C ) yet still showing the “ws1” large-wing phenotype ( Figure 1 ) was then backcrossed into the genome-sequenced AsymCX strain for >10 generations , and then purebred . This strain ( called ws1gV_40kb ) was used for wing measurements and quantitative PCR . Additional recombinants from these experiments further localized the ws1g effect to a 10 . 8kb region in giraulti , and a corresponding 13 . 5kb in vitripennis due to insertion/deletion differences . We examined this sequence and flanking regions for gene predictions [10] and also manually scanned the region for open reading frames and ESTs [18] . The region does not contain protein coding sequence for dsx , but only the dsx 5′ UTR , promoter and cis-regulatory region . We estimated the recombination rate between ws1 and lethalD4 by counting all ( living ) male offspring of a set of virgin females ( lethalD4 ws1g/+ + ) hosted for positional cloning . 89 males out of 15594 screened were recombinant ( + ws1g ) , a map distance of 0 . 57cM . Of a larger set of 683 + ws1g recombinant males screened with the ws1–8 marker ( Table S1 ) , 6 were recombinant between ws1–8 and ws1g , a distance of 36–50kb ( uncertainty is due to the uncertainty in the location of ws1g in the 13 . 5kb region ) . Local recombination rate was calculated as [ ( 0 . 57 cM between ws1g and lethalD4 ) / ( 683 recombinants between ws1g and lethalD4 ) ] *[ ( 6 recombinants between ws1g and ws1–8 ) / ( 36 or 50kb between ws1g and ws1–8 ) ] = 0 . 14 – 0 . 10 cM/Mb . RNA was isolated from wing discs , leg discs and whole individuals at the third instar larvae - prepupal transition . We found that this stage can be precisely identified to a few hours , between defecation of the larvae and ecdysis . Wing and leg discs were dissected from post-defecation prepupae under RNAse free conditions in 1X phosphate-buffered saline . After dissection , tissues were placed immediately on dry ice and if necessary stored at −80°C until RNA was isolated . Independent extractions of tissue were conducted to produce independent biological replicates . Each biological replicate consisted of 15–30 prepupal wings or legs or a single whole prepupa of each genotype ( ws1vV and ws1gV_40kb ) . Total RNA was isolated using Trizol ( Invitrogen ) . RNA was then quantified using a Qubit fluorometer ( Invitrogen ) and a Quant-iT RNA Assay Kit ( Invitrogen ) or a ND-1000 Spectrophotometer ( NanodropTechnologies , Oxfordshire , UK ) . Expression of EV431998 was tested in RT-PCR with primers TCGAGGCGGATAGTAAGGGC and AACTTTGTATTCCCTCAGCCAC . RT-PCR of other genes ( dsx and pros ) and reaction conditions are presented in [18] . For quantitative RT-PCR , first-strand cDNA Synthesis and qPCR were performed using SuperScript™ III Platinum SYBR Green Two-Step qRT-PCR Kit with ROX ( Invitrogen ) on an Applied Biosystems 7300 Real Time PCR System . RNA samples were split into reverse-transcribed and -RT controls . Male specific dsx ( dsxM ) was amplified using primers GCGGATGTGGAAGTAGCCAT and AATACTTGAACTTTTGACGATAAGCACT ( Figure S2 ) . In females , dsx was amplified using non-sex-specific primers CGAGCCACTGCCGAGTAT and TGGTAGCCAAACCGTTGTAAT . pros was amplified with GCTGATGTTCTTCTGGGTGAG and CCAGGAAGTTAGGACTCTTGAAG . The ribosomal protein rp49 was used as a control , with primers CTTCCGCAAAGTCCTTGTTC and AACTCCATGGGCAATTTCTG . All steps were performed according to the respective manufacturer's protocols . Each biological replicate was tested with two primer pairs ( e , g . , dsxM and rp49 ) . Each experiment was composed of one ws1gV_40kb and one ws1vV tissue sample ( 15–30 wings , 15–30 legs , or one whole prepupa ) , +RT and –RT , each run in triplicate . The median cycle threshold value of each triplicate was used for calculation . Expression ratios of dsx to rp49 were calculated using the 2−ΔΔCT method [40]: 2̂- ( ( ct[ws1vV dsxM] - ct[ws1vV rp49] ) - ( ct[ws1gV_40kb dsxM] - ct[ws1gV_40kb rp49] ) ) . Expression ratios were not corrected for differential amplification efficiency , and so the magnitude of expression ratios should be considered approximate . | The regulation of cell size and cell numbers is an important part of determining the size of organs in development , as well as of controlling cell over-proliferation in diseases such as cancer and diabetes . How the regulation of cell size and number can change to produce different organ sizes is not well understood . Here , we investigate the recent evolution of sex-specific wing size differences between two species that involve changes to cell size and number regulation . Males of the emerging genetic model wasp Nasonia vitripennis have small wings and do not fly , while males of the closely related species N . giraulti have large wings and do fly . We isolated a locus that contributes substantially to this wing size difference by increasing cell size and cell number . Surprisingly , we found that the determinant for this wing size difference is located in the non-coding region between two known transcription factors , the master sex determining gene doublesex and neurogenesis regulator prospero . The mechanism by which ws1 regulates sex specific wing growth has yet to be determined , although differences in dsx expression level in developing male wings may indicate a role for this sex determination locus . | [
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] | 2010 | Non-Coding Changes Cause Sex-Specific Wing Size Differences between Closely Related Species of Nasonia |
Determining how synaptic coupling within and between regions is modulated during sensory processing is an important topic in neuroscience . Electrophysiological recordings provide detailed information about neural spiking but have traditionally been confined to a particular region or layer of cortex . Here we develop new theoretical methods to study interactions between and within two brain regions , based on experimental measurements of spiking activity simultaneously recorded from the two regions . By systematically comparing experimentally-obtained spiking statistics to ( efficiently computed ) model spike rate statistics , we identify regions in model parameter space that are consistent with the experimental data . We apply our new technique to dual micro-electrode array in vivo recordings from two distinct regions: olfactory bulb ( OB ) and anterior piriform cortex ( PC ) . Our analysis predicts that: i ) inhibition within the afferent region ( OB ) has to be weaker than the inhibition within PC , ii ) excitation from PC to OB is generally stronger than excitation from OB to PC , iii ) excitation from PC to OB and inhibition within PC have to both be relatively strong compared to presynaptic inputs from OB . These predictions are validated in a spiking neural network model of the OB–PC pathway that satisfies the many constraints from our experimental data . We find when the derived relationships are violated , the spiking statistics no longer satisfy the constraints from the data . In principle this modeling framework can be adapted to other systems and be used to investigate relationships between other neural attributes besides network connection strengths . Thus , this work can serve as a guide to further investigations into the relationships of various neural attributes within and across different regions during sensory processing .
As experimental tools advance , measuring whole-brain dynamics with single-neuron resolution becomes closer to reality [1–4] . However , a task that remains technically elusive is to measure the interactions within and across brain regions that govern such system-wide dynamics . Here we develop a theoretical approach to elucidate such interactions based on easily-recorded properties such as mean and ( co- ) variance of firing rates , when they can be measured in multiple regions and in multiple activity states . Although previous theoretical studies have addressed how spiking statistics depend on various mechanisms [5–8] , these studies have typically been limited to a single region , leaving open the challenge of how inter-regional interactions impact the system dynamics , and ultimately the coding of sensory signals [9–13] . As a test case for our new theoretical tools , we studied interactions in the olfactory system . We used two micro-electrode arrays to simultaneously record from olfactory bulb ( OB ) and anterior piriform cortex ( PC ) . Constrained by these experimental data , we developed computational models and theory to investigate interactions within and between OB and PC . The modeling framework includes two distinct regions: a network that receives direct sensory stimuli ( here , the OB ) , and a second neural network ( PC ) that is reciprocally coupled to the afferent region . Each region contains multiple individual populations , each of which is modeled with a firing rate model [14]; thus even this minimal model involves several coupled stochastic differential equations ( here , six ) and has a large-dimensional parameter space . Analysis of this system would be unwieldy in general; we address this by developing a novel method to compute firing statistics that is computationally efficient , captures the results of Monte Carlo simulations , and can provide analytic insight . Thorough analysis of experimental data in both the spontaneous and stimulus-evoked states leads to a number of constraints on first- and second-order spiking statistics— many of which could not be observed using data from just one micro-electrode array . In particular , we find twelve ( 12 ) constraints that are consistent across different odorant stimuli . We use our theory and modeling to study an important subset of neural attributes ( synaptic strengths ) and investigate what relationships , if any , must be satisfied in order to robustly capture the many constraints observed in the data . We find that: i ) inhibition within OB has to be weaker than the inhibition in PC , ii ) excitation from PC to OB is generally stronger than excitation from OB to PC , iii ) excitation from PC to OB and inhibition within PC have to both be relatively strong compared to inputs originating in OB ( inhibition within OB and excitation from OB to PC ) . We validate these guiding principles in a large spiking neural network ( leaky integrate-and-fire , or LIF ) model , by showing that the many constraints from the experimental data are all satisfied . Finally , we demonstrate that violating these relationships in the LIF model results in spiking statistics that do not satisfy all of the data constraints . Our predictions provide insights into interactions in the olfactory system that are difficult to directly measure experimentally . Importantly , these predictions were inferred from spike rates and variability , which are relatively easy to measure . We believe that the general approach we have developed—using easy-to-measure quantities to predict hard-to-measure interactions—will be valuable in diverse future investigations of how whole-brain function emerges from interactions among its constituent components .
We first present our data from simultaneous dual micro-electrode array recordings in anesthetized rats . During each 30-second trial an odor was presented for roughly one second; recordings continued for a total of 30 seconds . This sequence was repeated for 10 trials with 2-3 minutes in between trials; the protocol was repeated for another odor . Recordings were processed to extract single-unit activity; the number of units identified was: 23 in OB and 38 in PC ( first recording , two odors ) , 18 in OB and 35 in PC ( second recording , another two odors ) . In total , there were four different odors presented . In this paper , we focus on the spike count statistics rather than the detailed temporal structure of the neural activity ( Fig 1A and 1B ) . We divided each 30 s trial into two segments , representing the odor-evoked state ( first 2 seconds ) and the spontaneous state ( remaining 28 seconds ) . We computed first- and second-order statistics for identified units; i . e . , firing rate νk , spike count variance , and spike count covariance ( we also computed two derived statistics , Fano Factor and Pearson’s correlation coefficient , for each cell or cell pair ) . Spike count variances , covariances and correlations were computed using time windows Twin ranging between 5 ms and 2 s . In computing population statistics we distinguished between different odors ( four total ) , different regions ( OB vs . PC ) , and different activity states ( spontaneous vs . evoked ) ; otherwise , we assumed statistics were stationary over time . We then sought to identify relationships among these standard measures of spiking activity . For example , we found that mean firing rate of OB cells in the evoked state was higher than the mean firing rate in the spontaneous state , or ν O B E v > ν O B S p ( although there is significant variability across the population , we focus on population-averaged statistics here ) . We found twelve ( 12 ) robust relationships that held across all odors . Table 1 summarizes the consistent relationships we found in our data , and Figs 1C and 1D , 2 and 3 show the data exhibiting these relationships when combining all odorant stimuli ( see S1 Text for statistics plotted by distinct odors ) . Throughout the paper , when comparing activity states the spontaneous state is in black and the evoked state in red; when comparing regions the OB cells are in blue and PC cells in green . A common observation across different animals and sensory systems , is that firing rates increase in the evoked state ( see , for example , Figure 3 in [15] ) . Indeed , we observed that average firing rates in both the OB and PC were higher in the evoked state than in the spontaneous state ( Fig 1C and 1D ) . Furthermore , the firing rate in the OB was larger than the firing rate in the PC , in both spontaneous and evoked states ( see mean values in Fig 1C and 1D ) . Stimulus-induced decorrelation appears to be a widespread phenomena in many sensory systems and in many animals [7]; stimulus-induced decorrelation was previously reported in PC cells under different experimental conditions [16] . Here , we found that in the PC , the average spike count correlation is lower in the evoked state ( red ) than in the spontaneous state ( black ) , at least for time windows of 0 . 5 s and above ( Fig 2A ) . Although we show a range of time windows for completeness , we focus on the larger time windows because in our experiments the odors are held for 1 s; furthermore , our theoretical methods only address long time-averaged spiking statistics . Note that stimulus-induced decorrelation in the OB cells was not consistently observed across odors . Another common observation in cortex , is for variability to decrease at the onset of stimulus [15]: in Fig 2B we see that the Fano Factor of spike counts in PC cells decreases in the evoked state ( red ) compared to the spontaneous state ( black ) ; note that other experimental labs have also observed this decrease in the Fano factor of PC cells ( see supplemental figure S6D in [16] ) . Fig 2C and 2D shows a comparison of PC and OB spike count correlation in the spontaneous state and evoked state , respectively . Spike count correlation in PC ( green ) is larger than correlation in OB ( blue ) in the spontaneous state , but in the evoked state the relationship switches , at least for time windows larger than 0 . 5 sec . Fig 3 shows the four remaining constraints that are consistent for all odors and for all time windows . The Fano Factor in PC ( green ) is larger than in OB ( blue ) , in the spontaneous state ( Fig 3A ) ; spike count variance in PC ( green ) is smaller than in OB ( blue ) in the evoked state ( Fig 3B ) ; spike count covariance in PC ( green ) is smaller than in OB ( blue ) in the evoked state ( Fig 3C ) ; and in OB the spike count variance in the evoked state ( red ) is larger than spontaneous ( black , Fig 3D ) . Throughout the paper , we scale the spike count variance and covariance by time window for aesthetic reasons; this does not affect the relative relationships . We model two distinct regions ( OB and PC ) with a system of six ( 6 ) stochastic differential equations , each representing the averaged activity of a neural population [14] or representative cell ( see Fig 4 for a schematic of the network ) . For simplicity , in this section we use the word “cell” to refer to one of these populations . Each region has two excitatory ( E ) and one inhibitory ( I ) cell to account for a variety of spiking correlations . We chose to include two E cells for two reasons: first , excitatory cells are the dominant source of projections between regions; we need at least two E cells to compute an E-to-E correlation . Moreover , in our experimental data , we are most likely recording from excitatory mitral and tufted cells ( we do not distinguish between mitral vs tufted here , and therefore refer to them as M/T cells ) ; therefore , the experimental measurements of correlations are likely to have many E-to-E correlations . The arrays likely record from I cell spiking activity as well , and the inclusion of the I cell is also important for capturing the stimulus-induced decreases in correlation and Fano factor [7 , 15] ( also see [17] who similarly used these same cell types to analyze spiking correlations in larger spiking network models ) . We use j ∈ {1 , 2 , 3} to denote three OB “cells” and j ∈ {4 , 5 , 6} for three PC cells , with j = 1 as the inhibitory OB granule cell and j = 4 as the inhibitory PC cell . The equations are: τ d x j d t = - x j + μ j + σ j η j + ∑ k g j k F ( x k ) ( 1 ) where F ( xk ) is a transfer function mapping activity to firing rate . Thus , the firing rate is: ν j = F ( x j ) . ( 2 ) We set the transfer function to F ( X ) = 1 2 ( 1 + tanh ( ( X - 0 . 5 ) / 0 . 1 ) ) , a commonly used sigmoidal function [14] for all cells; experimental recordings of this function demonstrate it can be sigmoidal [18–20] . All cells receive noise ηj , the increment of a Weiner process , uncorrelated in time but correlated within a region: i . e . 〈ηj ( t ) 〉 = 0 , 〈ηj ( t ) ηj ( t + s ) 〉 = δ ( s ) , and 〈ηj ( t ) ηk ( t + s ) 〉 = cjk δ ( s ) . We set cjk to: c j k = { 0 , if j ∈ { 1 , 2 , 3 } ; k ∈ { 4 , 5 , 6 } 1 , if j = k c O B if j ≠ k ; j , k ∈ { 1 , 2 , 3 } c P C if j ≠ k ; j , k ∈ { 4 , 5 , 6 } ( 3 ) The parameters μj and σj are constants that give the input mean and input standard deviation , respectively . Within a particular region ( OB or PC ) , all three cells receive correlated background noisy input , but there is no correlated background input provided to both PC and OB cells . This is justified by the experimental data ( see Fig S9 in S2 Text ) ; average pairwise OB-to-PC correlations are all relatively small , and in particular , less than pairwise correlations within the OB and PC . Furthermore , anatomically there are no known common inputs to both regions that are active at the same time . We also set the background correlations to be higher in PC than in OB: i . e . , c P C > c O B . This is justified in part by our array recordings , where correlated local field potential fluctuations are larger in PC than in OB . Furthermore , one source of background correlation is global synchronous activity; Murakami et al . [21] has demonstrated that state changes ( i . e . , slow or fast waves as measured by EEG ) strongly affect odorant responses in piriform cortex but only minimally effect olfactory bulb cells . Finally , PC has more recurrent activity than the olfactory bulb; this could lead to more recurrent common input , if not cancelled by inhibition [22] . We constructed our model to have two distinct activity states , spontaneous and evoked . We modeled the evoked state by increasing the three parameters μ1 , μ2 , μ3 , representing mean input to the olfactory bulb ( values given in Table 2 ) . All other parameters were the same for both states . While increasing the input to the I cells in OB in the evoked state ( μ1 ) is not anatomically accurate because granule cells do not receive direct sensory input [23] , overall this captures the net effect of stimulus input to granule cells ( see section Generality of Firing Rate Model Predictions for how we apply this method to a specific olfactory system ) . The model we have described is less realistic than a large network of spiking models ( such as Hodgkin-Huxley or leaky integrate-and-fire neurons ) . However , its simplicity permits fast and efficient evaluation of firing rate statistics , a necessity in exploring a large parameter space . Specifically , we calculate the statistics of the coupled network by solving a system of transcendental equations Eqs 28–45 , rather than using Monte Carlo simulations . These equations were derived using an approximation based on asymptotic expansions ( see Materials and methods: Approximation of Firing Statistics in the Firing Rate Model for details ) . This fast method allowed us to evaluate many parameter combinations , and therefore constrain the unknown coupling parameters , gjk , which would otherwise be an intractable problem . Comparisons of the firing statistics computed from our method and Monte Carlo simulations show that the mean activity and firing rates are very accurate; variance and covariance ( and thus correlation ) are not as accurate , for larger coupling strengths ( see Fig S10 in S2 Text comparing 100 random parameter sets ) . Nonetheless , we will find that these reduced model results are replicated by more realistic and larger spiking network models . In principle , there can be up to 36 coupling strengths , which is intractable to explore in detail . We make the following assumptions: Within OB , there is also excitatory ( M/T ) input to the inhibitory ( granule ) cells: g12 = g13 = 0 . 1—these values are small because feedforward inhibition is known to be a significant component in this circuit [28] . Within PC , we also include similar connections from E to I cells: g45 = g46 = 0 . 1 . Recurrent E to E connections in PC are omitted; such connections can cause problems for our reduction method , resulting in oscillatory firing rates that cannot be efficiently captured . We also make the following simplifying assumptions to limit the dimension of the parameter space of interest: The resulting network model is illustrated in Fig 4 . Here we use non-standard notation for the 4 main connections of interest; instead of subscripts , we use two indicative capital letters ( e . g . , gIP ) so that readers can easily distinguish the connections we explore , vs . unexplored connections . Thus , we were left with four undetermined coupling strengths: gIO , gIP , gEO and gEP . We comprehensively surveyed a four-dimensional parameter space in which each coupling strength |gIO| , |gIP| , gEO , gEP was chosen between 0 . 1 and 2 , with a interval of 0 . 1 , giving us 204 = 1 . 6 × 105 total models . Given each choice of 4-tuple {gIO , gIP , gEO , gEP} , we computed first- and second-order statistics of both activity xk and firing rates F ( xk ) using the formulas given in Eqs 28–45 , and checked whether the results satisfied the constraints listed in Table 1—comparing the mean statistic across all 3 cells or all 3 possible pairs in various states and regions . We found that approximately 1 . 1% of all 4-tuples satisfied the constraints; we display them in Fig 4 , by projecting all constraint-satisfying 4-tuples onto a two-dimensional plane where the axes are two of the four coupling parameters . We show four out of six possible pairs ( the other two show qualitatively similar patterns , see Fig S11 in S2 Text ) : |gIO| vs . |gIP| ( Fig 4A ) , gEO vs . gEP ( Fig 4B ) , |gIO| vs . gEP ( Fig 4C ) , and |gIP| vs . gEO ( Fig 4D ) . The results from the minimal firing rate model are: The statistics computed in Eqs 28–45 rely on the assumption that the activity distributions xk are only weakly perturbed from a normal distribution; this may be violated for larger coupling strengths . Thus , we used Monte Carlo simulations of Eq 1 to check the accuracy of this approximation; specifically we performed Monte Carlo simulations only on each 4-tuple of parameters for which the analytic approximation met our constraints . The resulting parameter sets that satisfied all 12 constraints are included as red dots in Fig 4A–4D ( therefore a red dot indicates that all 12 constraints were satisfied both for the analytic approximation and for the Monte Carlo simulations ) . The result was a smaller set of parameters , but it is evident that the qualitative results derived from the fast analytic solver hold for the Monte Carlo simulations . Moreover , these results were robust to the choice of transfer function: in Fig . S12 of S2 Text , we show that the same constraints are obtained when using a “square root” transfer function , rather than a sigmoid . How do each of the 12 data constraints ( Table 1 ) restrict the set of possible model parameters ? Fig 5 addresses this question in two ways . In Fig 5A , we show , for each constraint , the fraction ( as a percent ) of all 204 parameter sets for which that constraint is satisfied , when statistics are computed via the reduction method ( see Materials and methods , Approximation of Firing Statistics in the Firing Rate Model ) . Constraints have varying levels of restrictions , but the second order firing statistics in the evoked state appear more restrictive than the others . Together , only 1 . 1% of the values in parameter space satisfy all 12 constraints . In Fig 5B , we show , for each constraint , the fraction of all 204 parameter sets for which that constraint is satisfied , in both the reduction method and in Monte Carlo simulations ( recall that we took the relatively conservative approach of only testing the Monte Carlo simulations on the admissible set from the reduction method ( 1 . 1% ) ; this yielded only 0 . 13% of parameter space . The constraint that ρ O B S p < ρ P C S p has the smallest percent by far in Fig 5B . We attribute this “mismatch” to inaccuracies in our method with stronger coupling ( note that gIP and gEP are both relatively strong in the admissible set ) ; the smaller percentages in Fig 5B compared to Fig 5A are likely due to errors in the Cov and Var calculations ( see Fig S10 in S2 Text ) , as well as possible amplification of these errors when dividing by Var in the ρ calculation . Another way to succinctly examine the structure of the four neural attributes: gIO , gEO , gIP , gEP is to consider a matrix: A ( j , : ) = [ g I O ( j ) , g E O ( j ) , g I P ( j ) , g E P ( j ) ] ( 4 ) where the jth row of A corresponds to a parameter set where all 12 constraints are satisfied . We first subtract the mean , finding that [gIO , gEO , gIP , gEP]T = [−0 . 62 , 1 . 11 , −1 . 38 , 1 . 29]T , which is consistent with the results described in Fig 4 . A standard singular value composition ( SVD ) of the mean-corrected matrix , A = U Σ V T , shows that two dimensions in the parameter space accounts for 82% of the remaining variance ( as quantified by the singular values ) and thus provide an approximation to the structure of the valid gIO , gEO , gIP , gEP values . The eigenvectors corresponding to the largest singular values are: [gIO , gEO , gIP , gEP]T = [−0 . 05 , 0 . 60 , −0 . 07 , 0 . 79]T and [gIO , gEO , gIP , gEP]T = [0 . 56 , 0 . 05 , 0 . 82 , 0 . 08]T; that is , they reflect high positive correlations between the two inhibitory strengths gIP and gIO , and between the two excitatory strengths gEP and gEO . Therefore , with the minimal firing rate model we predict the connectivity strengths generally satisfy: | g I O | < g E O < g E P < | g I P | . We next asked whether the full set of data constraints were necessary; would we have seen a similar relationship between connectivity strengths , while using only a subset of the constraints outlined in Table 1 ? Because the admissible set is defined as the intersection over all constraints , removing any constraint would likely result in a different and ( if different ) larger parameter space . We considered i ) keeping 8 of the 12 constraints in Table 1 , neglecting the constraints on the Co-variability row , and ii ) keeping only 4 of the 12 constraints in Table 1 , neglecting both the Variability and Co-variability rows ( i . e . , only with the firing rate ) . Briefly , the result is that i ) 21 . 5% of parameters in the analytic method satisfy the constraints; ii ) 33 . 4% of parameters in the analytic method satisfy the constraints; compare this to 1 . 1% ( and 0 . 13% Monte Carlo ) with all 12 constraints . The relationships of the connection strengths are different than when all 12 constraints are included: for example , it is no longer true that gEP > gEO , once the covariance constraints are omitted . In general , we should expect that if we change the wiring diagram of our simple firing rate model ( Fig 4 ) , then the same experimental constraints might result in different predictions . This could be a concern since our simple firing rate model is lacking many connections and cell types that exist in the real olfactory system [23] . However , we tested one alternative wiring diagram with different neurons receiving stimulus input , no E-to-I connections within OB , and no E-to-I connections within PC . Our predictions were robust to these changes . Second and most importantly , we tested whether our predictions held in a larger network of leaky integrate-and-fire neurons . This spiking network model also had more realistic network connectivity , more closely mimicking known anatomy of real olfactory systems . The following highlight the differences between the spiking model and the firing rate model: The parameter gEO will now refer to the strength of E-to-E connections , rather than E-to-I connections , from OB to PC . The next two sections demonstrate that our predictions hold for this LIF network model ( also see S3 Text ) . Here we show that a general leaky integrate-and-fire ( LIF ) spiking neuron model of the coupled OB-PC system can satisfy all 12 data constraints . Rather than try to model the exact underlying physiological details of the olfactory bulb or anterior piriform cortex , our goal is to demonstrate that the results from the minimal firing rate model can be used as a guiding principle in a more realistic coupled spiking model with conductance-based synaptic input . The LIF model does not contain all of the attributes and cell types of the olfactory system , but is a plausible model that contains: i ) more granule than M/T cells in OB ( a 4-to-1 ratio , comparable to the 3-to-1 ratio used in [29] ) ; ii ) E-to-E connections from OB to PC that drive the entire network within PC; iii ) E-to-I ( granule cell ) feedback from PC to OB; iv ) lack of sensory input to granule I cells in OB . We also show that the minimal firing rate model results can be applied to a generic cortical-cortical coupled population ( see S3 Text ) . We set the four conductance strength values to: g I O = 7 g E O = 10 g I P = 20 g E P = 15 ; ( 5 ) See Fig 6 or Eqs 65–67 for exact definitions of gXY; these conductance strength values are dimensionless scale factors . These values were selected to satisfy the relationships derived from the analysis of the rate model ( see Fig 4 ) . In contrast to the minimal firing rate model , here the conductance values are all necessarily positive; an inhibitory reversal potential is used to capture the hyperpolarization that occurs upon receiving synaptic input . With the conductance strengths in Eq 5 , and other standard parameter values in a typical LIF model , we were able to easily satisfy all 12 constraints: see Table 3 and Fig 6 ( Table 4 for LIF parameter values ) . While the firing rates in the LIF network ( Table 3 ) do not quantitatively match with the firing rates from the experimental data , a few qualitative trends are apparent: ( i ) the ratio of mean spontaneous to evoked firing rates are similar to that observed in experimental data , for both OB and PC , ( ii ) the same is true of the standard deviation , ( iii ) the ratio of the mean OB firing rate to PC firing rate is similar to what is observed in the experimental data , in both spontaneous and evoked states . Therefore , the LIF network captures the mean firing rates reasonably well . One difference between the LIF spiking network and the minimal firing rate model is that in the evoked state , mean background input to both the OB and PC cells is increased , compared to the spontaneous state ( recall that in the minimal firing rate model , only the mean input to the OB cells increased in the evoked state; this ensured that stimulus-induced changes in PC were due to network activity ) . When the mean input to the PC cells is the same in the spontaneous and evoked states , 10 of the 12 constraints were satisfied—the exception was the correlation of PC in the evoked state , which decreased but is still larger than the spontaneous correlation ( see Fig . S13 in S2 Text ) . The reason is that as firing rates increase , the OB spiking is more variable and the synaptic input from OB to PC is noisier , so the input to PC activity is diffused . To capture the final two constraints , we allowed mean input drive to PC to increase in the evoked state . This has also been used in previous theoretical studies to achieve stimulus-induced decreases in spiking variability and co-variability [30] . Churchland et al . [15] used an extra source of variability in the spike generating mechanism , a doubly stochastic model , which was simply removed with stimulus onset . Thus , the mechanism we employ ( increased mean input with lower input variability ) is consistent with other studies that analyzed stimulus-induced changes in variability [15 , 30] .
In computing statistics for the minimal firing rate model , we only considered equilibrium firing statistics , in which a set of stationary statistics can be solved self-consistently . More sophisticated methods might be used to address oscillatory firing statistics ( see [60] where the adaptive quadratic integrate-and-fire model was successfully analyzed with a reduced method ) ; capturing the firing statistics in these other regimes is a potentially interesting direction of research . The limitation to steady-state statistics is not unique , but is shared by other approximation methods . Some methods are known to have issues when the system bifurcates [61 , 62] because truncation methods can fail [63] . Several authors have proposed procedures to derive population-averaged first- and second-order spiking statistics from the dynamics of single neurons . The microscopic dynamics in question may be given by a master equation [61 , 62 , 64–66] , a generalized linear model [67 , 68] , or the theta model [69 , 70] . ( Other authors have derived rate equations at the single-neuron level , by starting with a spike response model [74] or by taking the limit of slow synapses [73] . ) While we would ideally use a similar procedure to derive our rate equations , none of the approaches we note here is yet adapted to deal with our setting , a heterogeneous network of leaky integrate-and-fire neurons . Instead , we focused here on perturbing from a background state in which several populations ( each population modeled by a single equation ) receive correlated background input but are otherwise uncoupled . This allows us to narrow our focus to how spike count co-variability from common input is modulated by recurrent connections . We also note that other recent works have used firing rate models to explain observed patterns of correlated spiking activity in response to stimuli . Rosenbaum et al . [43] have studied the spatial structure of correlation in primate visual cortex with balanced networks [71]; Keane & Gong [72] studied wave propagation in balanced network models . Designing a spiking neural network model of two different regions that satisfies the many experimental data constraints we have outlined is a difficult problem that would often be addressed via undirected simulations . We have shown that systematic analysis of a minimal firing rate model can yield valuable insights into the relative strength of unmeasured network connections . Furthermore , these insights are transferable to a more complex , physiologically realistic spiking model of the OB–PC pathway . Indeed , incorporating the relative relationships of the four conductance strengths resulted in spiking network models that satisfied the constraints . Strongly violating the relative relationships of these conductance strengths led to multiple violations of the data constraints . Because our approach can be extended to other network features , we are hopeful that the general approach we have developed—using easy-to-measure quantities to predict hard-to-measure interactions—will be valuable in future investigations into how whole-brain function emerges from interactions among its constituent components .
All procedures were carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by University of Arkansas Institutional Animal Care and Use Committee ( protocol #14049 ) . Isoflurane and urethane anesthesia were used and urethane overdose was used for euthanasia . After the array recordings were spike sorted to identify activity from distinct cells , we further processed the data as follows: We divided each 30 s trial into two segments , representing the odor-evoked state ( first 2 seconds ) and the spontaneous state ( remaining 28 seconds ) . In each state , we are interested in the random spike counts of the population in a particular window of size Twin . For a particular time window , the jth neuron has a spike count instance Nj in the time interval [t , t + Twin ) : N j = ∑ k ∫ t t + T w i n δ ( t - t k ) d t ( 6 ) The spike count correlation between cells j and k is given by: ρ T = Cov ( N j , N k ) Var ( N j ) Var ( N k ) , ( 7 ) where the covariance of spike counts is: Cov ( N j , N k ) = 1 n - 1 ∑ ( N j - μ ( N j ) ) ( N k - μ ( N k ) ) . ( 8 ) Here n is the total number of observations of Nj/k , and μ ( N j ) : = 1 n ∑ N j is the mean spike count across Twin-windows and trials . The correlation ρT is a normalized measure of the the trial-to-trial variability ( i . e . , noise correlation ) , satisfying ρT ∈ [−1 , 1]; it is also referred to as the Pearson’s correlation coefficient . For each cell pair , the covariance Cov ( Nj , Nk ) and variance Var ( Nj ) are empirically calculated by averaging across different time windows within a trial and different trials . A standard measure of variability is the Fano Factor of spike counts , which is the variance scaled by the mean: F F k = Var ( N k ) μ ( N k ) . ( 9 ) In principle , any of the statistics defined here might depend on the time t as well as time window size Twin; here , we assume that Var , Cov , FF , and ρT are stationary in time , and thus separate time windows based only on whether they occur in the evoked ( first 2 seconds ) or spontaneous ( last 28 seconds ) state . Each trial of experimental data has many time windows ( an exception is when Twin = 2s; in the evoked state , there is only 1 window per trial ) ; the exact number depends on the state , the value of Twin , and whether disjoint or overlapping windows are used . In this paper we use overlapping windows by half the length of Twin to calculate the spiking statistics ( e . g . , if the trial length is 2 s and Twin = 1s , then there are 3 total windows per trial: [0s , 1s] , [0 . 5s , 1 . 5s] , and [1s , 2s] ) . The results are qualitatively similar for disjoint windows and importantly , the relationships/constraints are the same with disjoint windows . We limit the size of Twin ≤ 2s because this is the maximum duration of the evoked state , within each trial . The average spike count μ ( Nj ) of the jth neuron with a particular time window Twin is related to the average firing rate νj of that neuron: ν j : = μ ( N j ) T w i n ( 10 ) Recall that the activity in each representative cell is modeled by: τ d x j d t = - x j + μ j + σ j η j + ∑ k g j k F ( x k ) ( 11 ) where F ( xk ) is a transfer function mapping activity to firing rate . Thus , the firing rate is: ν j = F ( x j ) . ( 12 ) The index of each region is denoted as follows: j ∈ {1 , 2 , 3} for the 3 OB cells , and j ∈ {4 , 5 , 6} for the 3 PC cells , with j = 1 as the inhibitory granule OB cell and j = 4 as the inhibitory PC cell ( see Fig 4 ) . In this paper , we set σ1 = σ2 = σ3 = σOB and σ4 = σ5 = σ6 = σPC ( see Table 2 ) . In the absence of coupling ( i . e . gjk = 0 ) , any pair of activity variables , ( xj , xk ) , are bivariate normally distributed because the equations: τ d x j d t = - x j + μ j + σ j ( 1 - c j k ξ j ( t ) + c j k ξ c ( t ) ) ( 13 ) τ d x k d t = - x k + μ k + σ k ( 1 - c j k ξ k ( t ) + c j k ξ c ( t ) ) ( 14 ) describe a multi-dimensional Ornstein-Uhlenbeck process [78] . Note that we have re-written ηj/k ( t ) as sums of independent white noise processes ξ ( t ) , which is always possible for Gaussian white noise . Since x j ( t ) = 1 τ ∫ 0 t e - ( t - u ) / τ [ μ j + σ j η j ( u ) ] d u , we calculate marginal statistics as follows: μ ( j ) ≡ ⟨ x j ⟩ = μ j + 0 ( 15 ) σ 2 ( j ) ≡ ⟨ ( x j - μ ( j ) ) 2 ⟩ = ⟨ σ j 2 τ 2 ∫ 0 t ∫ 0 t e - ( t - u ) / τ η j ( u ) e - ( t - v ) / τ η j ( v ) d u d v ⟩ = σ j 2 τ 2 lim t → ∞ ∫ 0 t e - 2 ( t - u ) / τ d u = σ j 2 2 τ A similar calculation shows that in general we have: Cov ( j , k ) = c j k 2 τ σ j σ k ( 16 ) Thus , ( xj , xk ) ~N ( ( μjμk ) , 12τ ( σj2σjσkcjkσjσkcjkσk2 ) ) . To simplify notation , we define: ρ S N ( y ) : = 1 2 π e - y 2 / 2 , the standard normal PDF ( 17 ) ρ2D ( y1 , y2 ) :=12π1−cjk2exp ( −12y→T ( 1cjkcjk1 ) −1y→ ) , bivariatestandardnormal ( 18 ) With coupling , an exact expression for a joint distribution for ( x1 , x2 , x3 , x4 , x5 , x6 ) is not explicitly known . However , we can estimate this distribution ( and any derived statistics , such as means and variances ) using Monte Carlo simulations . All Monte Carlo simulations of the six ( 6 ) coupled SDEs were performed using a time step of 0 . 01 with a standard Euler-Maruyama method , for a time of 500 units ( arbitrary , but relative to the characteristic time scale τ = 1 ) for each of the 3000 realizations . The activity xj was sampled at each time step after an equilibration period . Furthermore , we can approximate moments of the joint distribution under the assumption of weak coupling , as described in the next section . We will now show how to compute approximate first and second order statistics for the firing rate model with coupling; i . e . , we aim to compute the mean activity 〈xj〉 , mean firing rate 〈F ( xj ) 〉 , variance and covariances of both: 〈xj xk〉 and 〈F ( xj ) F ( xk ) 〉 . For a simpler exposition , we have only included twelve synaptic connections; we have excluded self ( autaptic ) connections and E→E connections . An equation for each statistic can be derived by first writing Eq 11 as a low-pass filter of the right-hand-side: x j ( t ) = 1 τ ∫ 0 t e - ( t - u ) / τ [ μ j + σ j η j ( u ) + ∑ k g j k F ( x k ) ] d u ( 19 ) We then take expectations , letting t → ∞ , we have: μ ( j ) : = ⟨ x j ⟩ = μ j + ⟨ ∑ k g j k F ( x k ) ⟩ = μ j + ∑ k g j k ⟨ F ( x k ) ⟩ ( 20 ) We assume the stochastic processes are ergodic , which is generally true for these types of stochastic differential equations , so that averaging over time is equivalent to averaging over the invariant measure . We will make several assumptions for computational efficiency . First , we only account for direct connections in the formulas for the first and second order statistics , assuming the terms from the indirect connections are either small or already accounted for in the direct connections . We further make the following assumptions to simplify the calculations: ⟨ ∫ 0 t F ( x k ( u ) ) e - ( t - u ) / τ d u ∫ 0 t F ( x k ( v ) ) e - ( t - v ) / τ d v ⟩ ≈ τ 2 E [ F 2 ( x k ) ] + ( τ E [ F ( x k ) ] ) 2 ( 21 ) where E [ F n ( x k ) ] : = ∫ F n ( σ ( k ) y + μ ( k ) ) ρ S N ( y ) d y ( 22 ) ⟨ ∫ 0 t σ j η j ( u ) e - ( t - u ) / τ d u ∫ 0 t F ( x k ( v ) ) e - ( t - v ) / τ d v ⟩ ≈ τ 2 E [ N j F ( x k ) ] , if j ≠ k ( 23 ) where E [ N j F ( x k ) ] : = σ j 2 ∫ ∫ y 1 F ( σ ( k ) y 2 + μ ( k ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 24 ) 〈∫0tσjηj ( u ) e− ( t−u ) /τdu∫0tF ( xk ( v ) ) e− ( t−v ) /τdv〉≈τ2σk2∫yF ( σ ( k ) y+μ ( k ) ) ρSN ( y ) dy , ifj=k ( 25 ) 〈∫0tF ( xj ( u ) ) e− ( t−u ) /τdu∫0tF ( xk ( v ) ) e− ( t−v ) /τdv〉≈τ2E[ F ( xj ) F ( xk ) ]+τ2E[ F ( xj ) ] E [F ( xk ) ] ( 26 ) whereE[ F ( xj ) F ( xk ) ]:=∬F ( σ ( j ) y1+μ ( j ) ) F ( σ ( k ) y2+μ ( k ) ) ρ2D ( y1 , y2 ) dy1dy2 ( 27 ) and Nj denotes the random variable ∫ 0 t σ j η j ( u ) e - ( t - u ) / τ d u , which is by itself normally distributed with mean 0 and variance σ j 2 τ / 2 . The first assumption , Eq 21 , states that time-average of F ( xj ( t ) ) multiplied by an exponential function ( low-pass filter ) is equal to the expected value scaled by τ/2; the second and third , Eqs 23 and 25 , address Nj and F ( xk ( t ) ) , for j ≠ k and j = k respectively ( similarly for Eq 26 ) . In all of the definitions for the expected values with ρ2D , note that the underlying correlation cjk depend on the pair of interest ( j , k ) . Finally , we assume that the activity variables ( xj , xk ) are pairwise normally distributed with the subsequent statistics; this is sufficient to “close” our model and solve for the statistical quantities self-consistently . This is implicitly a weak coupling assumption because with no coupling , ( xj , xk ) are bivariate normal random variables . The resulting approximations for the mean activity are: μ ( 1 ) = μ 1 + ∑ k = 2 , 3 , 5 , 6 g 1 k ∫ F ( σ ( k ) y + μ ( k ) ) ρ S N ( y ) d y ( 28 ) μ ( 2 ) = μ 2 + g 21 ∫ F ( σ ( 1 ) y + μ ( 1 ) ) ρ S N ( y ) d y ( 29 ) μ ( 3 ) = μ 3 + g 31 ∫ F ( σ ( 1 ) y + μ ( 1 ) ) ρ S N ( y ) d y ( 30 ) μ ( 4 ) = μ 4 + ∑ k = 2 , 3 , 5 , 6 g 4 k ∫ F ( σ ( k ) y + μ ( k ) ) ρ S N ( y ) d y ( 31 ) μ ( 5 ) = μ 5 + g 54 ∫ F ( σ ( 4 ) y + μ ( 4 ) ) ρ S N ( y ) d y ( 32 ) μ ( 6 ) = μ 6 + g 64 ∫ F ( σ ( 4 ) y + μ ( 4 ) ) ρ S N ( y ) d y . ( 33 ) The resulting approximation to the variances of the mean activity are: τ σ 2 ( 1 ) = σ 1 2 2 + ∑ k = 2 , 3 , 5 , 6 g 1 k 2 2 Var ( F ( σ ( k ) Y + μ ( k ) ) ) + ∑ ( j , k ) ∈ { ( 2 , 3 ) ; ( 5 , 6 ) } g 1 j g 1 k Cov ( F ( σ ( j ) Y 1 + μ ( j ) ) , F ( σ ( k ) Y 2 + μ ( k ) ) ) ( 34 ) τ σ 2 ( 2 ) = σ 2 2 2 + g 21 2 2 Var ( F ( σ ( 1 ) Y + μ ( 1 ) ) ) + σ 2 g 21 ∫ ∫ y 1 2 F ( σ ( 1 ) y 2 + μ ( 1 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 35 ) τ σ 2 ( 3 ) = σ 3 2 2 + g 31 2 2 Var ( F ( σ ( 1 ) Y + μ ( 1 ) ) ) + σ 3 g 31 ∫ ∫ y 1 2 F ( σ ( 1 ) y 2 + μ ( 1 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 36 ) τ σ 2 ( 4 ) = σ 4 2 2 + ∑ k = 2 , 3 , 5 , 6 g 4 k 2 2 Var ( F ( σ ( k ) Y + μ ( k ) ) ) + ∑ ( j , k ) ∈ { ( 2 , 3 ) ; ( 5 , 6 ) } g 4 j g 4 k Cov ( F ( σ ( j ) Y 1 + μ ( j ) ) , F ( σ ( k ) Y 2 + μ ( k ) ) ) ( 37 ) τ σ 2 ( 5 ) = σ 5 2 2 + g 54 2 2 Var ( F ( σ ( 4 ) Y + μ ( 4 ) ) ) + σ 5 g 54 ∫ ∫ y 1 2 F ( σ ( 4 ) y 2 + μ ( 4 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 38 ) τ σ 2 ( 6 ) = σ 6 2 2 + g 64 2 2 Var ( F ( σ ( 4 ) Y + μ ( 4 ) ) ) + σ 6 g 64 ∫ ∫ y 1 2 F ( σ ( 4 ) y 2 + μ ( 4 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 39 ) In Eqs 28–39 , all of the variances and covariances are computed with respect to Y ∼ N ( 0 , 1 ) ( for Var ) and ( Y1 , Y2 ) ~N ( ( 00 ) , 12 ( 1cjkcjk1 ) ) ( for Cov ) ; both are easy to calculate . The value cjk depends on the pairs; for example in Eq 35 , the ρ2D has cjk = cOB , the background correlation value in the olfactory bulb but in Eq 34 , the Cov term is with respect to ρ2D with cjk = cPC , the background correlation value in the piriform cortex . Lastly , we state the formulas for the approximations to the covariances . Although there are 15 total covariance values , we are only concerned with 6 covariance values ( 3 within OB and 3 within PC ) ; we neglect all covariances between regions . First , our experimental data set shows that these covariance ( and correlation ) values are small ( see Fig S9 in S2 Text ) . Second , because there is no background correlation ( i . e . , common input ) between PC and OB in our model , any nonzero covariance/correlation arises strictly via direct coupling . Thus , we cannot view OB-PC covariance from coupling as a small perturbation of the background ( uncoupled ) state; we do not expect our model to yield qualitatively accurate predictions for these statistics . The formulas for the covariances of interest are: τ Cov ( 1 , 2 ) = 1 2 c O B σ 1 σ 2 + σ 1 g 21 2 ∫ y 2 F ( σ ( 1 ) y + μ ( 1 ) ) ρ S N ( y ) d y + σ 2 g 12 2 ∫ y 2 F ( σ ( 2 ) y + μ ( 2 ) ) ρ S N ( y ) d y + σ 2 g 13 2 ∫ y 2 F ( σ ( 3 ) y + μ ( 3 ) ) ρ S N ( y ) d y + 1 2 ∑ ( j , k ) g 1 j g 2 k C ( j , k ) ( 40 ) τ Cov ( 1 , 3 ) = 1 2 c O B σ 1 σ 3 + σ 1 g 31 2 ∫ y 2 F ( σ ( 1 ) y + μ ( 1 ) ) ρ S N ( y ) d y + σ 3 g 12 2 ∫ y 2 F ( σ ( 2 ) y + μ ( 2 ) ) ρ S N ( y ) d y + σ 3 g 13 2 ∫ y 2 F ( σ ( 3 ) y + μ ( 3 ) ) ρ S N ( y ) d y + 1 2 ∑ ( j , k ) g 1 j g 3 k C ( j , k ) ( 41 ) τ Cov ( 2 , 3 ) = 1 2 c O B σ 2 σ 3 + g 21 g 31 2 Var ( F ( σ ( 1 ) Y + μ ( 1 ) ) ) + σ 3 g 21 + σ 2 g 31 2 ∫ ∫ y 1 2 F ( σ ( 1 ) y 2 + μ ( 1 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 42 ) τ Cov ( 4 , 5 ) = 1 2 c P C σ 4 σ 5 + σ 4 g 54 2 ∫ y 2 F ( σ ( 4 ) y + μ ( 4 ) ) ρ S N ( y ) d y + σ 5 g 45 2 ∫ y 2 F ( σ ( 5 ) y + μ ( 5 ) ) ρ S N ( y ) d y + σ 5 g 46 2 ∫ y 2 F ( σ ( 6 ) y + μ ( 6 ) ) ρ S N ( y ) d y + 1 2 ∑ ( j , k ) g 4 j g 5 k C ( j , k ) ( 43 ) τ Cov ( 4 , 6 ) = 1 2 c P C σ 4 σ 6 + σ 4 g 64 2 ∫ y 2 F ( σ ( 4 ) y + μ ( 4 ) ) ρ S N ( y ) d y + σ 6 g 45 2 ∫ y 2 F ( σ ( 5 ) y + μ ( 5 ) ) ρ S N ( y ) d y + σ 6 g 46 2 ∫ y 2 F ( σ ( 6 ) y + μ ( 6 ) ) ρ S N ( y ) d y + 1 2 ∑ ( j , k ) g 4 j g 6 k C ( j , k ) ( 44 ) τ Cov ( 5 , 6 ) = 1 2 c P C σ 5 σ 6 + g 54 g 64 2 Var ( F ( σ ( 4 ) Y + μ ( 4 ) ) ) + σ 6 g 54 + σ 5 g 64 2 ∫ ∫ y 1 2 F ( σ ( 4 ) y 2 + μ ( 4 ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 ( 45 ) where C ( j , k ) = ∫ ∫ F ( σ ( j ) y 1 + μ ( j ) ) F ( σ ( k ) y 2 + μ ( k ) ) ρ 2 D ( y 1 , y 2 ) d y 1 d y 2 - ( ∫ F ( σ ( j ) y + μ ( j ) ) ρ S N ( y ) d y ) ( ∫ F ( σ ( k ) y + μ ( k ) ) ρ S N ( y ) d y ) ( 46 ) We use a generic spiking neural network model of leaky integrate-and-fire neurons to test the results of the theory . There were NOB = 100 total OB cells , of which we set 80% ( 80 ) to be granule ( I- ) cells and 20% ( 20 ) to be mitral/tufted ( M/T ) E-cells . There are known to be many more granule cells than M/T cells in the OB; this ratio of 4-to-1 is similar to other models of OB ( see [29] who used 3-to-1 ) . The equations for the OB cells are , indexed by k ∈ {1 , 2 , … , NOB}: τ m d v k d t = μ O B - v k - g k , X I ( t ) ( v k - E I ) - g k , X E ( t ) ( v k - E E ) - g k , X P C ( t - τ Δ , P C ) ( v k - E E ) + σ O B ( 1 - c ˜ O B η k ( t ) + c ˜ O B ξ o ( t ) ) v k ( t * ) ≥ θ k ⇒ v k ( t * + τ r e f ) = 0 g k , X E ( t ) = γ X E p X E ( 0 . 2 N O B ) ∑ k ′ ∈ { presyn OB E-cells } G k ′ ( t ) g k , X I ( t ) = γ X I p X I ( 0 . 8 N O B ) ∑ k ′ ∈ { presyn OB I-cells } G k ′ ( t ) g k , X P C ( t ) = γ X , P C p X , P C ( 0 . 8 N P C ) ∑ j ′ ∈ { presyn PC E-cells } G j ′ ( t ) τ d , X d G k d t = - G k + A k τ r , X d A k d t = - A k + τ r , X α X ∑ l δ ( t - t k , l ) . ( 65 ) The conductance values in the first equation gk , XI , gk , XE , and gk , XPC depend on the type of neuron vk ( X ∈ {E , I} ) . The last conductance , g X , P C ( t - τ Δ , P C ) ( v k - E E ) , models the excitatory presynaptic input ( feedback ) from the PC cells with a time delay of τΔ , PC . The conductance variables gk , XY ( t ) are dimensionless because this model was derived from scaling the original ( raw ) conductance variables by the leak conductance with the same dimension . The leak , inhibitory and excitatory reversal potentials are 0 , E I , and E E , respectively with E I < 0 < E E ( the voltage is scaled to be dimensionless , see Table 4 ) . ξk ( t ) are uncorrelated white noise processes and ξo ( t ) is the common noise term to all NOB cells . The second equation describes the refractory period at spike time t*: when the neuron’s voltage crosses threshold θj ( see below for distribution of thresholds ) , the neuron goes into a refractory period for τref , after which we set the neuron’s voltage to 0 . The parameter γXY gives the relative weight of a connection from neuron type Y to neuron type X; the parameter pXY is the probability that any such connection exists ( X , Y ∈ {E , I} ) . Gk is the synaptic variable associated with each cell , and dependent only on that cell’s spike times; its dynamics are given by the final two equations in Eq 65 and depend on whether k ∈ {E , I} . Finally , two of the parameters above can be equated with coupling parameters in the reduced model: g E P = γ E , P C ; g I O = γ E I ( 66 ) which are dimensionless scale factors for the synaptic conductances . The PC cells had similar functional form but with different parameters ( see Table 4 for parameter values ) . We modeled NPC = 100 total PC cells , of which 80% were excitatory and 20% inhibitory . The equations , indexed by j ∈ {1 , 2 , … , NPC} are: τ m d v j d t = μ P C - v j - g j , X I ( t ) ( v j - E I ) - g j , X E ( t ) ( v j - E E ) - g j , X O B ( t - τ Δ , O B ) ( v j - E E ) + σ P C ( 1 - c ˜ P C η j ( t ) + c ˜ P C ξ p ( t ) ) v j ( t * ) ≥ θ j ⇒ v j ( t * + τ r e f ) = 0 g j , X E ( t ) = γ X E p X E ( 0 . 8 N P C ) ∑ j ′ ∈ { presyn PC E-cells } G j ′ ( t ) g j , X I ( t ) = γ X I p X I ( 0 . 2 N P C ) ∑ j ′ ∈ { presyn PC I-cells } G j ′ ( t ) g j , X O B ( t ) = γ X , O B p X , O B ( 0 . 2 N O B ) ∑ k ′ ∈ { presyn OB E-cells } G k ′ ( t ) τ d , X d G j d t = - G j + A j τ r , X d A j d t = - A j + τ r , X α X ∑ l δ ( t - t j , l ) . ( 67 ) Excitatory synaptic input from the OB cells along the lateral olfactory tract is modeled by: g X , O B ( t - τ Δ , O B ) ( v j - E E ) . The common noise term for the PC cells ξp ( t ) is independent of the common noise term for the OB cells ξo ( t ) . Two of the parameters above can be equated with coupling parameters in the reduced model: g E O = γ E , O B ; g I P = γ E I ( 68 ) The values of the parameters that were not stated in Table 4 were varied in the paper: g I O , g E O , g I P , g E P . To model two activity states , we allowed mean inputs to vary ( see Table 4 ) . In contrast to the reduced model , we increased both inputs to PC cells ( from μPC = 0 in the spontaneous state to μPC = 0 . 4 in the evoked state ) as well as to OB cells; μOB = 0 . 6 in the spontaneous state to μOB = 0 . 9 in the evoked state only for M/T cells ( OB granule cell input is the same for spontaneous and evoked ) . Finally , we model heterogeneity by setting the threshold values θj in the following way . Both OB and PC cells had the following distributions for θj: θ j ∼ e N ( 69 ) where N is normal distribution with mean - σ θ 2 / 2 and standard deviation σθ , so that {θj} has a log-normal distribution with mean 1 and variance: e σ θ 2 - 1 . We set σθ = 0 . 1 , which results in firing rates ranges seen in the experimental data . Since the number of cells are modest with regards to sampling ( NOB = 100 , NPC = 100 ) , we evenly sampled the log-normal distribution from the 5th to 95th percentiles ( inclusive ) . We remark that the synaptic delays of τΔ , PC and τΔ , OB were set to modest values to capture the appreciable distances between OB and PC . This is a reasonable choice based on evidence that stimulation in PC elicit a response in OB 5-10 ms later [79] . In all Monte Carlo simulations of the coupled LIF network , we used a time step of 0 . 1 ms , with 2 s of biology time for each of the 50 , 000 realizations ( i . e . , over 27 . 7 hours of biology time ) , enough simulated statistics to effectively have convergence . | Sensory processing is known to span multiple regions of the nervous system . However , electrophysiological recordings during sensory processing have traditionally been limited to a single region or brain layer . With recent advances in experimental techniques , recorded spiking activity from multiple regions simultaneously is feasible . However , other important quantities— such as inter-region connection strengths—cannot yet be measured . Here , we develop new theoretical tools to leverage data obtained by recording from two different brain regions simultaneously . We address the following questions: what are the crucial neural network attributes that enable sensory processing across different regions , and how are these attributes related to one another ? With a novel theoretical framework to efficiently calculate spiking statistics , we can characterize a high dimensional parameter space that satisfies data constraints . We apply our results to the olfactory system to make specific predictions about effective network connectivity . Our framework relies on incorporating relatively easy-to-measure quantities to predict hard-to-measure interactions across multiple brain regions . Because this work is adaptable to other systems , we anticipate it will be a valuable tool for analysis of other larger scale brain recordings . | [
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] | 2017 | A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system |
The conservation of the intrinsic dynamics of proteins emerges as we attempt to understand the relationship between sequence , structure and functional conservation . We characterise the conservation of such dynamics in a case where the structure is conserved but function differs greatly . The triosephosphate isomerase barrel fold ( TBF ) , renowned for its 8 β-strand-α-helix repeats that close to form a barrel , is one of the most diverse and abundant folds found in known protein structures . Proteins with this fold have diverse enzymatic functions spanning five of six Enzyme Commission classes , and we have picked five different superfamily candidates for our analysis using elastic network models . We find that the overall shape is a large determinant in the similarity of the intrinsic dynamics , regardless of function . In particular , the β-barrel core is highly rigid , while the α-helices that flank the β-strands have greater relative mobility , allowing for the many possibilities for placement of catalytic residues . We find that these elements correlate with each other via the loops that link them , as opposed to being directly correlated . We are also able to analyse the types of motions encoded by the normal mode vectors of the α-helices . We suggest that the global conservation of the intrinsic dynamics in the TBF contributes greatly to its success as an enzymatic scaffold both through evolution and enzyme design .
Understanding a proteins’ inherent flexibility , or intrinsic dynamics , is fundamental to understanding the mechanism with which they are able to perform their function . Yet we know little about the conservation of dynamic properties in a structural fold , whether the similarity is due to or regardless of evolutionary conservation . In many cases , protein families can be distinguished by their similarity in dynamics [1 , 2] , however there is also growing evidence that this may be influenced by the level of similarity in their overall structural topology , which can be robust to mutations [3 , 4] . General properties have been ascribed to elements of protein structures , such as the correlated motions of β-sheets [5] , while a clear similarity between the flexibilities of non-homologous enzymes catalysing the same reaction has also been demonstrated [6] . The role of dynamics is not limited to the most flexible regions of the protein; the most rigid regions of proteins have been suggested to act as energy sinks as part of their functional role [7] . Studies by Micheletti and colleagues have also suggested that dynamics is conserved for function , regardless of the structural conservation [8 , 9] . Enzymes that possess the TIM Barrel Fold ( TBF ) , named after the enzyme triosephosphate isomerase ( TIM ) , provide a good case for exploring the intrinsic dynamics of structures that are similar in shape yet completely different in function . In a comprehensive review by Nagano et al . in 2002 , TBF proteins were described with regards to structural similarity as domains and functional abilities [10] . They found that only four out of the 21 protein families analysed were found to have direct sequence-based evolutionary relationships . Due to its property of being very evolvable [11] , TBF is a popular fold in the field of protein design [12–14] . The functions of these proteins span five out of six of the Enzyme Commission classes , another reason it earns the distinction of being a “super-fold”[15] . The TBF consists of eight β-strands and α-helices that alternate in sequence and fold into a barrel-like shape ( Fig 1 ) . The eight β-strands close to form a parallel β-barrel core , while the eight α-helices surround this core , each flanking its corresponding β-strand . The TBF is the only reported case of the parallel β-barrel within its structural topology , as most β-barrels are anti-parallel and possess a variety of shear numbers ( a descriptor of the inter-strand twisting ) [16–18] . The diversity of the TBF is accommodated by the loops between the eight β-α pairs that are able to accommodate additional secondary structure elements ( SSEs ) or domains in some proteins [10 , 14 , 19] . TBF proteins can exist as part of multi-domain enzymes , displaying additional versatility as a scaffold for many reaction types . Aside from the similarity in shape , enzymes with this fold have other common traits . For instance , most of their active sites occur at the C-terminal end of the barrel and can be placed on different secondary structures and most commonly on the loops between them , providing a greater number of possibilities for their positioning in sequence . For example , triosephosphate isomerase has catalytic residues on β-strand 1 and loops 1 , 4 and 6 , while chitinase B has them on the β-loop-α unit 4 and loop 6 ( S1 Fig , panels 1N55 and 1E15 ) . In general , all of the catalytic sites are found on the C-terminal end of the barrel-like structure , which is referred to as the catalytic end , while the N-terminal end is referred to as the stability end ( Fig 1 ) [19] . The β-barrel core of the TBF proteins is mostly hydrophobic [18] , where the amino acids are found to be rather packed , preventing access to solvent . A mathematical study on the structural features of parallel β-barrels by Lasters et al . [20] showed that eight β-strands form the optimal configuration for the packing of amino acid side chains inside the β-barrel core . They found that the parallel barrels that make up the protein core appear to tolerate little variability on the right-handedness of its shear and its stability through inter-strand hydrogen-bonds , resulting in the conservation of key geometric parameters such as inter-strand twist and strand number . Despite all the characterisation of the fold via experiments and structural bioinformatics methods , the understanding of the dynamics has been restricted to the flexibility of the substrate binding site loops of the TIM enzyme , e . g . via solid-state nuclear magnetic resonance [21] , molecular dynamics simulations [22] and normal mode analysis ( NMA ) using elastic network models ( ENM ) [23] . This fold has also been used as a test case for protein engineering and co-evolution studies [11 , 12 , 24] . Intrinsic dynamics as described by ENM-based NMA calculations is a quick and reliable measure of protein dynamics and flexibility , especially at the secondary structure and domain levels . ENM-based NMA has been particularly amenable to the comparison of intrinsic dynamics of multiple structures [4 , 25] . Despite evidence that there is conservation of dynamics that follows the conservation of sequence and structure , it has been a challenge to separate this from the influence of structural topology independent of evolutionary relationships . Others have also studied families and superfamilies of proteins with low sequence conservation using ENM-based NMA , showing its relevance and usefulness when exploring their similarities [1 , 2 , 8 , 26–34] . Knowing detailed attributes of the TBF only drives our curiosity as to its success as a common structural framework further , regardless of the mode of its structural and sequence conservation . We seek to explore the similarities in intrinsic dynamics using ENM-based NMA between five structures from five different superfamilies with varying levels of structural similarity and evolutionary relationship , as revealed by Nagano et al . [10] . Focussing on the role of the secondary structures and their flexibility within the context of the fold , we show that the intrinsic dynamics can be better compared when including homologous proteins from within the five superfamilies . We relate the catalytic and ligand sites of these proteins to their rigidity , as defined by the fluctuation profiles . To characterise the differences in rigidity , we examine the significant correlated movements of the secondary structure elements from five representative TBF structures from each superfamily . Finally , we also characterise the types of displacements of the β-barrel core and the outer α-helical bundle undergo . Upon finding that the α-helices are more mobile , collectively and independently , than the β-barrel core , we characterised the displacements of the individual α-helices as well .
To assess the overall similarity in intrinsic dynamics between structures , we perform analysis that is reliant on a multiple structure alignment . The flexibility is compared at the amino acid positions that are comparable within the set of structures i . e . the ones that can be structurally aligned through the whole dataset . The actual calculation of the dynamical similarity score is influenced by the alignment of the structures considered due to the alignment’s role in defining the comparable positions within a set of proteins [25] . When comparing structures that have poor sequence identities ranging from 18% to 45% ( S3 Fig , left panel ) , such as these five TBFs ( with PDB ID . s 1N55 , 1E15 , 1KKO , 3CH0 and 3CWN , Table 1 ) , constructing a reliable multiple structure alignment can be challenging . This challenge is compounded in structures with a TBF due to the symmetry of their fold , and difference in sizes , especially with regards to volume [35] . Here , we used MUSTANG[36] to obtain multiple structural alignments of the proteins in the dataset and we use a global similarity measure , the Bhattacharyya coefficient ( BC ) score ( See Methods section ) , not just as a measure of similarity between the intrinsic dynamics of the structures but also as a means to validate the quality of each alignment . We recently demonstrated that this strategy was reliable for a dataset of 53 structures with the TIM-barrel fold [25] . When aligning the five main structures , we find that the first α-helix of 1N55 shifts by one α-helical unit to correspond to the second α-helical units of the other four structures , causing a mismatch . The mismatch results in the loss of conserved points between the first and eighth strands of the structural alignment , which are not considered in the calculation of the BC score ( S2 Fig ) . The BC analysis between the five structures has scores that range from 0 . 75 to 0 . 83 , with the least similar pair of structures being 1N55 and 1E15 ( 0 . 75 ) and the most similar pairs being 1KKO & 1N55 and , 1KKO & 3CH0 ( 0 . 83 for both pairs ) ( Fig 2A ) . In this alignment , 106 residues are conserved in the alignment ( Fig 2A ) . Incorporating evolutionary information by using multiple sequences is a usual practice that adds robustness to sequence alignments . Following this principle , we performed a multiple structural alignment with 23 structures ( Fig 2 and S2 Table ) , consisting of five homologues per superfamily , except for 1KKO where only three were found based on our criteria ( cf . Methods , S3 Fig—right panel ) . In this alignment , 135 evenly distributed positions are conserved ( Fig 2D ) . The clustering of the BC scores showed that the structures group according to their superfamilies ( Fig 2C ) . Moreover , we see that the two former separate superfamilies belonging to the structures 1N55 and 3CWN have clusters that are next to each other . 1N55 and 3CWN currently belong to the same Aldolase Class I in CATH ( cf . Methods ) . The scores between the five main structures range from 0 . 78 to 0 . 82 , whereas the scores within the homologous sets tend to be between 0 . 84 to 0 . 95 for 1E15 , 0 . 90 to 0 . 95 for 1N55 , 0 . 83 to 0 . 90 for 3CH0 and 0 . 89 to 0 . 96 for 3CWN . The scores show a separation between the structures within and between the superfamilies and correspond to the separation seen in Fuglebakk et al . [37] . The clustering of the 23 structures according to their BC scores reproduces the classification by Nagano et al . [10] . Next , we extracted the alignment of the five main structures from the alignment of the 23 structures , which naturally increased the number of conserved points by ten . Thus , 145 residue positions were considered in the BC score calculation here ( Fig 2F ) . Despite using the exact same structural alignment as the previous one , a mere increase in ten conserved positions in the alignment reorders them in the clustering ( Fig 2E ) . This results in an incorrect grouping of the two enzymes from the Aldolase Class I superfamily ( 1N55 and 3CWN ) . Thus , we find that the structural alignment and BC calculations with 23 structures are more reliable than the ones obtained from just the five main structures . Incidentally , even if we artificially increase the number of corresponding points on the structures , we are unable to obtain the correct ordering of the structures . This could be due to the loss of evolutionary information when we allow parts of the structures that do not naturally align to be considered in the comparison . The normalised fluctuations show the magnitude of mobility of each Cα atom , and are often referred to as theoretical B-factors of the structures . When examining the normalised fluctuations profiles for the five representative proteins ( Fig 3 and S4 Fig ) , we find that the β-strands and the α-helices of the TBF are immobile compared to the regions in-between them . Between the β-strands and the α-helices of the TBF , we find that the β-strands fluctuate less , whereas the α-helices show greater tendency for fluctuation . Yet , as we expected , both of these secondary structure elements fluctuate much less than the loop regions between them . The only exception is the second helix in 1E15 , where two of the homologues ( 1D2K , 3G6L ) possess large fluctuations within the secondary structure region . Upon closer scrutiny , we find that the α-helices in these two homologues are shorter , and that segments of the secondary structure with large fluctuations are disordered in those structures while they are α-helical in others . We compared the normalised fluctuations profiles of the TBF domains in Fig 3 to the profiles calculated from ENMs with the accompanying subunits or domains according to their biological assemblies ( Fig 4 ) . We observe that the β-strands remain the most rigid parts of the TBFs , while the α-helices are slightly more flexible . The main difference between the profiles lies in the loop regions and we find that oligomerisation and the presence of other domains mainly act to rigidify loop regions , or modulate the rigidity of these loops , without changing the rigidity profiles of the secondary structures significantly . The trend of rigidity is further illustrated by the normalised deformation energies , as displayed on the structures themselves in Fig 5 and S5 Fig . The normalised deformation energies calculated here inform about the local flexibility of each Cα atom relative to its neighbouring atoms [38] . We find that the β-barrel core has high deformation energies , while the loop regions and surface residues have lower energies . These differences indicate that the β-barrel core acts as a rigid anchor of the structure , in contrast to the slightly more mobile α-helices . This also corresponds to the fluctuation analysis ( Fig 3 and S4 Fig ) , which shows that the generally hydrophobic cores of these structures are robust to fluctuations . The correlations between pairs of residues in the protein inform us about the coupling of the motions across regions , SSEs or domains of proteins . To capture significant correlations between distant amino acids , we considered pairwise correlation scores above the 95th percentile rank , for Cα atoms that are at least 8 Å apart ( cf . Methods , as implemented in [39] ) . We found that the bulk of these strong correlations lie within the loop regions , connecting to and spanning the helices ( Fig 6 ) . When examining 1KKO , the structure with the least number of accessories on the periphery of the TIM barrel , we see that these strong and distant correlations tend to be uniformly distributed around the barrel , connecting to the strands via the loops above and below them . The density of these correlations at the loop regions also changes with the presence of accessory secondary structure elements . The same analysis was performed on the biological assemblies to explore the impact of the oligomerisation on the distribution of the significant , distant correlations . We found that most of the correlations lie in the loop regions and within the α-helices , while the β-strands are correlated to the α-helices via the loop regions ( e . g . 1N55 , S9 Fig ) . This pattern is highly similar to the one we observe in the monomeric forms in Fig 6 . Moreover , there is a densification of significant distant correlations in regions that are away from the oligomeric interface , reflecting the rigidification of the loops upon oligomerisation . In three structures , 1E15 ( Fig 6 ) , 3CWN and 3CH0 ( S6 Fig ) , regions with accessory elements also possess strong , distant anti-correlations with other such highly correlated regions , none of which involve the secondary structures of the main fold . Moreover , the β-strands and their flanking α-helices are not correlated together directly , but associate with each other via the loops connecting them . This creates a “gap” in the network of correlations between the β-barrel core and outer flanking helical bundle ( Fig 6 and S6 Fig ) . The exception is in the case of 3CH0 , where the β-strand that carries the catalytic residue is well correlated to the connecting α-helix . Like the secondary structure elements , the catalytic residues also correspond to more rigid parts of the structure , as shown in Fig 7 . Most substrate-binding , phosphate-binding and metal ion-binding sites also follow this trend , with some exceptions such as the substrate-binding residues W220 and E221 in 1E15 ( index 290 and 291 in Fig 7 ) and T360 and C361 in 1KKO ( index 196 and 197 in Fig 7 ) and the phosphate-binding residue G173 in 1N55 ( index 174 in Fig 7 ) . It should be noted that even the exceptions do not lie in the regions with the highest peaks . Despite the extremely low flexibility , the catalytic residues lie in the interface between the least and the most deformable subdomains of the structure ( Fig 5 ) . The observation in Fig 5 can be explained by the need for these residues to be in close proximity to the more deformable substrate-binding sites of the enzymes . As the catalytic residues are located at the C-terminal end of the β-barrel , we isolated the distant , significant correlations shown in Fig 6 that involve the β-strands of the five main TBF enzymes . In doing so , we observed that there were not many of such correlations between the β-strands and within the β-barrel core in comparison with the rest of the structures . Strong correlations in the β-strands are mainly in close range and typically do not span beyond their neighbouring strand . Thus , they do not appear in the distant correlation analysis but only if we decrease the distance threshold to 4 Å that corresponds to the approximate distance between the Cα atoms of two adjacent β-strands ( S8 Fig ) . The significant , distant correlations connect to the larger hubs that run along the flanking α-helices via the loops . As a result , we find that the catalytic residues are situated close to these hubs of distantly correlated residues between the strands and following helices , as illustrated in Fig 8A with 1N55 . This is generally the case with all the structures to varying degrees . This is seen in a more extended manner in 1KKO ( Fig 8B ) , where we can see that the significant , distant correlations connect the two loops above the C-terminal ends of the β-strands via the secondary structure elements . In the examples of the five main TBFs we analysed , we observe the connection between significant , distant correlations and residues implicated with substrate- , phosphate- and metal-ion binding is less clear than with the catalytic residues ( Fig 8C and 8D ) . A useful way to generalise the contributions of low energy normal modes to relevant domain motions is by finding the overlap between the normal modes and predefined idealised displacement vectors [40] . To determine the propensity for collective displacements of the α-helical bundle and the β-barrel core , we calculated the overlap between the ENM modes and a predefined set of normalised idealised displacements ( cf . Methods ) . The idealised displacement vectors describe translation along and rotation around the axis of inertia of the α-helical bundle or the β-barrel . The overlap is expressed as a score , Ωw , which is the sum of the overlap over all the non-trivial modes , weighted by their eigenvalues . This means that Ωw values that are low have a greater contribution from lower energy modes ( thus more favourable ) , while Ωw values that are high have greater contribution from higher energy modes . As a result , we find that the β-barrels are significantly less mobile than the outer helical bundles , as shown by their much higher Ωw scores , confirming the analysis of the normalised fluctuations ( Fig 9 ) . Both the rotation and translation displacements of the outer α-helical bundle separate well from the displacements of the β-barrel core for all five structures ( Fig 9 ) . Moreover , the translation of the α-helical bundle is more favourable than rotation in four out of the five structures , with varying levels of differences from the rotation displacements . We attempted to characterise the types of displacements preferred by the α-helices ( Fig 10 ) . We performed overlaps with several normalised ideal vector displacements: vertical , horizontal , tilting , N- and C-terminal bending , and quantified them using the overlap score , Ωw ( cf . Methods section ) . We find that the helices of the five main TBF structures are more prone to vertical and horizontal displacements than tilting and bending ( Fig 10 ) . In the vertical and horizontal displacements , we find that helix 7 possesses the greatest range of scores . Of the tilting and bending displacements , the N-terminal bending is least favoured . There also seems to be a preference for the bending of the C-terminal ends of the α-helices , as compared to the tilting and N-terminal bending displacements . As the tilting displacement is a combination of the bending displacement in opposite directions , it is possible that this displacement is less favourable due to the relative immobility of the N-terminal end . Comparing the mobility of the individual SSEs in each structure , we observed that there were no trends that could be related to functional sites ( i . e . catalytic residues or ligand binding sites ) , or the presence of accessory structures . The mobility of the 7th strand of 1E15 can be treated as an artefact from the removal of the domain at the connecting loop . 1KKO is observed to have the most mobile α-helices of all the structures , while in 3CWN the first four α-helices display greater mobility than the last four in sequence when we consider all the displacement types together .
In this study , we have shown that the alignment of the structures improved greatly from the introduction of homologues from each of the five superfamilies . In fact , when performing their study , Nagano et al . [10] found that many structures served as “stepping-stone” sequences , which bridged the gaps between distantly homologous sequences . This strategy also had a dramatic impact on the BC scores , which showed a clear separation between the superfamilies , including the clustering of the formerly separated TIM and ALD1 families . As a result , the BC clustering achieved a similar result to Nagano and colleagues [10] , possibly with greater ease than their structure and sequence-alignment intensive protocol . The removal of these homologues while preserving the same alignment showed the problem of over-fitting , as we lost the meaningful clustering of the BC scores when ten additional amino acid positions were considered between the five structures . In addition to quantifying the similarity between the structures’ intrinsic dynamics , the BC scores incidentally served as a check for alignment quality , revealing their relevance as a scoring function in multiple structural or structure-based sequence alignments . Large multiple structural alignments like the ones we have used are not common , beyond examples that demonstrate the efficacy of an algorithm . Efforts made to include dynamical information in alignments are currently only available for pairwise alignments [29] . We emphasise that the comparative analysis of intrinsic dynamics is extremely sensitive to alignments and should be carefully considered when designing comparative flexibility studies and interpreting the results . The parallel β-barrel core of the TBF is shown to be extremely rigid , relative to the rest of the structure . The flanking α-helices are mobile , but only just , in contrast to the loop regions . These are demonstrated in this study by the combination of the normalised fluctuations profiles ( Figs 3 and 4 ) , the deformation energies ( Fig 5 ) , and the pairwise correlations ( Figs 6 and 7 and 8 ) . The rigidity of the β-barrel is not surprising , considering that the strands are stabilised by an extensive network of hydrogen bonds between them . The influences of the strand-strand interactions are recapitulated by the network of short-range ( over 4Å ) correlations that originate from the β-barrel core , as shown in S4 Fig . This is unlike anti-parallel β-barrels , which despite being rigid cylinder-like structures , undergo motions such as breathing ( that involve the change in cylinder volume ) , bending and twisting to be able to perform their function [41] . In contrast , the α-helices display collective movements that are absent in the core of the TBF , as demonstrated by their relative immobility as a collective unit ( Fig 9 ) . Our results show that the α-helical bundles prefer to rotate and translate vertically , compared to the β-barrel core . Moreover , the individual α-helices are more likely to translate vertically along their own principal axis of inertia and horizontally away from the centre of mass of the structure by a large extent over tilting and bending ( Figs 10 and 11 ) . We also saw that the C-terminal ends of the α-helices seem to displace more than the N-terminal ends . This is consistent with the idea that the generally shorter loops of the N-terminal end serve to provide stability ( as seen in the TIM enzyme ) and possibly influence mobility of the α-helix [14] . The lack of trend between the mobility of individual helices and the position of the catalytic residues suggests that the flexibility of the helices is dependent on structure . We further suggest that the mobility could have a different role in the functionality of the enzyme; the α-helices could act as sensors that modulate the flexibility of the functional loops via oligomerisation or protein-protein interactions , as the loops’ motions correlate well with the α-helices . The difference in the size of the β-barrel core and the α-helical bundle could be a factor that influences the separation of their mobility , and requires further investigation . The rigidity of the β-barrel core is consistent with findings by others [42 , 43] that it acts as a site of stabilising residues important for the integrity of the fold . For example , using a combination of computational approaches to define residue-residue contacts , hydrophobicity and amino acid conservation in 71 TBF structures , Gromiha et al . [43] found that the majority of the stabilising residues found in the β-barrel core have very low normalised B-factors , indicating a preference for immobility . The immobility of the β-barrel core that they describe mirrors our finding that the β-barrel core is very rigid ( Figs 3 and 4 ) . We would also like to highlight the finding that oligomerisation acts to modulate the flexibility of the loop without changing the rigidity of the β-barrel core and the slightly more mobile α-helices ( Fig 4 ) . The observation from the fluctuations profile of the biological assemblies is consistent with the general flexibility patterns we have found in the analysis of the SSE flexibility of single TBF domains from each enzyme . The loop regions ( including accessory secondary structures ) are strongly correlated parts of TBF , with strong correlations running along the helices . The significant correlations between the β-strands in the core are fewer and within a shorter distance range , in contrast . Correlations that are implicated with the strands connect further to hubs of correlations associated with the loops and helices . The strands and helices are mostly correlated in motion via the loops at the N-terminal and C-terminal ends of the SSEs , and stronger correlations tend to occur within the accessory structures rather than the TIM barrel domain structure of the enzymes . This still holds for TBFs in their biological assembly form , yet the correlations in the regions away from the oligomeric interface are generally intensified ( Cf . S9 Fig ) . Thus , we conclude that variation within the loop regions based on functionality could impact the mobility of the α-helices as well , and not that of the β-strands; a property that needs to be considered when designing TBF-based enzymes . When looking at the correlation objects that span from the β-strands specifically , we see that the significant , distant correlations we defined span up to more than one β-α unit . However the numbers differ from structure to structure . This could be due to the fact that the number of correlations sampled using the percentiles changes with the size of the structure and is not a feature of the βαβ module of the structures , which has been suggested as the elementary module of the TBF [13 , 19 , 44 , 45] . The slow motions of loops have been shown to be functionally important in some enzymes [46–48] . Katebi et al . showed , via ENM analysis that loops 6 and 7 of the TIM enzyme move in a concerted fashion [23] . We believe that the rigidity of the β-strands allows for distant correlations that are functionally relevant ( cf . Fig 8 and S6 Fig ) , as demonstrated in other studies [5 , 49] . In addition , Yang et al . [45] reported the presence of critical inter-SSE hydrogen-bonds between the main chain amino groups of odd-numbered β-strands and the side chain acceptors of the loops leading to their following α-helices , situated at the N-terminal end of the TIM enzyme structure . They believed that this was key to the stability of the fold and evidence for the modular evolution of the fold . Our ENM modelling does not take such interactions into account , as we only consider the Cα atoms of the proteins , but they are consistent with the correlations we observe between the β-strands and the α-helices via the loop regions that would reinforce this pattern . The catalytic residues of all the structures analysed were found to be in the more rigid regions of the structure , whether they were found on the secondary structures or the loops , in terms of the normalised fluctuations . The catalytic residue positions were also found to have intermediate levels of deformation energy , consistent with studies which show active sites to be at the boundary of more deformable regions that allow appropriate conformational changes for substrate-binding [50 , 51] . This is consistent with the idea that the positioning of catalytic residues in enzymes are conserved throughout evolution , as they are selected for optimal access to the substrate they act upon [52] . Katebi et al . also observed this trend in the TIM enzyme , where they concluded that the stability of the loops were important for catalysis [23] . In a large-scale computational analysis of 760 structures of enzymes belong to different folds , Chien & Huang defined the rigidity of the proteins using the weighted-contact network model and found that a significant proportion of catalytic residues lie in rigid environments [53] . We found that the phosphate and metal-binding sites also follow this trend , with the substrate binding sites to a lesser extent , depending on the superfamily . The binding of the substrate is the biggest distinguishing feature between these structures , as in the case of the chitinase , where this is aided by the addition of a domain at the C-terminal end of the TBF . The TBF provides a number of rigid positions for the placement of catalytic residues , in particular at the C-terminal end of each β-strand that are geometrically close by the virtue of being symmetrical . Thus , nature can exploit a myriad of positions when placing an active site on the TBF . In fact , TBF has been referred to as a fold that is easily parameterisable for de novo protein design [54] . As a principle , it has been demonstrated that the rigidity of the peptide backbone of certain folds , such as the β-propeller , confers it a greater advantage for the grafting of new active sites than others that are deemed more flexible , due to the need for binding pocket complementarity for catalysis [55] . Thus , the success of the TBF as a scaffold for both natural and novel artificial enzymes cannot be decoupled from its intrinsic dynamic properties . We were able to find signature properties of the TIM barrel fold that are consistent regardless of sequence or functional conservation; i ) the relative immobility of the inner β-barrel core between all the structures ii ) the relative flexibility of the flanking α-helices ii ) the strong , long-distance correlations of the strands in the immobile β-barrel core to the α-helices via the loops linking them . We determined that the preferred displacement types of the flanking α-helices consist of vertical and horizontal motions . We saw that this fold is successful in providing rigid positions for all catalytic residues , and that most other residue positions involved in function also share this property . We believe that the number of rigid positions offered by the TBF combined to the possibility of adding neighbouring loops or other accessory elements is key to explaining its versatility . Our results also show that comparative flexibility studies are highly sensitive to the alignments used . It is really striking that the main patterns of the intrinsic dynamics in the TBF are as conservative as the fold , and not dependent on function . This result is supported by the findings of Zen et al . [9] who reported that flexibility measures could be used to satisfactorily align TIM barrels with different functions as captured by their EC classification . It is also remarkable that signature flexibility pattern is present independent of the oligomerisation state , which otherwise tends to affect the loop and α-helical regions . We believe that this conservative intrinsic dynamics of the TIM barrel scaffold further characterises its success as a versatile platform for many types of enzymatic reactions . We find that despite the varying levels of structural and sequence homology , the overall shape is able to determine global similarities in intrinsic dynamics . We further suggest that sequence or functional similarity may not be the main driving force in the conservation of intrinsic dynamics . The characterisation of the flexibility of the TBF also has implications in protein and drug design , in that by exploiting the intrinsic dynamic signatures , one could provide solutions that were previously limited to specific target areas .
We used protein structures from five different superfamilies , referred to as Triose phosphate isomerase ( TIM ) , Aldolase class I ( ALD1 ) , Enolase ( ENOL ) , Chitinase ( CHTN ) subfamily of the Glycosidases ( GLYC ) and Phosphatidylinositol ( PI ) phospholipase C ( PIPLC ) in Nagano et al . [10] , summarised in S1 Table . According to the phylogenetic analysis in this review , the protein families ( nomenclature used is in parentheses ) cluster according to the following groupings where TIM and ALD1 are closely linked , followed by ENOL , with CHTN and PIPLC being distant outliers . CHTN and PIPLC are also considered to be two of the four superfamilies to have little evolutionary link to the rest of the superfamilies . These superfamilies relate to each other at the Topology level in the CATH database [56] as of January 2011 . Since then , two of the superfamilies , TIM and ALD1 , have been reclassified to be part of the same Homology level [57] . For the purposes of comparison , we picked five representative structures from each of these superfamilies , which are further annotated in ( S2 Table ) . These structures all exist as part of dimers , and have varying lengths that include additional secondary structures , further illustrated in S1 Fig . The structures are also treated as monomers , even though most come as dimers . As the enzymes chosen are subject to the CATH domain classification , we found that it is appropriate to exploit the structural information that the classification provides as a starting point , as has been done previously by Zen et al . [9] . Moreover , we find the conformation of a subunit isolated from an oligomer is able to capture the influence of the interactions of other subunits [58] . The structures were prepared according to the domain annotation found in CATH , which included the truncation of 2 structures: the first domain of 1KKO and a domain sitting on loop 7 of 1E15 . This resulted in a set of structures with varying length , with 1KKO as the smallest at 246 amino acids , followed closely by 1N55 ( 248 ) , then 3CH0 ( 271 ) , 3CWN ( 315 ) and 1E15 ( 355 ) . Three of the five structures bind to a phosphate moiety in their substrate ( 1N55 , 3CWN and 3CH0 ) , while two structures , 1KKO and 3CH0 , have Mg2+ and Ca2+ metal ions as co-factors respectively ( S1 Table ) . There is no consensus on the positions of their catalytic or substrate binding sites on the fold . We also investigated the biological assemblies as provided by the Protein Data Bank ( PDB ) for comparison ( Cf S3 Table ) . For the first half of the analysis , we also include homologues from each of these superfamilies . The homologues were retrieved from Blastp , searched against the PDB database . The lowest E-values were chosen for each , where the hits were not identical to the query sequence ( i . e . below 99% identical ) and did not have the same taxonomic rank . This led us to pick four additional structures for all the superfamilies except for the Enolase , where only two other structures were found to fit the criteria , resulting in a total of 23 structures analysed ( S2 Table ) . The sequence alignments performed to determine sequence identities between the various structures were generated using MUSCLE [59] as implemented in JALVIEW [60] . The web service SIAS ( http://imed . med . ucm . es/Tools/sias . html ) was then used to calculate the sequence identity which is defined as the following: S=100⋅ ( IL ) ( 1 ) where I is the number of identical residues and L is the length of the alignment , including the gaps . We obtained the structural alignments from MUSTANG [36] . This alignment program was one of the top three performers in a benchmarking study [61] and at aligning TIM Barrel proteins reliably [25] . It aligns the structures using the topological information from the Cα atoms in the backbone via an optimised progressive pairwise algorithm . The resulting alignment was used in the FASTA format for the comparative analysis of the intrinsic dynamics . To obtain the description of the intrinsic dynamics of these structures , we first constructed their Elastic Network Model ( ENM ) for normal mode analysis ( NMA ) . The ENMs were constructed using the Cα force field[62] , as implemented in Molecular Modelling Toolkit [63] . Each amino acid is represented by a mass at the position of its Cα atom . The following pair potential describes the interaction between two Cα atoms , Vij ( r ) =kij2 ( ‖rij‖−‖rij0‖ ) 2 ( 2 ) where: kij={arij0−b , forrij0<dc ( rij0 ) −6 , forrij0≥d ( 3 ) rij is the pair distance vector between two Cα atom positions i and j , while rij0 is the corresponding pair distance vector in the input configuration . Hinsen [62] parameterised the force constants kij in the construction of the force field , such that a = 8 . 6x105 kJ mol−1 nm−3; b = 2 . 39x105 kJ mol−1 nm2; c = 128 kJ mol−1 nm4 and d = 0 . 4 nm . The potential energy of a configuration r of the ENM is then: V ( r ) =∑i=1N∑j=i+1NVij ( r ) ( 4 ) The normal modes are eigenvectors of the mass weighted matrix of second order partial derivatives of the potential V . The eigenvalues correspond to the squares of the frequencies for each mode . The Bhattacharyya coefficient ( BC ) score is calculated based on Fuglebakk [37] , as it is implemented in WEBnm@ [64] . The BC score compares the effective covariances , from the subset modes of the aligned cores of two structures , ( A and B ) , such that: BC=exp ( −12ln[|12 ( A∼+B∼ ) | ( |A∼||B∼| ) −12] ) ( 5 ) Here |X| denotes the determinant of X and the rank of the matrices are reduced in two steps: First , An and Bm are obtained from the n and m lowest frequency modes of their respective proteins and normalised by dividing by their trace . Then , A∼ and B∼ are obtained by projecting An and Bm on to s eigenvectors of ( An+Bm ) /2 that explain most of its variance . For each comparison n and m are chosen so that 95% of the variance of each protein is retained and s so that 75% of the variance of ( An+Bm ) /2 is retained . The fluctuation of the Cα atom of each residue can be described as a sum over its displacement for all non-trivial modes that are weighted by their eigenvalues . The fluctuation for each residue position , Fi is given by the equation: Fi=∑m=13N−6||[dm]i||2λm ( 6 ) where λj is the eigenvalue of mode j , N is the number of modes , and [dm]i is the displacement vector for residue i in mode m . The normalised deformations are based on the definition in [65] . The deformation energy for each Cα atom , for a single mode is given by: Ei=N∑j=1N|dj|2⋅12∑j=1Nkij| ( di−dj ) ⋅ ( ri0−rj0 ) |2|ri0−rj0|2 ( 7 ) where N is the number of Cα atoms in the protein , di and dj are the displacement vectors of atoms i and j and ri0-rj0 is the corresponding pair distance vector in the input configuration , respectively . The normalised deformation energy ( Di ) for a particular Cα atom position i is summed and then normalised over all non-trivial modes ( 3N-6 ) by the following: Di=∑m=13N−6[Em]i3N−6 ( 8 ) where [Em]i is the deformation energy Ei from ( 7 ) for mode m . The correlation matrix as defined by Ichiye and Karplus [66] is calculated from the normal modes . Each element in the matrix quantifies the coupling between two atoms i and j as: Cij=∑m=13N−61λm[vm]i⋅[vm]j ( ∑m=13N−6[vm]i⋅[vm]i ) 12⋅ ( ∑m=13N−6[vm]j⋅[vm]j ) 12 ( 9 ) where vm and λm are eigenvectors and eigenvalues of the mth normal mode respectively and the i and j subscripts denote the component of the mode corresponding to individual atoms , summed over all non-trivial modes . Cij is the expected inner product of displacements of atom i and j , and ranges from –1 to 1 , where –1 and 1 are maximal anti-correlations and correlations , respectively , and 0 represents a lack of any correlation . For visual inspection , strong Cij correlation scores in the correlation matrix collected as objects are represented as sticks in Figs 6 and 8 , as implemented in [39] . The correlation scores are chosen to reflect the 95th percentile rank of their absolute values , because their magnitudes describe correlations of the same strength . We chose a percentile threshold instead of a threshold based on the value of the pairwise correlation because of its strength to identify the most significant correlations in a protein structure . In the absence of such a criteria , the choice of a threshold based on a hard correlation value cut-off , would imply that we arbitrarily decide which correlation values are relevant without a reliable reference . The correlated pairs of Cα atoms are later separated by positions that have positive correlations above the 95th percentile and those that have negative correlations below the negative of this score . Furthermore , only the correlations between atoms that are at least 8Å apart are considered , to filter out the pairs of Cα atoms whose correlations are along the peptide backbone and are heavily influenced by adjacent bonding and interactions due to close proximity . These pairs of Cα atoms are also linked by the springs with the stronger force constants in the ENM . The distance threshold is reduced to 4Å , while the score threshold is increased to the 97 . 5th percentile rank when examining signification correlations that originate at the β-strands , as it corresponds to the approximate distance of the Cα atoms in adjacent strands ( S8 Fig ) . The objects resulting from the search are visualised using the molecular graphics program PyMOL [67] as sticks between atom pairs , in red when positive and blue when negative . The ideal vector overlap is a method that allows the characterisation of the normal modes vectors as simplified displacements in the protein structure [40] . The calculation of the dot product ( overlap ) between a displacement vector and the full set of normal modes identifies which modes contribute most to the given displacement . Low energy modes are characterised by larger amplitude motions while higher energy modes describe motions with smaller amplitudes . Hence displacements contributed by low energy modes will have larger amplitudes than those contributed to by higher energy modes . We defined two types of displacement vectors: i ) normalised rotational and vertical displacements of the α-helical bundles and β-barrel cores ( 8 SSEs each ) of the five main TBF structures analysed ( Fig 11A and S1 Methods ) , ii ) vectors describing vertical , horizontal , tilting and bending displacements of the N- and C-terminal halves of individual SSEs ( Fig 11B and S1 Methods ) . The overlap score , Ωw , to evaluate these is given by: Ωw=∑m=13N−6λm ( z⋅vm ) 2 ( 10 ) where vm is the normal mode vector of mode m , and z is the 3N normalised ideal vector ( as defined in S1 Methods ) , where N is the number of Cα atoms . The Ωw score is a cumulative sum that includes the energetic contribution , such that the sum of the squared overlaps is weighted by the modes’ eigenvalue over all non-trivial modes . This results in a score which is high if the displacement is energetically unfavourable , and vice-versa . | Proteins are dynamic entities , and their flexibility is intimately linked with function . Some suggest that function drives the conservation of flexibility in proteins , while it has also been observed that proteins with similar structures also exhibit similar flexibility . To investigate the role of shape vs . function in the conservation of protein flexibility , we studied proteins with the TIM Barrel Fold , a common enzyme scaffold , from five functionally distinct protein families , computationally . The results from our comparative analysis approach show that the similarity in structural fold dictates overall similarity in intrinsic flexibility between the proteins . In particular , the dynamic characteristics TIM Barrel Fold allow it to accommodate a variety of differences in enzymatic active site positioning without disrupting its overall flexibility . This study has an implication in the way we understand the evolvability of protein sequence and structure in nature . In addition , the signature patterns of flexibility we describe should be considered when designing novel enzymes with the same fold . | [
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] | 2016 | Similarity in Shape Dictates Signature Intrinsic Dynamics Despite No Functional Conservation in TIM Barrel Enzymes |
Fragile X syndrome is caused by loss of function of a single gene encoding the Fragile X Mental Retardation Protein ( FMRP ) . This RNA-binding protein , widely expressed in mammalian tissues , is particularly abundant in neurons and is a component of messenger ribonucleoprotein ( mRNP ) complexes present within the translational apparatus . The absence of FMRP in neurons is believed to cause translation dysregulation and defects in mRNA transport essential for local protein synthesis and for synaptic development and maturation . A prevalent model posits that FMRP is a nucleocytoplasmic shuttling protein that transports its mRNA targets from the nucleus to the translation machinery . However , it is not known which of the multiple FMRP isoforms , resulting from the numerous alternatively spliced FMR1 transcripts variants , would be involved in such a process . Using a new generation of anti-FMRP antibodies and recombinant expression , we show here that the most commonly expressed human FMRP isoforms ( ISO1 and 7 ) do not localize to the nucleus . Instead , specific FMRP isoforms 6 and 12 ( ISO6 and 12 ) , containing a novel C-terminal domain , were the only isoforms that localized to the nuclei in cultured human cells . These isoforms localized to specific p80-coilin and SMN positive structures that were identified as Cajal bodies . The Cajal body localization signal was confined to a 17 amino acid stretch in the C-terminus of human ISO6 and is lacking in a mouse Iso6 variant . As FMRP is an RNA-binding protein , its presence in Cajal bodies suggests additional functions in nuclear post-transcriptional RNA metabolism . Supporting this hypothesis , a missense mutation ( I304N ) , known to alter the KH2-mediated RNA binding properties of FMRP , abolishes the localization of human FMRP ISO6 to Cajal bodies . These findings open unexplored avenues in search for new insights into the pathophysiology of Fragile X Syndrome .
Fragile X syndrome , one of the most frequent human genetic diseases , is caused by the silencing of the FMR1 gene that codes for a heterogeneous set of Fragile X Mental Retardation protein ( FMRP ) isoforms [1]–[3] . FMRP , particularly abundant in neurons [4] , contains two KH domains and an RGG box , both common characteristics amongst RNA-binding proteins [5] and is localized in the cytoplasm . FMRP is a component of messenger ribonucleoprotein complexes present within the translation apparatus [6]–[8] , while in neuronal extensions , it is also found in granules containing mRNA that are transported towards autonomous translation micro-domains present in synapses and in growth cones distant from the soma [9] , [10] . The most prevalent concept regarding the absence of FMRP is that it causes translation dysregulation and defects in mRNA transport which are thought to alter local protein synthesis essential for synaptic development and maturation [11] . FMRP has been reported to associate with several hundred mRNAs , as detected by high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation ( HITS-CLIP ) [12] . A prevalent model posits that FMRP is a nucleocytoplasmic shuttling protein that transports its mRNA targets out of the nucleus [13]–[15] . Despite the fact that FMRP has been observed in the nucleus [16] , the nature and potential role ( s ) of nuclear localized FMRP remain unknown . Using a new generation of antibodies against FMRP , we present evidence that the most common FMRP isoforms , which are associated with the translation machinery , are not detected in the nucleus . In contrast , FMRP isoforms 6 and 12 ( ISO6 and ISO12 ) [17] were found to be predominantly nuclear and more specifically associated with Cajal bodies . These observations suggest that the nuclear FMRP isoforms may have functions independent from the major cytoplasmic FMRP isoforms . This , in turn , also suggests that nuclear post-transcriptional RNA metabolism could be implicated in the pathophysiology of Fragile X syndrome .
While validating a new generation of antibodies against FMRP raised in chicken [18] , we were intrigued by the fact that several batches of IgYs stained , in addition to the classical cytoplasmic distribution , distinct intense dots in the nucleus of HeLa cells . These nuclear dots have not been previously detected using any sera or antibodies raised against FMRP . Double immunostaining of HeLa cells with mAb1C3 [4] , a widely used monoclonal antibody against FMRP , and with IgYC10 [18] revealed that both antibodies stained the cytoplasm as expected ( Figure 1A ) . To ascertain that the IgY were specific for FMRP , we affinity-purified the anti-FMRP IgY using recombinant hFMRP . The resulting IgY still reacted with both the nuclear and cytoplamic structures that were eliminated when the immunoreactions were performed in the presence of recombinant hFMRP competitor ( data not shown ) . Although IgYC10 stains strongly the cytoplasm in both human and murine cells in culture , it detects the nuclear structures only in human cells precluding any further analyses in the mouse model . In addition , the detection of FMRP in these nuclear structures by immunofluorescence analyses with the newly developed IgYC10 , but not with the classical mAb1C3 , strongly suggests that the epitope lying between amino acid 66 to 112 recognized by the latter [4] is not accessible in these structures . We therefore used human fibroblasts to validate whether the nuclear structures were specific to FMRP . Fibroblasts from healthy donors showed the same nuclear and cytoplasmic staining patterns as those observed in HeLa cells , however , the nuclear foci present in >85% of HeLa cells were only detected in <20% of the human fibroblasts . As expected , no FMRP staining was observed in fibroblasts derived from Fragile X patients ( Figure 1B ) , clearly demonstrating the specificity of IgYC10 . The round shaped bright nuclear foci , typically two to six in number , reminiscent of Gems in HeLa cells [19] , prompted us to investigate whether these FMRP positive structures could correspond to Cajal bodies . These bodies are nuclear structures known to be involved , among others things in histone pre-mRNA transcription and 3′-end processing , as well as in assembly and maturation of RNP complexes , including splicing snRNPs , snoRNPs , scaRNPs and the telomerase RNP [20]–[23] . Double immunostaining with antibodies against Coilin and SMN , marker proteins for Cajal bodies [20]–[23] , confirmed that the nuclear FMRP detected with the IgYC10 antibody is indeed associated with these structures ( Figure 1C ) . Similar results were obtained with the human embryonic kidney 293 ( HEK293 ) cells ( data not shown ) . The observation that FMRP localizes in Cajal bodies raised the question of whether FMRP is only transiently present in the nucleus as part of a shuttling process , or if a specific FMRP sub-population is targeted to the Cajal bodies to remain there . Using standard cell fractionation analyses , it has been estimated that 5 to 10% of total FMRP is recovered within the nuclear fraction ( Figure 2A and [15] ) . However , when we increased the concentration of the nuclear sample ( ∼20 µg as for the total sample ) , additional bands of lower molecular weights could be detected ( Figure 2A ) . Either these bands corresponded to new FMRP species , or to degradation products . To our knowledge it has not been proven yet whether this so-called nuclear FMRP is present inside the nucleus or is associated with the nuclear enriched fraction obtained after cell lysis as pointed out by Sittler et al [17] . To investigate this question , HeLa cells grown on coverslips were lysed in situ in the presence of a buffer containing the non-ionic detergent NP40 to remove most of the cytoplasm , while a cold-resistant cytoskeletal framework containing the cell nucleus [24]–[26] remained attached to the coverslip . After such a treatment , we observed that the FMRP cytoplasmic staining detected with IgYC10 was greatly reduced and was mainly present as perinuclear granular structures outside of the nucleus ( Figure 2B ) embedded in the cytoskeleton framework , as highlighted using an anti-tubulin antibody ( Figure 2C ) . The same cytoplasmic distribution was also observed using mAb1C3 ( Figure S1 ) . On the other hand , FMRP-containing nuclear foci were still detectable following this treatment , strongly arguing that these are indeed nuclear structures . It has been reported that when cells are treated with Leptomycin B ( LMB ) , an inhibitor of the Exportin1/CRM1 pathway of nuclear export of RNA and nuclear proteins containing an NES [27] , FMRP accumulates in the nucleus [28] . We therefore treated HeLa cells with 50 ng/ml LMB , as described [28] . However , such a treatment for 20 hours turned to be lethal , indicating strong toxicity of the drug at that concentration . It is noteworthy that the LMB treatment described in [28] was applied 48 hours post-transfection , at a time when cells may contain too much overexpressed FMRP which was shown to cause deleterious effects [29] . Since the turnover of FMRP synthesis and its stability are not known , we hypothesized that a long treatment would be necessary for FMRP to accumulate in the nucleus and prevent its exit . We therefore tested several doses of LMB that could be tolerated for 18 hours , to allow any accumulation of FMRP in the nucleus . We determined that cells could tolerate a treatment of 2 ng/ml ( 3 . 7 µM ) and remained apparently normal by visual inspection under the microscope . Immunofluorescence with IgYC10 showed that a very slight increase of dispersed endogenous FMRP could be detected in the nucleus of treated cells as compared to untreated cells , which showed distinct staining of Cajal bodies ( Figure 3A and B ) . However this slight increase was likely due to fragmentation and disintegration of Cajal bodies after treatment with LMB and redistribution of its components into nuclear speckles rather than the sequestration of FMRP in the nucleus as claimed [28] . Indeed , double-staining of HeLa cells with IgYC10 and anti-coilin IgG showed that the core protein coilin was redistributed in the nucleoplasm after LMB treatment ( Figure 3 ) , as previously documented [30] , [31] . Altogether , these results do not provide support for the hypothesis that full length FMRP isoforms ( ISO1 or ISO7 ) shuttle in and out of the nucleus to escort its putative mRNA targets as previously suggested [13]–[15] . Since the bona fide full length FMRP detected in nuclear preparations appears to be a perinuclear contaminant ( Figure 2 ) , and that GFP-tagged full length FMRP is exclusively cytoplasmic ( Figure 4 ) , what would then correspond to the signals detected in Cajal bodies ? These unexpected results prompted us to examine whether the new anti-FMRP IgYC10 could recognize nuclear isoforms that have not been detected previously . Indeed , the primary FMR1 transcripts undergo extensive alternative splicing [32]–[35] leading to numerous potentially different mRNAs as suggested by RT-PCR analyses . Alternative splicing affects the presence of exon 12 and 14 and the choice of acceptor sites in exons 15 and 17 [17] . ISO1 is the longest isoform , while ISO7 lacking exon 12 is the most commonly expressed form in all cells tested . Four short FMRP isoforms ( ISO4 , 6 , 10 and 12 ) lack the proposed nuclear export signal ( NES ) and the RGG domains and have C-termini divergent from the major proteins ISO1 and 7 ( Figure 4A ) . The ISO4 , 6 , 10 and 12 isoforms were shown to have a nuclear localization as detected using transient transfection assays [17] . To determine if one or more of the four nuclear isoforms is present in Cajal bodies , and to test whether a fluorescently tagged version of the nuclear FMRP isoforms would behave similarly to the endogenous protein , we inserted the cDNA coding for each of the four nuclear FMRP isoforms in frame downstream of monomeric GFP cDNA and transfected them in HeLa cells and STEK fmr1−/− KO cells lacking FMRP . Immunoblot analyses of transiently expressed FMRP isoforms in STEK cells are shown in Figure S2 . To avoid the formation of stress granules induced by high levels of FMRP [29] , and to prevent saturation of the nuclei with FMRP , all analyses were performed within 6 hours post-transfection . As controls , we also followed the fate of GFP-ISO1 FMRP . While the latter isoform was detected exclusively in the cytoplasm ( Figure 4B ) , ISO4 , and ISO10 were uniformly distributed in the nucleoplasm while being excluded from nucleoli ( Figure 4B ) . Under the conditions used here , GFP-ISO6 and GFP-ISO12 were predominantly found associated with Cajal bodies as confirmed by their co-localization with Coilin ( Figure 4C; not shown for ISO12 ) . Because both ISO6 and 12 localize to Cajal bodies and mimic the structures detected by IgYC10 ( see Figure 1 ) , they were analyzed in further studies . Previously , sequence analysis of a cDNA encoding human ISO6 [17] revealed that this isoform was generated by alternative splicing of FMR1 pre-mRNA in which exon 13 was spliced directly to exon 15 at a distal splice acceptor site ( Figure S4A ) , eliminating exon 14 and the sequences encoded in exon 15 from the region between the proximal and distal splice acceptor sites ( labeled exon 15a ) , which are present in ISO1 ( reading frame RF3; Figure S4A ) . Splicing of exon 13 to 15 in ISO6 results in an amino acid sequence derived from reading frame 1 in exon 15b from the distal splice acceptor site ( Figure 5A and Figure S4A ) . This alternate reading frame continues through exon 16 which is spliced to exon 17 ( Figure S4B ) . In human ISO6 cDNA , exon 16 is spliced to exon 17 using a proximal splice acceptor site generating a transcript that encodes an amino sequence in reading frame 2 ( RF2; Figure S4C ) . The alternative splicing detected in the human ISO6 cDNA determined by Sittler et al . [17] is predicted to occur in a variety of other species , as shown in Figure 5A . ISO12 is similar to ISO6 except that it lacks exon 12 , which results in shortening of a loop between the β2 and β′ strands within the KH2 domain [36] . A cDNA encoding a variant of human ISO6 ( hISO6 ) has been detected in the mouse [32] , [35] . This cDNA is identical in structure to the human ISO6 cDNA except that exon 16 is spliced into exon 17 at a distal splice acceptor site and lacks 17 amino acids encoded by exon17a between the proximal and distal splice acceptors sites ( Figure 5B and Figure S5 ) . Since we were unable to detect Fmrp in Cajal bodies in mouse cells such as STEK , 3T3 , MN-1 , and primary neuron cultures , we hypothesized that the Cajal body localization signal in ISO6 might map to the 17 aa sequence encoded by exon 17a that are missing in the mouse Iso6 variant ( Figure 5B and Figure S5 ) . To test this , we first examined the localization of the mIso6 variant lacking the 17aa of exon17a using a construct containing the mIso6 variant cDNA coding sequences ( obtained from David Morris , University of Washington , Seattle ) fused downstream of GFP ( Figure 5C ) . In both human HeLa and mouse MN-1 cells , the GFP-mIso6 variant gave a general nucleoplasmic localization that was quite distinct from the Cajal body localization seen with the GFP-hISO6 ( Figure 5D ) . Next , we tested the localization of GFP-hISO6 lacking the 17 aa encoded by exon 17a ( amino acids 489 to 505 ) from GFP-hISO6 ( Figure 5C ) . As shown in Figure 5D , the hΔ489-505 ISO6 gave a nucleoplasmic localization in both HeLa and MN-1 mouse cells , similarly to GFP-mIso6 variant . Bioinformatic analysis of the 17 aa region encoded by exon 17a of hISO6 using the nuclear localization signal ( cNLS ) Mapper Program [37] , identified a cluster of conserved positively charged amino acids ( KHxR ; aa 502–505 ) at the C-terminal end of this region that were predicted to form a bipartite NLS with a second cluster of positively charged amino acids ( RRKR ; aa 522–525 ) encoded within the N-terminal sequence of exon 17b . The disruption of this bipartite NLS in hΔ489-505 ISO6 and in mIso6 may be responsible for the disruption of the Cajal body localization seen with these constructs . Having shown that endogenous cytoplasmic FMRP does not appear to traffic to the nucleus after treatment with LMB ( see above ) , we confirmed these results using transfection assays . HeLa cells were transfected with vectors encoding GFP-ISO7 and GFP-ISO6 . Four hours after transfection , LMB was added to the culture medium at a concentration of 2 ng/ml . After 20 hours of treatment with the drug , cells were processed for immunofluorescence analyses . Control cells with no LMB treatment showed strong GFP-ISO7 cytoplasmic fluorescence and as expected , the presence of FMRP in stress granules due to over-accumulation [29] . The presence of Cajal bodies in the nucleus was assessed using anti-coilin staining , and no difference could be observed between transfected and untransfected cells ( Figure 6A ) . In contrast , LMB treatment induced Cajal bodies to become dispersed , as numerous smaller coilin positive foci were redistributed in the nucleoplasm in both transfected and untransfected cells ( Figure 6A ) . On the other hand , no transfected GFP-ISO7 could be detected in the nucleus after LMB treatment , confirming our previous results with endogenous FMRP ( see Figure 3 ) . We next tested the effects of LMB on the nuclear distribution of ISO6 . In untreated cells , GFP-ISO6 was associated mainly with Cajal bodies similarly to the endogenous nuclear FMRP detected with the IgYC10 antibody ( see Figure 1 ) . After treatment with LMB , GFP-ISO6 was no longer concentrated in Cajal bodies , as it was evenly distributed throughout the nucleoplasm . Also , coilin was redistributed in the nucleoplasm , as smaller coilin positive foci were observed ( Figure 6B ) . Altogether , these results strongly suggest that the Cajal bodies signals observed for endogenous FMRP with our new antibody likely correspond to ISO6 and/or ISO12 ( Figure 3 ) . To demonstrate biochemically the presence of ISO6 and ISO12 FMRP in Cajal bodies , we isolated and purified these structures according to the procedure described by the Lamond's laboratory [38] , [39] . Immunoblot analyses of Cajal body proteins using the FMRP mAb1C3 revealed a band at approximately 44 kDa ( Figure 7A ) . The same band was observed using mAb2F5 directed against an epitope laying between amino acids 1 and 204 of FMRP [40] , as well as with IgYC10 . All three of these antibodies react with ISO1 , 6 , 7 and 12 . In contrast , a rabbit polyclonal antibody directed against the FMRP peptide RTGKDRNQKKEKPD ( amino acids 557 to 619 ) present at the C-terminus of full-length FMRP ( ISO1 ) did not react with the Cajal extracts . Since this peptide sequence is present in ISO1 and 7 , but not in ISO6 and 12 , due to the frameshift induced by alternative splicing of exon 14 , these results indicate that only ISO6/12 forms of FMRP associate with Cajal bodies . The unexpected observation that reactive FMRP in isolated Cajal bodies migrates at 44 kDa strongly suggests that ISO6/12 nuclear proteins are processed . Such a processing has been recently described for two well-known Cajal bodies markers , namely SMN and Coilin , which have been shown to be targets of calpain [41]–[43] . Contrary to proteases that fully degrade a substrate protein , calpains are calcium-dependent cysteine proteases that act by limited specific cleavages . We therefore examined whether the 44 kDa FMRP reactive protein could correspond to ISO6/12 that had undergone limited cleavage of the full length proteins , which have apparent molecular weights of 62 and 60 kDa respectively ( see Figure S3 ) . Bioinformatic searches [44] predict that the highest scoring calpain cleavage site is situated at amino acid 369 , yielding an FMRP form with a theoretical molecular weight of 42 kDa , a value close to the observed apparent molecular weight of 44 kDa obtained in SDS-PAGE . To determine if the 44 kDa product we observed is generated by Calpain digestion , we assayed the cleavage susceptibility of ISO6 in a cell-free assay . This nuclear isoform was used as a model since it is the longest nuclear isoform . ISO6 was transiently expressed in STEK Fmr1−/− KO cells that lack FMRP , and cell lysates were incubated in the presence of IgYC10 and anti-chicken antibodies coupled to agarose beads . The immunocomplex was treated in situ in the presence of 0 . 05 U Calpain1 for 10 and 20 minutes . As controls , the complexes were either not treated , or treated in the presence of ALLN , an inhibitor of Calpain 1 . The results ( Figure 7B ) clearly showed that a FMRP fragment at approximately 44 kDa was generated after incubation of ISO6 with Calpain1 . Additional bands were detected around 60 and 57 kDa , that we interpreted to be intermediate cleavage products . Since the majority of FMRP present in the immunocomplex attached to the agarose beads yielded only partial cleavage , we hypothesized that the cleavage sites could be structurally protected in the immunocomplex preventing its complete digestion . We therefore conducted the Calpain 1 assay using total extracts obtained from STEK cells transfected with pTL1-ISO6 FMR1 . A clearer picture was obtained since a progressive decrease of ISO6 could be followed while intermediate species were generated ( Figure 7B ) . Finally , we compared the digestion patterns of ISO6 and ISO7 , which showed the main cleavage product at 44 kDa , while intermediate cleavage products ( Figure 7C ) were different in agreement with the fact that ISO7 contains additional cleavage sites . In the absence of any information about putative RNAs that would bind ISO6 , we performed RNA binding assays using homopolymer RNAs conjugated to agarose beads [45] to determine the ability of ISO6 to bind RNA compared to its full-length ISO1 counterpart . Despite the fact that it lacks the C-terminal RGG domain present in ISO1 , we observed that ISO6 was preferentially retained on polyG and to a lesser extent to polyU , but not to polyA or polyC ( Figure 8B ) , a pattern of binding to RNA homopolymers similar to that observed for FMRP ISO1 [5] . Since our studies suggested that ISO6 is processed in Cajal bodies to yield a 44 kDa protein lacking the C-terminus , but still retaining the KH1 and KH2 domains , we examined the RNA binding of the cleaved ISO6 from extracted Cajal bodies . We observed the same binding patterns seen with full length ISO6 . The binding activity to polyG and polyU was still stable at 300 mM NaCl . These results strongly suggest that the affinity of ISO6 to polyG is not due to the RGG domain , but rather to the intrinsic properties of the KH1 and KH2 domains , that are shared by the different FMRP isoforms . Our results suggest that ISO6 might also play a role in interacting with RNA in Cajal bodies . Our homopolymer binding assays indicated that the RNA binding properties of the KH domains of processed FMRP ISO6 were conserved . It was previously shown that a missense mutation of an isoleucine to asparagine ( I304N ) in the second KH-type RNA-binding domain , identified in a patient with a severe Fragile X phenotype [46] , greatly affects the RNA binding properties of FMRP [47]–[49] . We therefore introduced this mutation in the GFP-FMRP-ISO6 construct in order to study the nuclear fate of the mutant protein . In repeated experiments , we consistently observed that ISO6-I304N displayed a significantly diminished association with Cajal bodies , and was found mostly in the nucleoplasm ( Figure 9A ) . These results strongly suggest that the RNA-binding properties of FMRP-ISO6 are necessary for the protein to be incorporated in Cajal bodies . We next reasoned that this reduced or partial co-localization of ISO6-I304N with Cajal bodies might be a reflection of differential dynamic behavior . In order to study the dynamic turnover of ISO6 and ISO6-I304N in Cajal bodies , we performed Fluorescence Recovery After Photobleaching ( FRAP ) experiments ( Figure 9B ) . Indeed , the kinetics of recovery of a photobleached GFP-tagged protein can be seen as a reflection of its degree of association with other proteins and/or nucleic acids . For these experiments , HeLa cells were transfected with either GFP-ISO6 , GFP-ISO6-I304N , GFP-coilin or GFP-SMN for comparison purposes , and cells were imaged after ∼6 h post-transfection as above . Briefly , one fluorescent protein-containing Cajal body per cell nucleus was photobleached with a brief laser pulse and a series of images then captured in rapid succession ( Figure 9B ) . Subsequent quantification of the fluorescent intensities within the photobleached area plotted over time was used to derive mobile fraction ( MF ) and half time of recovery ( t1/2 ) values . Using this approach , we obtained a MF of 0 . 43 with a t1/2 of 1 . 5 sec for GFP-FMRP-ISO6 in Cajal bodies ( Figure 9C ) . This represents significantly faster kinetics than the other Cajal body-resident proteins GFP-coilin and GFP-SMN ( Figure 9C ) , which both display comparable and slower dynamic turnover as documented previously [31] , [50] . Strikingly , a much faster turnover rate was obtained for GFP-ISO6-I304N mutant ( Figure 9C ) , consistent with this mutation likely disrupting one or more binding events as predicted . Altogether , these findings provide evidence for a novel function of the FMRP isoforms ISO6 and/or ISO12 in the nucleus , likely involving interaction with RNA . Most importantly , our results with the I304N patient mutation also suggest that loss of this novel nuclear function might contribute to the Fragile X syndrome .
FMRP is a cytoplasmic protein associated with the translation apparatus . However , it has been initially reported that FMRP was observed as intense nuclear staining in esophageal epithelium of mice [16] , but the reasons for this singular presence in nuclei remain obscure . Despite 20 years of intense efforts to detect FMRP in the nucleus in cell cultures , even when using the power of transfection assays , it is puzzling that the highest score ever reported is 0 . 4% of nuclei positive for FMRP [51] . It is worth mentioning that this estimate was obtained after 38 h of transfection , at a time when cells are overloaded with FMRP , which induces the formation of stress granules containing FMRP ( Figure S3 ) and the repression of translation with all its deleterious consequences [29] . Based on the assumption that FMRP is present in the nucleus , and that it contains NLS and putative NES signals , it has been widely accepted that FMRP is therefore a nucleocytoplasmic shuttling protein . Supporting this model , FMRP has been detected by immunogold electron microscopy in the nucleoplasm and in nuclear pores within neurons of rat brain , and it was deduced from the obtained images that FMRP was in transit between the nucleus and the cytoplasm [15] . However , it is important to recall that the mAb1C3 used ( previously also referred as to mAb1a ) is directed against an epitope laying between amino acids 66 and 112 at the N-terminus of FMRP [4] . Since the N-terminus is maintained and is common to all FMRP isoforms , it is not possible to determine which of the nuclear or cytoplasmic FMRP isoform ( s ) has ( have ) been detected . While truncated FMRP lacking the carboxyl portion and the NES , localizes to the nucleus [4] , the intact protein with the NES is cytoplasmic and barely penetrates the nucleus even when fused to the SV40 large T-antigen NLS [51] if one is careful not to observe FMRP at late times when cells are overly saturated with expressed protein . Indeed , we have observed that when overexpressed in transient transfection assays , FMRP accumulates to such high levels in the cell that any unusual images of distorted FMRP can be interpreted to the taste of the observer ( Figure S3 ) . Based on our results , we speculate that the sequence ( 429-LRLERLQI-438 ) present in the full length FMRP , although reminiscent of Rev-Rex-PKI NES nuclear export sequences ( LPPLERLTL ) behaves rather as a cytoplasmic anchoring domain or as a cytoplasmic retention domain ( CRD ) as proposed earlier [17] , [52] . What would then be the factors controlling the activities of the NLS and CRD ? FMRP contains two regions predicted to have a significant propensity to form coiled coil motifs involved in protein-protein interactions [52] . As illustrated in Figure 10A , the first coiled-coil domain is situated adjacent to the N-terminal NLS , while the second domain overlaps the C-terminal CRD domain . A third domain , the Agenet , also referred to as the NDF ( N-terminal domain of FMRP ) [53] , also overlaps with the NLS . Interestingly , all three regions are platforms for different known protein-protein interactors as illustrated in Figure 10A . Details of FMRP domains involved in interaction with protein partners can be found in [18] , [52]–[59] . By binding to these domains of FMRP , protein partners would lock either the NLS or the CRD domains or both , the result of which will be the retention of FMRP in the cytoplasmic compartment . In agreement with this mechanism , we have recently observed that Caprin1 interacts with FMRP at position 427–442 within the CRD [18] . On the other hand , we propose that isoforms of FMRP lacking the CRD , such as ISO6 and ISO12 have their NLS available to cellular factors that might guide them to the nucleus . Since the C-terminus of ISO6 differs from that of ISO1 ( Figure 10A ) it is not expected a priori that protein partners interacting with ISO1 at its C-terminus would also interact physically with ISO6 . On the other hand , as the N-terminus is maintained it is expected that protein partners that associate with ISO1 , also interact with ISO6 . It is fascinating that NUFIP has been originally shown to interact with ISO12 [59] , to shuttle between the nucleus and the cytoplasm [60] and to be implicated in nuclear RNPs biogenesis [61] . Since , according to our model , FMRP full length would not penetrate the nucleus , we postulate that it interacts at the periphery of nuclei with the nuclear pre-mRNPs that are just emerging from the nuclear pores to chaperon them to the translation machinery ( Figure 10B ) . Consistent with this view , cytoplasmic FMRP is concentrated at the periphery of the nucleus , lying in the perinuclear area ( see Figure 2B ) . Of the four nuclear FMRP isoforms that have been detected and characterized [17] , we found that only ISO6 and ISO12 are targeted to Cajal bodies . While the alternately spliced ISO6 and 12 mRNA variants have been shown to be actively translated [35] this is not the case for ISO4 and 10 mRNAs . Bioinformatic analyses predict that the latter two are substrates for the nonsense-mediated mRNA ( NMD ) pathway . Previous studies have shown that transiently expressed ISO12 FMRP is predominantly localized in the perinucleolar region [53] . It is worth mentioning that Cajal bodies were originally termed as nucleolar «accessory bodies» as they were detected close or in direct contact with the nucleolus ( drawings from Santiago Ramón y Cajal can be found in ref [62] ) . It is therefore possible that the nucleolar localization of ISO12 , shown in [53] , represents a static snapshot of dynamic events taking place between Cajal bodies and nucleoli , a phenomenon which has been documented in vivo by time lapse microscopy [63] . The fact that ISO6/12 are RNA-binding proteins favors the hypothesis that they could be implicated in nuclear post-transcriptional RNA control . As highlighted by the I304N mutation , the RNA-binding properties of ISO6 FMRP are necessary to allow its localization and perhaps its stability into Cajal bodies . However this hypothesis does not rule out that they might have additional or other function ( s ) as Cajal bodies have been reported to be involved in histone transcription and 3′-end processing , in assembly and maturation of RNP complexes , including splicing snRNPs , snoRNPs , scaRNPs and the telomerase RNP [reviewed in 20]–[23] , [64] . The functional significance of the processing of nuclear FMRP by Calpain 1 remains unknown , as both processed and unprocessed nuclear isoforms are able to bind homopolymer RNAs . It is worth noting that calpain cleavage at amino acid 369 is in the variable loop of the KH2 domain and leaves the 42/44 kDa truncated ISO6/12 with the ancestral KH core domain structure likely to be functional . Processing by calpain has been reported for coilin and SMN [41]–[43] , known proteins associated with Cajal bodies . This mechanism might be necessary for their assembly . Further studies using antibodies to different portions of ISO6 FMRP will be required to determine where the processing of ISO6 takes place . A probable location will be in the nucleus , since cytoplasmic FMRP associated with polyribosomes seems to be protected as it is not processed . The fragile X syndrome by its peculiar mode of inheritance and its unusual dynamic mutations makes exception in the context of classical genetics . The analyses of the functions of the numerous isoforms as well as of their differential and complex expression pattern in different tissues [35] may hold further surprises . We believe that the present study opens unexplored avenues in search for new insights into the pathophysiology of Fragile X syndrome .
HeLa S3 cell line was purchased from ATCC , and STEK Fmr1−/− KO cell line was established as previously described [29] . Motoneuron-derived MN-1 cells have been described previously [65] . Cultures from human fibroblasts were obtained from Mahmoud Rouabhia ( Faculté de médecine dentaire , Université Laval , Québec , Canada ) . Fragile X fibroblasts GM05131 and GM05848 were obtained from Coriell Cell Repository ( Camden , NJ , USA ) . Cells were propagated and maintained in DMEM supplemented with 10% FBS and antibiotics ( 100 units/ml penicillin , 50 mg/ml streptomycin ) . For transfection assays , Lipofectamine 2000 ( Invitrogen ) was used according to the manufacturer's protocol . Treatments with Leptomycin B ( Sigma ) were performed at the indicated doses and periods as detailed in the Results section . | Fragile X syndrome is the most common form of inherited mental retardation affecting approximately 1/7000 females and 1/4000 males worldwide . The syndrome is due to the silencing of a single gene , the Fragile Mental Retardation 1 ( FMR1 ) , that codes for a protein called the Fragile X mental retardation protein ( FMRP ) . This protein , highly expressed in the brain , controls local protein synthesis essential for neuronal development and maturation . While considerable efforts have been focused on understanding FMRP functions in mental retardation , the pathophysiology of the syndrome is not well understood . Here , we show that in addition to the well-studied roles of FMRP in regulating protein synthesis , a minor species of FMRP different from the major one , is specifically found in structures called Cajal bodies present in the cell nucleus . Our observations suggest that different FMRP species , also called isoforms , might have independent cellular functions . These findings might open new avenues in search for new insights in the pathophysiology of Fragile X Syndrome . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Nuclear Fragile X Mental Retardation Protein Is localized to Cajal Bodies |
Gene-regulatory enhancers have been identified using various approaches , including evolutionary conservation , regulatory protein binding , chromatin modifications , and DNA sequence motifs . To integrate these different approaches , we developed EnhancerFinder , a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity . EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs , evolutionary patterns , and diverse functional genomics datasets from a variety of cell types . In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line , we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser . We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data , such as sequence motifs , evolutionary conservation , or the binding of enhancer-associated proteins . We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues , likely due to their uniquely high GC content . We applied EnhancerFinder to the entire human genome and predicted 84 , 301 developmental enhancers and their tissue specificity . These predictions provide specific functional annotations for large amounts of human non-coding DNA , and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies . We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci . Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track , which we hope will enable researchers to further investigate questions in developmental biology .
Eukaryotic gene expression is regulated by a highly orchestrated network of events , including the binding of regulatory proteins to DNA , chemical modifications to DNA and nucleosomes , recruitment of the transcriptional machinery , splicing , and post-transcriptional modifications . Enhancers are genomic regions that influence the timing , amplitude , and tissue specificity of gene expression through the binding of transcription factors and co-factors that increase transcription ( as reviewed in [1] , [2] ) . In humans , genetic variation in enhancer regions is implicated in a wide variety of developmental disorders , diseases , and adverse responses to treatments [3] , [4] , [5] . Enhancers have been discovered in introns , exons , intergenic regions megabases away from their target genes [6] , and even on different chromosomes [7] . An enhancer frequently drives only one of many domains of a gene's expression [8] , [9] and different cell types accordingly exhibit considerable differences in their active enhancers [10] , [11] . This modularity enables the creation of complex regulatory programs that can evolve relatively easily between closely related species [12] , [13] . Individual enhancers were initially identified using transgenic assays in cultured cell lines [14] , [15] and later in vivo in model organisms , such as mouse , Drosophila , and zebrafish . In the in vivo experiments , a construct containing the sequence to be tested for enhancer activity , a minimal promoter , and a reporter gene ( e . g . , lacZ ) is injected into fertilized eggs , and transgenic individuals are assayed for reporter gene expression . Early efforts to find enhancers at the genome scale used comparative genomics . Several studies assayed non-coding regions conserved across diverse species for enhancer activity [16] , [17] , [18] , since functional non-coding regions likely evolve under negative selection . This approach identified many enhancers at a range of levels of evolutionary conservation [19] , [20] , [21] . However , relying on evolutionary conservation alone has several shortcomings: many characterized enhancers are not conserved between species [22] , non-coding conservation is not specific to enhancer elements , and evolutionary patterns provide little information about the tissue and timing of enhancer activity . Enhancer prediction has been revolutionized by recent technological advances , including chromatin immunoprecipitation coupled with high-throughput sequencing ( ChIP-seq ) [23] , RNA sequencing ( RNA-seq ) , and sequencing of DNaseI-digested chromatin ( DNase-seq ) [24] or formaldehyde-assisted isolation of regulatory elements ( FAIRE-seq ) [25] . These “functional genomics” assays enable genome-wide measurement of histone modifications , binding sites of regulatory proteins , transcription levels , and the structural conformation of DNA . The ENCODE project [26] , FANTOM project [27] , and similar studies focused on specific cell types [28] , [29] have dramatically increased the amount of publicly available functional genomics data . Functional genomics studies revealed several genomic signatures of active enhancers . For example , known enhancers are associated with the unstable histone variants H3 . 3 and H2A . Z [30] , [31] and low nucleosome occupancy [32] , although these chromatin states are not unique to enhancers . Monomethylation of lysine 4 on histone H3 ( H3K4me1 ) , a lack of trimethylation at the same site ( H3K4me3 ) , and acetylation of lysine 27 on histone H3 ( H3K27ac ) may distinguish active enhancers from promoters [10] , [33] , [34] , enhancers that are “poised” for activity later in development [35] , [36] , and regulatory elements that repress gene expression [37] , [38] . Additional features that pinpoint specific classes of active enhancers include binding of the transcriptional cofactor p300/CBP [18] , [39] , [40] , [41] , clusters of transcription factor ( TF ) binding sites [42] , [43] , [44] , [45] , and enhancer RNA transcription ( eRNAs ) [46] . Collectively , functional genomics data have pinpointed the locations of many novel enhancers and yielded insights into sequence and structural determinants of enhancer activity . However , these patterns have not proven to be universal [47] , [48] , and there is unlikely to be a single chromatin signature that identifies all classes of enhancers [11] , [49] , [50] . Given the complexity of these functional genomics data sets , computational methods have been developed to improve and generalize the enhancer predictions made from simple combinations of these data . Support vector machines ( SVMs ) and linear regression models trained to interpret DNA sequence motifs underlying known enhancers have successfully identified novel enhancers active in heart [51] , hindbrain [52] , and muscle [53] development . Another approach used SVMs to learn patterns of short DNA sequence motifs that distinguish markers of potential enhancers , such as p300 and H3K4me1 , in different cellular contexts [54] , [55] . Random forests have been used to predict p300 binding sites from histone modifications in human embryonic stem cells and lung fibroblasts [56] . Machine-learning algorithms have also been applied to the related problem of selecting functional TF binding sites out of the thousands of hits to a TF's binding motif throughout the genome [57] , [58] , [59] , [60] , [61] , [62] , [63] . Finally , two groups have taken a less supervised approach and used hidden Markov models ( ChromHMM ) [64] and dynamic Bayesian networks ( Segway ) [65] to segment the human genome into regions with unique signatures in ENCODE data and then assigned potential functions , such as enhancer activity , to these states . While rich datasets coupled with sophisticated algorithms have successfully identified many novel enhancers , comprehensive enhancer prediction is challenging for two main reasons . First , no single type of data is currently sufficient to identify all enhancers active in a given context . Many of the approaches described above use a single mark or motif as a proxy for an enhancer , but this gives an incomplete representation of all biologically active enhancers . Second , while a great deal of functional genomics data are available for different cell lines and tissues , it is not understood how informative experiments in a given cellular context are indicative of enhancer activity in other contexts . With these issues in mind , we introduce EnhancerFinder , a new two-step machine-learning method for predicting enhancers and their tissue specificity . In machine learning , a classification algorithm is trained to distinguish between labeled training examples ( e . g . , enhancers and non-enhancers ) based on features of these labeled examples ( e . g . , evolutionary conservation , chromatin signature , DNA sequence ) . The trained classifier can then be used to predict the labels for uncharacterized genomic regions ( e . g . , which ones are enhancers ) . Our approach employs two rounds of a supervised machine-learning technique called multiple kernel learning ( MKL ) [66] , [67] . MKL is based on the theory of SVMs [68] , but provides greater flexibility to combine diverse data ( e . g . , evolutionary conservation , sequence motifs , and functional genomics data from different cellular contexts ) and to interpret their relative contributions to the resulting predictions . Our implementation of EnhancerFinder applies MKL in two steps with the goal of generating a genome-wide set of developmental enhancers to better characterize gene regulation during development . The algorithm , which is trained using in vivo validated enhancers from the VISTA enhancer database [69] and publicly available genomic data , first aims to distinguish human developmental enhancers from the genomic background and then in a second step predicts enhancer tissue specificity . In contrast to most other enhancer prediction strategies , which are trained on epigenetic marks or sequence motifs that serve as a proxy for a subset of all active enhancers , our use of a heterogeneous and in vivo validated set of enhancers , enables us to investigate the complex suite of features that underlie active regulatory regions . With appropriate training data , EnhancerFinder could be applied to study gene regulation at other developmental stages . Our analyses demonstrate that EnhancerFinder's integration of diverse types of data from different cellular contexts significantly improves prediction of validated enhancers over approaches based on a single context or type of data . We find that enhancers active in some developmental contexts are easier to identify than others . Applying EnhancerFinder to the entire human genome allowed us to predict more than 80 , 000 developmental enhancers , with tissue-specific predictions for brain , limb , and heart . These predictions significantly overlap known non-coding regulatory regions and are enriched near relevant genome-wide association study ( GWAS ) lead single nucleotide polymorphisms ( SNPs ) and genes expressed in the predicted tissue . To illustrate the utility and accuracy of our genome-wide enhancer predictions , we used them to investigate the enhancer landscape near three developmentally expressed genes . First , we screened predicted enhancers near FOXC1 and FOXC2 in transgenic zebrafish , and found that 70% ( 7 of 10 ) of tested EnhancerFinder predictions have confirmed ( 6 ) or suggestive ( 1 ) developmental enhancer activity . In addition , we validated a novel cranial nerve enhancer near the ZEB2 locus using a transgenic mouse enhancer assay . Taken together , our results suggest that the EnhancerFinder approach of integrating diverse data sets significantly improves prediction of biologically active enhancers , providing high-confidence candidate enhancers for studies in developmental gene regulation .
Step 1 of our pipeline aims to distinguish all enhancers active in the context of interest ( e . g . , a specific developmental stage ) from non-enhancer regions . Step 2 then builds classifiers to predict the tissues in which the enhancer candidates from Step 1 are active . This two-step approach allows us to accurately identify enhancers , while also distinguishing their tissues of activity . We train and evaluate EnhancerFinder using the VISTA Enhancer Browser , which at the time of our analysis contained over 700 human sequences with experimentally validated enhancer activity in at least one tissue at embryonic day 11 . 5 ( E11 . 5 ) in transgenic mouse embryos . VISTA also contained a similar number of regions without enhancer activity in this context . E11 . 5 in mouse development roughly corresponds to E41 ( Carnegie stage 17 [70] ) in human development . In Step 1 of EnhancerFinder , we used all 711 VISTA enhancers as positive training data , and for negative training data , we created a set of 711 random regions matched to the length and chromosome distribution of the positives to represent the genomic background . We did not use the VISTA negatives as negative training examples in Step 1 , because they are not representative of all non-enhancer regions ( see below ) . Our goal in Step 1 is to develop a method that can be used to scan the whole genome and distinguish developmental enhancer regions from non-enhancer regions . The second step of EnhancerFinder aims to distinguish enhancers active in a given embryonic tissue from non-enhancers and enhancers active in other tissues . We consider all enhancers in VISTA with activity in a tissue of interest as positives and all other regions in VISTA ( including regions not active at E11 . 5 ) as negatives ( see Methods ) . This second step that includes enhancers active in other tissues as negatives in the training proves to be essential for obtaining high specificity in predicting tissue of activity ( see below ) , and it is important to do this in two steps rather attempting to distinguish enhancers of a given tissue from genomic background in one step . To evaluate EnhancerFinder , we compared it to several commonly used enhancer prediction approaches . Unless otherwise noted , we evaluated the performance of all prediction algorithms using 10-fold cross validation to compute the area under the curve ( AUC ) for receiver operating characteristic ( ROC ) curves . We also computed precision-recall curves ( Figure S1 ) and compared power at a low false positive rate . Because EnhancerFinder learns enhancer signatures from a training data set , we first explored biases in the VISTA enhancers that might affect how well EnhancerFinder could generalize to the whole genome . The genomic regions tested by VISTA were not selected randomly , and thus their positives do not represent a random sample of active enhancers . Nearly all regions tested by VISTA are evolutionarily conserved across mammals ( 706 of 711 positives and 727 of 736 negatives ) . Since our goal is to predict a broadly applicable , high confidence set of developmental enhancers , we did not include this feature when making genome wide predictions . However , with this bias in mind , we did evaluate several models that incorporate the degree of evolutionary conservation ( see below ) . In addition to conservation , several studies deposited in VISTA have considered enhancer-associated proteins and histone marks , such as p300 , H3K27ac , and H3K4me1 . We collected all data sets of these types from ENCODE and computed their overlap with VISTA enhancers . Fewer than half of the VISTA positives are marked by all three of p300 , H3K27ac , and H3K4me1 ( from any data set ) , with substantial percentages marked by only one or two and 13% ( 93/711 ) marked by none ( Figure S2 ) . These findings indicate that VISTA positives are not highly biased towards a single type of ChIP-seq feature , motivating us to include these features in our genome-wide predictions , with the caveat that the trends we observe for VISTA positives might not generalize to all classes of enhancers . Our analysis also suggests that the standard practice of equating active enhancers with all regions marked by a single ChIP-seq feature , or even the union of overlapping peaks from several ChIP-seq experiments , will fail to identify all active enhancers in a given context . EnhancerFinder predicts enhancers by integrating classifiers based on distinct data types . In our first evaluation of EnhancerFinder , we consider: functional genomics data , evolutionary conservation patterns , and DNA sequence motifs . Combining these different approaches enables EnhancerFinder to accurately distinguish enhancers from the genomic background ( Figure 2A; AUC = 0 . 96 ) . The functional genomics component of EnhancerFinder ( which we refer to as All Functional Genomics ) is a linear SVM that incorporates 2469 datasets generated by the ENCODE project and smaller scale studies . These include DNaseI hypersensitivity data and ChIP-Seq for p300 , many histone modifications , and many TFs from many adult and embryonic tissues and cell lines ( Table S1 ) . DNA sequence patterns are integrated via a 4-spectrum kernel ( DNA Motifs ) , which summarizes the occurrence of all length four DNA sequences ( 4-mers ) in input regions [71] . We found that little was gained by increasing k , considering multiple k simultaneously , or incorporating knowledge of transcription factor binding site ( TFBS ) motifs as in a previous approach [52] ( Figures S3 and S4 ) . Finally , evolutionary conservation information is incorporated with a linear SVM that uses mammalian phastCons scores [72] as features ( Evolutionary Conservation ) . One motivation for developing EnhancerFinder was to explore whether combining previous successful approaches to enhancer prediction would improve performance . Each of the classifiers combined in EnhancerFinder is representative of a different strategy for predicting enhancers . Thus , we compared the performance of EnhancerFinder to each of its constituents , which are SVMs trained on the same enhancer data as EnhancerFinder , but using only one type of the data features ( e . g . , only sequence motifs ) . EnhancerFinder significantly outperformed each of the individual classifiers ( Figure 2A; p = 2 . 0E-7 for Evolutionary Conservation , p = 2 . 6E-8 for DNA Motifs , and p = 4 . 4E-16 for All Functional Genomics , McNemar's test ) , suggesting that these different types of data capture unique aspects of enhancers that are not completely encompassed by any single data type . Not surprisingly , we found that of the three component classifiers in EnhancerFinder , Evolutionary Conservation yields the best performance ( AUC = 0 . 93 ) . As noted above , nearly all regions tested for enhancer activity by VISTA ( positives and negatives ) are evolutionarily conserved compared to the genomic background . Nonetheless , considering additional features significantly improved predictions . The DNA Motifs ( AUC = 0 . 88 ) and All Functional Genomics ( AUC = 0 . 89 ) classifiers also exhibit strong performance , but also do not perform as well as the combined classifier . EnhancerFinder has nearly twice the power of any of the individual classifiers at a 5% false positive rate ( FPR ) , and its power advantage is even larger at lower FPRs . All Functional Genomics , DNA Motifs , and Evolutionary Conservation achieve roughly similar performance from different feature data , but each individual classifier predicts a somewhat different set of enhancers during evaluation ( Figure 2B ) . Roughly two-thirds of the enhancer predictions are shared between the three classifiers . The improvement provided by combining these data argues that these data sources are indeed complementary . We also compared EnhancerFinder's performance with several current computational methods used to identify enhancers . We were able to make the most direct comparison with CLARE , a popular method for identifying enhancers from DNA sequence data , i . e . , transcription factor binding site motifs and other sequence patterns [73] . This approach , which has been successfully applied in several contexts [51] , [52] , [53] , [74] , makes few assumptions about the input , and is publicly available as a web server . On our Step 1 enhancer prediction task , we find that CLARE achieves an ROC AUC of 0 . 79 . This is much lower than DNA Motifs ( AUC = 0 . 88 ) , our approach based on sequence data alone , and the full EnhancerFinder ( AUC = 0 . 96; Figure 2C ) . At a 5% FPR , the power of CLARE is about 20% , compared to approximately 30% for DNA Motifs and more than 60% for EnhancerFinder . Comparisons with additional methods were complicated by the fact that most were developed in different contexts . We designed EnhancerFinder specifically to predict biologically active developmental enhancers . Most existing approaches focus on data from a single cell line and define enhancers based on specific enhancer-associated marks or proteins ( such as p300 in human embryonic stem cells ) rather than biological activity . Thus , we did not anticipate that they would perform as well as EnhancerFinder at developmental enhancer prediction . However , since the predictions of these methods are commonly used outside the specific contexts in which they were made , we believe that it is useful to evaluate how well they can identify developmental enhancers and how much the EnhancerFinder approach applied to developmental enhancers improves on their performance . In particular , we compared EnhancerFinder to ChromHMM and Segway [64] , , two unsupervised machine learning methods for segmenting the genome into a small number of functional “states” based on consistent patterns in ENCODE data for individual cell lines . The states resulting from the segmentations of each cell line's data are annotated by hand into predicted functional classes , which include enhancer activity . To evaluate these methods , we considered the states overlapping our training and testing regions . Any region with an overlapping enhancer state was considered a predicted enhancer and all others were predicted non-enhancers . In this way , we obtained a single point in ROC space for the state predictions . Since there is no score or confidence value associated with the state assignments , a full ROC curve could not be created for these methods . Figure 2C gives the performance for several versions of ChromHMM and Segway based on ENCODE data from different cell lines . Both methods perform better than random , but considerably worse than EnhancerFinder and CLARE ( p≈0 ) . We stress that , in contrast to our supervised method , these methods were not explicitly trained to perform the same task as EnhancerFinder , and thus we did not expect them to perform as well as EnhancerFinder . Indeed , these results argue that their utility in identifying developmental enhancers is limited compared to specialized approaches . As illustrated above , our machine learning prediction and evaluation framework enabled us to quantitatively explore the utility of different genomics datasets in enhancer prediction by creating classifiers based on different types of data ( i . e . , sequence motifs , evolutionary conservation , and functional genomics ) and comparing their performance . We also used this framework to investigate other questions about the utility of different subsets of these data for enhancer prediction . For example , one might expect that some of the datasets included in All Functional Genomics ( e . g . , experiments in cancer cell lines or adult tissues ) would not be as useful as others ( e . g . , experiments in embryonic tissues ) for predicting developmental enhancers , and that limiting the features examined by the classifier to the most relevant experiments might improve performance . To explore this hypothesis , we trained linear SVM classifiers to predict VISTA enhancers ( as in Step 1 of EnhancerFinder ) based on different subsets of all the functional genomics features ( Table 1 ) and compared their performance . First , we considered a collection of 244 datasets from embryonic tissues and cell lines ( Embryonic Functional Genomics ) . Next , we created a classifier that considers data from a wider range of contexts by training a linear SVM using a large , manually curated set of 509 potentially relevant functional genomics data sets ( Relevant Functional Genomics ) . This set includes embryonic datasets , along with additional DNaseI and ChIP-seq data from adult tissues and cell lines related to the dominant tissues of activity in VISTA . For example , we included data from human cardiac myocytes , since there are many developmental heart enhancers in our training examples . We compared these to the All Functional Genomics classifier described above that uses all 2496 functional genomics features . All Functional Genomics ( AUC = 0 . 89 ) performed slightly , but not significantly , better than Relevant Functional Genomics ( AUC = 0 . 87; p = 0 . 16 ) , and both significantly outperformed Embryonic Functional Genomics ( AUC = 0 . 83; p = 9 . 2E-9 and p = 2 . 7E-6 , respectively ) ( Figure 3A ) . At low FPRs , the differences in power between these classifiers were modest . The Embryonic Functional Genomics classifier included the most time-appropriate datasets , yet its performance was improved by including additional data sets that seem less relevant to our classification problem a priori . Thus , we conclude that it can be advantageous to consider a range of functional genomics features , especially when few features are available from the context of interest . The utility of these additional data sets might indicate that some enhancer features are stable across cell types and developmental stages , but it could also reflect information these data provide about genomic regions that are not active enhancers during development ( see Discussion ) . We also explored the utility of individual functional genomics datasets that are often used as proxies for developmental enhancers by creating three linear SVM classifiers: H3K27ac , H3K4me1 , and p300 . These SVMs were trained to distinguish VISTA positives from the genomic background ( Step 1 ) using all available data of the specified type from ENCODE , which include a range of cell types and tissues ( Table S1 ) . All three classifiers performed better than random ( Figure 3B ) . H3K4me1 ( AUC = 0 . 72 ) and p300 ( AUC = 0 . 68 ) performed similarly ( p = 0 . 25 ) , with p300 performing best at low FPRs and H3K4me1 best at higher FPRs . Both significantly outperformed H3K27ac ( AUC = 0 . 61; p = 9 . 4E-15 and p = 5 . 5E-9 , respectively ) ; however , we caution against extrapolating from this comparison , since it may reflect biases in the feature sets available and the VISTA positives . Since combinations of these features are often used to predict enhancers , we next trained a linear SVM classifier ( Basic Functional Genomics ) that includes all three data types together . The combined classifier significantly outperforms all the individual classifiers ( AUC = 0 . 77; p<2E-7 for each ) , suggesting that each data type contributes unique information about enhancer activity . Also , all four SVM classifiers achieved much better performance than the common approach of simply considering regions overlapping with these data ( Figure S5 ) . EnhancerFinder also learns weights for individual features within classifiers that reflect their contribution to the enhancer predictions . We found that features known to be associated with enhancer activity in relevant cellular contexts generally receive positive weights , while those associated with other types of elements received negative weights ( Text S1 and Figure S6 ) . In the previous sections , we focused on generic developmental enhancer prediction ( Step 1 of EnhancerFinder ) . Step 2 of EnhancerFinder applies a second round of MKL to refine and further annotate predicted enhancers from Step 1 ( Figure 1 ) . In this study , Step 2 consists of training an MKL classifier to distinguish VISTA enhancers active in a given tissue from VISTA regions without activity in that tissue , i . e . , non-enhancers from VISTA plus enhancers for other tissues . We did not require that the positive training examples be active only in the tissue of interest . Using the same feature data as in Step 1 , we created tissue-specific classifiers for all tissues with more than 50 examples in VISTA: forebrain , midbrain , hindbrain , heart , limb , and neural tube . The performance of EnhancerFinder's tissue specificity predictions varied dramatically between tissues ( Figure 4 ) , with the best performance for heart ( AUC = 0 . 85 ) , followed by limb ( AUC = 0 . 74 ) , forebrain ( AUC = 0 . 72 ) , midbrain ( AUC = 0 . 72 ) , hindbrain ( AUC = 0 . 69 ) , and neural tube ( AUC = 0 . 62 ) , which was the worst of the tested tissue classifiers , but better than random . We combined all brain enhancers into one class , and the performance of this generic brain classifier was similar to that of the more specific brain classifiers ( AUC = 0 . 73 ) . The EnhancerFinder tissue-specific classifiers trained with all data types performed well for most tissues ( Table 1 ) ; however , classifiers based on functional genomics alone often performed as well as the full EnhancerFinder classifier , suggesting functional genomics data are more informative about developmental enhancer tissue specificity than degree of conservation or sequence motifs . Most previous efforts to predict tissue-specific enhancers have performed a single training step using enhancers or enhancer marks present in the tissue of interest as positives and non-enhancer regions or the genomic background as negatives . To test whether our two-step method improves upon these previous approaches , we trained one-step MKL tissue-specific classifiers and compared their predicted tissue distributions to those of validated enhancers from the VISTA database ( Figure 5A ) . First , we trained a set of tissue-specific classifiers using enhancers active in each tissue as positives and the genomic background as negatives . These classifiers predict very similar sets of enhancers regardless of the target tissue; and they vastly overestimate the number of enhancers that are active in multiple tissues ( 95% of predictions versus 8% of VISTA ) and the number of true enhancers of each tissue ( Figure 5B ) . In contrast , classifiers trained as in Step 2 of EnhancerFinder , i . e . , using tissue-specific enhancers as positives and a mix of enhancers active in other tissues and regions with no activity in VISTA as negatives , show much greater tissue-specificity in their predictions ( 76% ) and a similar amount of overlap as among known enhancers ( Figure 5C ) . The relative ease of identifying heart enhancers is likely due to several unique characteristics . Known heart enhancers at E11 . 5 are more evolutionarily conserved than genomic background , but significantly less conserved than enhancers in other tissues [39] , [41] . In addition , we observed that heart enhancers at this developmental stage are uniquely close to the nearest transcription start site ( TSS ) ( Figure S7 ) . These two patterns are consistent with a recent study of mouse enhancers from different developmental stages [75] . Finally , we observed that E11 . 5 heart enhancers have an unusually high GC content ( 49% ) compared to enhancers of other tissues at E11 . 5 ( ∼40% ) . A simple classifier based solely on the GC content of a region performs nearly as well as our full classifier for heart enhancers ( Figure S8 ) . In contrast , sequence-based classifiers do not perform well on the other tissues whose enhancer GC content is not significantly different from the genomic background ( Table 1 ) . The high GC content of heart enhancers is not due to overlap with CpG islands . Only about 4% of VISTA enhancers overlap with a CpG island , and this number is consistent across tissues . We also did not find enrichment for any known GC-rich transcription factor binding site motifs in VISTA heart enhancers . We do see , however , that repeat regions in heart enhancers are depleted for the very AT-rich repeats seen in other enhancers , and that most of the repeat regions in heart enhancers are 40–60% GC . Our results suggest the possible existence of unknown GC-rich motifs that may be important for gene regulation in the cardiac lineage . The heart classifier based on functional genomics data alone exhibits strong performance compared to other tissue-specific classifiers as well ( Table 1 ) . It is possible that this is due to the presence of feature data from contexts more relevant to developmental heart activity than to other tissues , rather than unique attributes of the heart enhancers themselves . Indeed , the highest weighted features in the heart functional genomics classifier come from heart tissues . However , the performance of the heart classifier based only on functional genomics data does not decrease substantially when we exclude data from the most relevant contexts: embryonic heart tissue , adult hearts , and stages of a directed differentiation of stem cells into cardiomyocytes ( ROC AUC = 0 . 85 ) . Thus , it is possible that feature data from less obviously relevant contexts are more informative about heart activity than for other tissues . We suspect that the ease of distinguishing heart enhancers may be due to the earlier development of the heart compared to other tissues ( see Discussion ) . One of the main motivations for developing algorithms that can distinguish active enhancers is to apply them to unannotated genomic regions to aid the exploration and interpretation of the gene regulatory landscape of the human genome ( Figure 1 ) . To produce a genome-wide set of candidate developmental enhancers , we divided the genome into 1 . 5 kb blocks overlapping one another by 500 bp and applied Step 1 of EnhancerFinder to each of these regions . EnhancerFinder produces a score for each region; positive scores indicate membership in the positive set ( enhancers ) , and negative scores indicate membership in the negative set ( non-enhancers ) . To focus on high confidence predictions in this genome-wide analysis , we used the cross-validation-based evaluation described above to find a 5% FPR score threshold , and only considered regions exceeding this threshold . After merging overlapping positive predictions , we identified 84 , 301 developmental enhancers across the human genome with median length of 1 , 500 bp and total genome coverage of 183 , 695 , 500 bp ( 5 . 86% ) . The 5% FPR threshold we used corresponds to a 65% true positive rate ( TPR ) . To calculate the false discovery rate ( FDR ) , we must estimate the unknown fraction of 1 . 5 kb blocks of the human genome that harbor developmental enhancer regions . If this fraction were as high as 50% , a 5% FPR would correspond to a 9% FDR . If instead we estimate that 10% of 1 . 5 kb windows contain a developmental enhancer , we see an FDR of 47% at a 5% FPR . While this may seem high , our recent analysis of predicted enhancers with human-specific substitution rate acceleration found a lower failure rate at E11 . 5 ( 17% , 5/29 ) [74] , and only three of ten tested predictions did not validate with confirmed or suggestive activity in our zebrafish assay ( see below ) . This suggests that the FDR may be lower in experimental applications , especially when predicted enhancer regions are analyzed in the context of other relevant data . However , to accurately measure the true FDR would require experimental testing of a very large , random set of EnhancerFinder predictions , which is beyond the scope of this study . In our genome-wide analysis , we used the smaller Relevant Functional Genomics data set in order to reduce the computational time required . We also did not include evolutionary conservation data , because the positives in our training data are almost universally conserved . While most enhancers likely exhibit some evolutionary conservation , this extremely high fraction is likely due to bias in the selection of the tested regions in VISTA and could reduce our ability to detect less highly conserved novel enhancers genome-wide ( see Discussion ) . The resulting conservation-free classifier still performed extremely well in cross validation ( AUC = 0 . 92 ) . Supporting this approach , non-conserved regions make up over 20% of our genome-wide enhancer predictions . As noted above , we did not observe any other dramatic biases in the feature data associated with human VISTA enhancers . Next , we applied Step 2 of EnhancerFinder to all enhancer regions predicted in Step 1 . We focused on brain , limb , and heart , because these tissues are highly represented in VISTA and have been extensively studied in previous analyses of developmental enhancers . We predicted 7 , 400 limb enhancers , 19 , 051 heart enhancers , and 11 , 693 brain enhancers ( Figure 6 ) at a 5% FPR threshold tuned separately for each tissue . Since EnhancerFinder makes predictions for each tissue independently , there are no constraints on the distribution of tissues in the resulting genome-wide predictions . Nonetheless , we find a high level of tissue-specificity; nearly 90% of the limb , heart , and brain enhancers are predicted to be active in just one of the three tissues . All genome-wide enhancer predictions are available as tracks for import into the UCSC Genome Browser ( Data File S1 ) . These lists of high-confidence tissue-specific enhancers should not be viewed as exhaustive; we found thousands of regions with positive , but less significant scores from Step 2 of EnhancerFinder . To characterize and further validate our genome-wide enhancer predictions , we examined their genomic distribution with respect to several independent indicators of function ( details in Text S1 ) . Genes near brain and heart enhancers are enriched for expression in relevant tissues ( Tables S2 and S3 ) . Similarly , Gene Ontology ( GO ) Biological Process enrichment analyses of nearby genes suggest that our predicted developmental enhancers target genes that function in relevant cell types and tissues ( Figure 6 ) . The most prevalent transcription factor binding site motifs found in the sequences of predicted enhancers differed between enhancers of different tissues and included many relevant developmental TFs ( Table S4 ) . Finally , our predicted enhancers contain 676 lead SNPs associated with significant effects in GWAS ( Table S5 ) ; this is significantly more than expected at random ( permutation p<0 . 001 ) . Taken together , these analyses suggest that EnhancerFinder identifies many active regulatory regions that contain functionally relevant variation . Our tissue-specific enhancer predictions give valuable annotations to thousands of non-coding regions of the human genome that had not previously been linked to developmental regulation . For example , thousands of SNPs associated with disease by GWAS are in non-coding regions with limited functional annotations [76] . Our genome-wide enhancer predictions provide a resource for exploring the mechanisms and functional effects of these uncharacterized GWAS hits . To demonstrate that genome-wide EnhancerFinder predictions can facilitate the discovery of functional regulatory elements , we present two case studies in which we identify and validate novel enhancers near genes active during development .
We chose to define developmental enhancers for training as genomic regions that are experimentally shown to activate gene expression in vivo in embryonic mouse assays . We believe that this definition is better suited to identifying regions for further exploration and experimental characterization than approaches based on single data sources , such as p300 , H3K4me1 , or H3K27ac , associated with enhancers in individual cell lines . We showed that our predicted enhancers , based on this biologically active definition , significantly overlap data sets commonly used as proxies for enhancer activity , such as H3K27ac and p300 binding . However , these other data alone are not sufficient to identify all enhancers , as we demonstrated for H3K27ac , H3K4me1 , and p300 in Figure 3B . Similarly , when we evaluated the ability of other computational methods to identify enhancers , we find that they perform better than random , but that EnhancerFinder significantly outperforms them at identifying biologically active developmental enhancers . This is not surprising given the different contexts in which some enhancer predictions , such as those from ChromHMM and Segway , were developed . While EnhancerFinder could be used to predict enhancers in well-characterized cell lines , it is particularly useful at identifying enhancers in complex tissues that contain multiple cell types and in cell types that do not have much specific functional genomics data available . Other computational approaches to enhancer prediction have focused on identifying enhancers in individual cell types using functional genomics data from the same cells [56] or using the differences in cell type specific transcription factor binding to identify cell-type specific binding motifs [61] . These methods generally perform well , but they do not address enhancer prediction in cell types with little or no functional genomics data , or in tissues that contain multiple cell types . Data such as p300 binding sites and H3K4me1 have been used in previous studies to identify enhancers , and these data are major contributors to our enhancer predictions . However , data from other sources and contexts less directly associated with enhancer activity provide complementary information that improves our predictions . Some of these data may be negatively correlated with enhancer activity , allowing EnhancerFinder to learn what features distinguish regions that are not developmental enhancers . Others may help reinforce patterns present in data from more relevant contexts , reflecting some degree of stability in the features of enhancer regions across developmental stages and cell types . For example , we found that features measured in embryonic stem cells are quite useful for E11 . 5 enhancer prediction; their removal from the classifier degrades performance and/or they have large ( positive or negative ) MKL weights . Examination of these features suggests that some identify “poised” regions that will become active enhancers upon differentiation , while others seem to help distinguish stem cell enhancers ( i . e . , non-enhancers at E11 . 5 ) from those specific to differentiated lineages . We note that despite these interesting observations , most individual functional genomics features do not carry a great deal of information and the power of EnhancerFinder comes from the integration of different types of data . It is also possible that as a more complete experimental characterization of chromatin state and protein-DNA binding from E11 . 5 tissues is obtained , data from less relevant contexts will not provide as much improvement as it did in this study . We focused on a single developmental stage with a large number of validated enhancers . To efficiently extend enhancer detection and validation to new contexts , it will be very important to select the most informative data to collect . Even though the ENCODE project has produced an impressive amount of data , it still has not extensively assayed most contexts of interest to researchers , in particular developmental biologists . The performance of classifiers trained on subsets of all our data and the weights we learned for feature sets and individual features provide some guidance for future experiments . Evolutionary conservation and DNA sequence patterns are broadly useful in the identification of enhancers , but our results suggest that adding functional genomics data is necessary to make more precise predictions about the contexts of activity . H3K4me1 and p300 are two of the most useful functional genomics data types overall ( Figure S6 ) , but many others are useful in particular contexts . However , the non-random sampling of functional genomics data and enhancers makes definitively determining the relative utility of different data types challenging . We saw a broad range in our ability to predict the tissue specificity of enhancers from existing data . Heart enhancers were dramatically easier to identify than other tissue-specific enhancers . Heart enhancers have significantly higher GC content than enhancers of other tissues , are less evolutionarily conserved , and are closer to the nearest TSS than other known enhancers at E11 . 5 , and we show that GC content alone is sufficient to accurately predict many heart enhancers ( Figures S7Figures S7 and S8 ) . However , functional genomics data alone were also able to accurately predict heart enhancers . The underlying biological explanation for these patterns may have to do with relative developmental age of different organs and tissues . At E11 . 5 , the heart is further along its developmental trajectory than the other tissues considered , and heart enhancers have completed their most conserved developmental stage , whereas forebrain enhancers are most strongly conserved at E11 . 5 and E14 . 5 [75] . At E11 . 5 , many of the less conserved , mammal-specific features of the heart are developing [91] , [92] , whereas other tissues are still developing under more general , less species-specific conserved regulatory programs at E11 . 5 [93] . A recent study of enhancers in the adult mouse retina found that high local GC content was strongly correlated with enhancer activity [94] . Paired with our result , this suggests that GC content is a distinguishing feature of certain classes of enhancers . In spite of the strong overall performance of EnhancerFinder at predicting tissue-specific developmental enhancers , our approach has some limitations . First , we rely heavily upon the VISTA Enhancer Browser for training examples , because it is the largest collection of validated mammalian enhancers currently available . This resource provides an impressive catalog of validated human regulatory enhancers , but it is limited to a single developmental stage and experimental system . Without more data and analysis , it is difficult to evaluate how specific our predictions are to this context . Applying EnhancerFinder to known enhancers in model organisms , such as zebrafish and fly , would provide additional opportunities to evaluate our approach and findings , while potentially demonstrating differences in how enhancers function in these different species . Second , most of the enhancers present in VISTA are evolutionarily conserved . As a result , the VISTA enhancers cannot be viewed as an exhaustive catalog of the full range of enhancers . However , these regions have validated enhancer activity in vivo , and thus provide an appealing alternative to approaches that use single-mark proxies for enhancer activity ( e . g . , considering all H3K27ac peaks as active enhancer regions ) . In addition to being conserved , these regions contain many signatures of enhancers in their sequence motifs and functional genomics composition that are useful for predicting enhancers . To emphasize these features and mitigate the impact of bias towards conserved regions , we removed evolutionary conservation as a feature from EnhancerFinder when we applied it to predict enhancers genome-wide . Our goal in doing so was to improve our ability to discern less conserved enhancers in these genome-wide predictions , and indeed , we predicted thousands of non-conserved enhancers ( ∼20% of all predictions ) . Third , though our predictions are based on a large collection of genome-wide chromatin state , protein-binding , and sequence information from many contexts , we are still limited by data availability . Even with the impressive efforts of ENCODE and related projects , producing data that are perfectly matched to all contexts of interest is time consuming and sometimes impossible , especially when studying humans . Thus , it will be important to develop a principled understanding of how different data can be generalized across tissues , developmental stages , and between species . In our analysis , many of the highest weighted features come from contexts close to the developmental stage of interest , and thus we anticipate that gathering more data from developmentally relevant cells and tissues will significantly improve our ability to annotate genomic regions involved in the regulation of embryonic development . However , data from other , seemingly unrelated , contexts may continue to prove useful . This study annotates regulatory elements in the human genome and provides tools for interpreting the effects of mutations in non-coding regions . Our case studies on regions around ZEB2 , FOXC1 , and FOXC2 illustrate how our predictions can facilitate the rapid identification of novel enhancers . In addition , the statistical enrichment for GWAS SNPs in our genome-wide enhancer predictions suggests that they may be a good resource for pinpointing causal mutations in potential disease loci . EnhancerFinder is a general framework for enhancer prediction and evaluation of different data sources that aim to annotate the regulatory functions of the human genome . It could easily be extended to include additional types of data , such as population-level variation at each locus , information about the three-dimensional state of the genome from Hi-C and 5C , and predictions of potential target genes for each enhancer . It could also be used to analyze additional aspects of the data we already consider , such as accounting for the relative genomic position of different features [66] . The EnhancerFinder two-step approach enables delineation of features common to all enhancers versus those that characterize enhancers of different types . For example , we find that predicting enhancers that are unique to a single tissue is more difficult than those that are active in multiple tissues ( Figure S9 ) , that certain features make prediction of heart enhancers particularly easy , and that different features are selected in classifiers for general enhancers and those for specific tissues . Together , these results suggest that there may be distinct classes of enhancers , even among those active in a given tissue at a single developmental stage . Further analysis of EnhancerFinder classifiers based on different types of data may help suggest biological mechanisms underlying the functional distinctions and genomic features of these different classes of enhancers .
Transgenic mice were generated by Cyagen Biosciences ( http://www . cyagen . com/ ) . Their facility meets and often exceeds animal health and welfare guidelines . Animals were euthanized using techniques recommended by the American Veterinary Medical Association . All procedures were carried out in line with Gladstone Institutes and University of California guidelines . All zebrafish work was approved by the UCSF Institutional Animal Care and Use Committee ( protocol number AN100466 ) . All work presented in this paper is based on the February 2009 assembly of the human genome ( GRCh37/hg19 ) downloaded from the UCSC Genome Browser ( http://genome . ucsc . edu/ ) . Any data that was not in reference to this build was mapped over using the liftOver tool from the UCSC Kent tools ( http://hgdownload . cse . ucsc . edu/admin/jksrc . zip ) . In our framework , genomic regions are associated with a common set of descriptive features . We then apply machine-learning algorithms that use the features of known training examples to learn a function of the feature data that distinguishes the positives ( enhancers ) from the negatives ( non-enhancers ) . This function can then be applied to the features associated with uncharacterized genomic regions to predict their enhancer status . A positive score for a genomic region indicates predicted membership in the positive class ( enhancers ) and a negative score indicates predicted membership in the negative class ( non-enhancers ) . To evaluate the performance of trained classifiers , we performed 10-fold cross-validation on the training data and quantified our results with ROC AUC , precision-recall curves , and power estimates at fixed false positive rates . We computed p-values for the difference in performance between classification methods using McNemar's test [99] , [100] . To estimate false discovery rates , we trained EnhancerFinder classifiers at 1∶1 , 1∶10 , and 1∶100 ratios of positive to negative enhancers and used the resulting 10-fold cross-validation results to calculate the proportion of false discoveries genome-wide at a 5% FPR if the true proportion of 1 . 5 kb windows containing an enhancer was 50% , 10% , or 1% . We compared EnhancerFinder's predictions to those of several previous enhancer prediction methods . We obtained the performance of CLARE on our Step 1 prediction task , by inputting our positive and negative data into the CLARE web server [73] . We downloaded the genomic segmentations and annotations produced by ChromHMM [64] and Segway [65] . We considered the ChromHMM predictions based on different ENCODE cell lines both individually and together . Any genomic region in our evaluation data set that overlapped an enhancer state was considered a predicted enhancer , and all others were considered predicted non-enhancers . For Segway , we also considered the “TF activity” state . We predicted tissue-specific developmental enhancers throughout the human genome by applying a trained MKL classifier ( Step 1 of EnhancerFinder ) without conservation ( see Results ) to sliding windows of 1500 bp , moving along the human genome in 500 bp steps . The feature profile for each window was computed as described above . To focus on high-confidence predictions , we filtered the enhancer scores for the windows at a 5% FPR , estimated from cross-validation using the genomic background , and combined the remaining overlapping windows to produce 84 , 301 high-confidence predicted enhancers . To predict tissue specificity , we applied trained brain , limb , and heart classifiers ( Step 2 of EnhancerFinder ) without conservation to all 299 , 039 windows with positive enhancer scores in Step 1 . We then applied a 5% FPR cutoff for each tissue and concatenated the remaining overlapping windows into merged enhancer regions . Using this approach , we predicted 19 , 051 heart enhancers , 11 , 693 brain enhancers , and 7 , 400 limb enhancers . We characterized the expression patterns of the gene nearest to each predicted enhancer using the GNF Atlas 2 [101] . It contains expression data for genes in 79 different tissues , with expression measured using Affymetrix microarrays . For each of these 79 tissues , we used a paired t-test to determine if the nearest genes of predicted heart enhancers had significantly different mean values of expression than the nearest genes of brain enhancers . We did not include the limb enhancers in this analysis due to the lack of relevant expression data in the GNF Atlas 2 . We examined genomic regions near predicted developmental enhancers for enrichment of Gene Ontology functional annotations , known phenotypes , and pathways using GREAT [102] . Results were computed using the hypergeometric test for genome-wide significance , with the default settings and the “basal plus extension” association rule ( proximal 5 kb upstream , 1 kb downstream , plus distal up to 100 kb ) . We identified the sequence motifs present in each set of enhancers using the FIMO tool ( Find Individual Motif Occurrences ) from the MEME Suite of sequence motif analysis tools [103] . We considered known transcription factor binding motifs from the April 2011 release of the TRANSFAC database with a FIMO score threshold of 10e-5 . We identified those occurrences that fell in predicted enhancers , and summarized motifs to identify the most prevalent TFs in each tissue-specific set of enhancers . We analyzed the overlap of predicted enhancers with GWAS SNPs , based on the NHGRI catalog of 9 , 687 GWAS SNPs downloaded from the UCSC Genome Browser in October 2012 . Unadjusted permutation p-values were calculated by randomizing genomic locations of predicted enhancers ( matching for length and chromosome , and avoiding assembly gaps ) and overlapping these randomized regions with GWAS SNPs to assess significance of overlapping regions . Mouse enhancer assays were carried out in transient transgenic mouse embryos generated by pronuclear injections of enhancer assay constructs into FVB embryos ( Cyagen Biosciences ) . Human and chimpanzee DNA sequences were inserted upstream of a minimal promoter Hsp68 and a LacZ reporter gene . The human sequence was amplified using primers 5′-TGTATGAAACCTGTTCACTCTCC-3′ and 5′-GCTTAAAACAACTACTAGAATCAGGC-3′ from the bacterial artificial chromosome ( BAC ) RP11-107E5 ( from the BacPac resource at CHORI ) . The chimpanzee sequence was amplified using primers 5′-TGTATGAAACCTGTTCACTCTCC-3′ and 5′-GCTTAAAACAACTACTAGAATCAGGC-3′ from BAC CH251-677E03a ( CHORI ) . The embryos were collected and stained for LacZ expression at E11 . 5 . Following the annotation policies of the VISTA Enhancer Browser , we required that consistent spatial expression patterns be present in three or more embryos with staining in order for the region to be considered an enhancer . Zebrafish enhancer assays were performed in transient transgenic zebrafish embryos . We tested candidate enhancer regions that ranged in length from 987 bp to 3 , 633 bp ( see Table S6 for hg19 genomic coordinates ) , which we manually demarcated from within larger predicted enhancer regions based on signatures of likely enhancer function ( including DnaseI hypersensitivity sites , transcription factor binding sites , histone modifications , and conservation ) . We performed PCR to obtain the candidate enhancer sequence using human genomic DNA ( Roche ) . These were cloned into the E1b-GFP-Tol2 enhancer assay vector containing an E1b minimal promoter followed by GFP [104] , and the construct was verified by sequencing . Each construct was injected with Tol2 mRNA into at least 100 single-cell fertilized zebrafish embryos . We annotated GFP expression at approximately 24 and 48 hours post fertilization ( hpf ) , and considered an enhancer to be positive if we observed consistent expression in at least 15% of all fish alive at either 24 or 48 hpf [105] , and suggestive of enhancer activity if we observed consistent expression in at least 10% of all fish alive at 24 or 48 hpf , after subtracting out percentages of tissue expression in fish injected with the empty enhancer vector . For each construct , at least 50 fish were analyzed for GFP expression at 48 hpf . | The human genome contains an immense amount of non-protein-coding DNA with unknown function . Some of this DNA regulates when , where , and at what levels genes are active during development . Enhancers , one type of regulatory element , are short stretches of DNA that can act as “switches” to turn a gene on or off at specific times in specific cells or tissues . Understanding where in the genome enhancers are located can provide insight into the genetic basis of development and disease . Enhancers are hard to identify , but clues about their locations are found in different types of data including DNA sequence , evolutionary history , and where proteins bind to DNA . Here , we introduce a new tool , called EnhancerFinder , which combines these data to predict the location and activity of enhancers during embryonic development . We trained EnhancerFinder on a large set of functionally validated human enhancers , and it proved to be very accurate . We used EnhancerFinder to predict tens of thousands of enhancers in the human genome and validated several of the predictions near three important developmental genes in mouse or zebrafish . EnhancerFinder's predictions will be useful in understanding functional regions hidden in the vast amounts of human non-coding DNA . | [
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] | 2014 | Integrating Diverse Datasets Improves Developmental Enhancer Prediction |
Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration . Thus , prior beliefs play a key role during the learning process , especially when only ambiguous sensory information is available . Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations – the mapping between actual and visual location of the hand – during a learning task . Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement . After experiencing a particular transformation for one reach , subjects have insufficient information to determine the exact transformation , and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach . We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior , which was found to give high probability to parameter settings consistent with visuomotor rotations . Therefore , although the set of visuomotor transformations experienced had little structure , the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations . We then exposed the same subjects to a highly-structured set of visuomotor transformations , designed to be very different from the set of visuomotor rotations . During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations . In summary , we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure . Moreover , through experience of a novel task structure , participants can appropriately alter the covariance structure of their prior .
Uncertainty poses a fundamental problem for perception , action and decision-making . Despite our sensory inputs providing only a partial and noisy view of the world , and our motor outputs being corrupted by significant amounts of noise , we are able to both perceive and act on the world in what appears to be an efficient manner [1] , [2] . The investigation of the computational principles that might underlie this capability has long been of interest to neuroscientists , behavioral economists and experimental psychologists . Helmholtz [3] was one of the first to propose that the brain might operate as an ‘inference machine’ by extracting perceptual information from uncertain sensory data through probabilistic estimation . This computational framework has now gained considerable experimental support and has recently led to the formulation of the ‘Bayesian brain’ hypothesis [4] , [5] . According to this hypothesis , the nervous system employs probabilistic internal models representing Bayesian probabilities about different states of the world that are updated in accordance with Bayesian statistics whenever new evidence is incorporated . Crucially , this update depends on two components: a prior that represents a statistical distribution over different possible states of the world , and the incoming evidence about the current state that is provided through noisy sensory data . In the Bayesian framework the prior can have a strong impact on the update , with particular priors leading to inductive biases when confronted with insufficient information . Many perceptual biases have been explained as the influence of priors learned from the statistics of the real world , such as the prior for lower speed when interpreting visual motion [6] , [7] , the prior for lights to shine from above when interpreting object shape [8] , [9] and the prior that near-vertical visual stimuli are longer than horizontal stimuli [10] . However , there are some phenomena such as the size-weight illusion – the smaller of two objects of equal weight feels heavier – that appear to act in the direction opposite to that expected from straightforward integration of the prior with sensory evidence [11] , [12] . Interestingly , despite the perceptual system thinking the smaller object is heavier , the motor system is not fooled as , after experience with the two objects , people generate identical forces when lifting them [13] . Many cognitive biases can also be explained , not as errors in reasoning , but as the appropriate application of prior information [14]–[16] , and the Bayesian approach has been particularly successful in explaining human performance in cognitive tasks [17] , [18] . In sensorimotor tasks , a number of studies have shown that when a participant is exposed to a task which has a fixed statistical distribution they incorporate this into their prior and combine it with new evidence in a way that is consistent with Bayesian estimation [5] , [19] , [20] . Similarly , when several sources of evidence with different degrees of uncertainty have to be combined , for example a visual and a haptic cue , humans integrate the two sources of evidence by giving preference to the more reliable cue in quantitative agreement with Bayesian statistics [21]–[23] . Moreover , computational models of motor control , such as optimal feedback control [24]–[27] , are based on both Bayesian estimation and utility theory and have accounted for numerous phenomena in movement neuroscience such as variability patterns [24] , bimanual movement control [28] , [29] , task adaptation [30]–[32] and object manipulation [33] . There have also been several proposals for how such Bayesian processing may be implemented in neural circuits [34]–[36] . If one uses Bayesian estimation in an attempt to learn the parameters of a new motor task , the prior over the parameters will impact on the estimates . While previously priors have been either imposed on a motor task or assumed , there has been no paradigm that allows the natural prior distribution to be assessed in sensorimotor tasks . Here we develop a technique capable of estimating the prior over tasks . We examine visuomotor transformations , in which a discrepancy is introduced between the hand's actual and visual locations , and estimate the prior over visuomotor transformations . Importantly , we are not simply trying to estimate the mean of the prior but its full covariance structure . Subjects made reaching movements which alternated between batches in which feedback of the hand's position was either veridical or had a visuomotor transformation applied to it . By exposing participants to a large range of visuomotor transformations we are able to fit a Bayesian observer model to estimate the prior . Our model assumes that at the start of each transformation batch a prior is used to instantiate the belief over visuomotor transformations and this is used to update the posterior after each trial of a transformation batch . The prior to which the belief is reset at the start of a transformation trial may change with experience . For our model we estimate the average prior used over an experimental session by assuming it is fixed within a session , as we expect the prior to only change slowly in response to the statistics of experience . Our approach allows us to study the inductive biases of visuomotor learning in a quantitative manner within a Bayesian framework and to estimate the prior distribution over transformations . Having estimated the prior in one experimental session , we examine whether extensive training in two further sessions with a particular distribution of visuomotor transformations could alter the participants' prior .
Figure 2 shows the starting location and rectangle in which the targets could appear together with 50 examples of ‘perturbation vectors’ that join the hand position on the first trial of a transformation batch to the displayed cursor position ( where is the trial index , in this case 1 ) . On the first trial of each transformation batch , the ‘target-hand vector’ joining the centre of the target to the final position of the hand ( the ‘target-hand vector’ ) was shorter than 3 cm in 90% of cases ( Figure 3 , column A , top panel ) , suggesting that the preceding veridical batches had washed out most of the learning . Subjects were instructed that on the second and subsequent trials of each transformation batch , they should attempt to compensate for the transformation in order to hit the target with the cursor . Hence on trials 2 and 3 , the proportion of final hand positions within 3 cm of the target drops to 43% ( middle panel of Figure 3 , column A ) and 36% ( bottom panel ) , respectively . Further analysis suggests that the increase in length of the target-hand vectors on trials 2 and 3 is due to subjects attempting to counter the transformation , rather than just exploring the workspace randomly . Figure 3 , column B shows that the direction of the target-hand vector tends to be opposite to that of the perturbation vector experienced on the previous trial , while column C shows that the lengths of these two vectors are positively correlated . The ratio of the length of the target-hand vector on the second trial to that of the perturbation vector on the first trial gives a measure of the extent of the adaptation induced by the experience on the first trial , with a value of zero suggesting no adaptation . We regressed this adaptation measure for all subjects and sessions ( removing a few outliers – 0 . 34% – where this measure was greater than 5 ) against the absolute angular difference between the direction of the first and second targets , in order to test the assumption made later in our modelling that adaptation generalizes across the workspace . If there were a local generalization function with a decay based on target direction we would expect that the greater the angular difference the smaller the adaptation measure . The fit had a slope which was not significantly different from zero ( ) suggesting global generalization . Compensatory responses tend to be in the correct direction: Column D shows that target-hand vectors on trials 2 and 3 tend to be in the same direction as the target-hand vector that would place the cursor on the target ( ) , and column E shows that the lengths of these two vectors are also positively correlated . This suggests that subjects are adapting within a batch so as to compensate for the induced perturbation . We fit subjects' performance on the first two trials of each transformed batch using a Bayesian observer model in which we assume subjects attempt to estimate the four parameters ( , , , & ) of the transformation matrix . We represent the subject's prior as a four-dimensional multivariate Gaussian distribution over these four parameters , centred on the identity transformation ( since subjects naturally expect the visual location of the hand to match its actual location ) . Our inference problem is to determine the covariance matrix of this prior . Figure 4 includes a schematic of a prior with the four-dimensional distribution shown as six two-dimensional marginalizations with isoprobability ellipses ( blue ) , representing the relation between all possible pairings of the four elements of the transformation matrix . An optimal observer would integrate this prior with information received on the first trial ( hand position and visual feedback of hand position ) to generate a posterior over transformations . Even if there were no noise in proprioception or vision , the information from the first trial would not uniquely specify the underlying transformation . For example , for a particular feedback on the first trial the evidence is compatible with many settings of the four parameters ( grey lines and planes in Figure 4 ) . Therefore , given the inherent ambiguity ( and noise in sensory inputs ) , the estimated transformation depends both on the sensory evidence and prior which together can be used to generate a posterior distribution over the four parameters of the transformation matrix ( Figure 4 , red ellipses ) . Our Bayesian observer then uses the most probable transformation ( the MAP estimate is the centre of the red ellipses in Figure 4 ) to determine where to point on the second trial . Our aim is to infer the prior distribution for each subject in each experimental session by fitting the pointing location on the second trial based on the experience on the first trial . The model assumes the observer starts each transformation batch within a session with the same prior distribution , although this distribution will of course be updated during each batch by combination with evidence . As shown above , these updates are washed out between batches through the interleaved veridical batches . In Session 1 , transformations were sampled so as to minimize pairwise correlations between elements of the transformation matrix . This ‘uncorrelated’ distribution was designed to avoid inducing learning of new correlations . The set of transformations experienced in the first session is shown in the top-left cell of Figure 5 , viewed in the same six projections of the four-dimensional space used in Figure 4 . The Gaussian priors fit to each of the eight subjects' data in Session 1 are shown in the middle-left cell of Figure 5 . For some pairs of elements of the transformation matrix ( e . g . ) the prior appears to show little correlation whereas for others ( e . g . ) there appears to be a stronger correlation . To quantify these relations we examined the correlation coefficients between each pair of elements of the transformation matrix across the subjects . First , to examine the consistency of the correlation across subjects we tested the null hypothesis that subjects' correlation coefficients were uniformly distributed between and ( Kolmogorov-Smirnov test ) . We found that only between elements and was the correlation significantly consistent ( ) . In addition we used a t-test to examine whether the correlations across subjects were significantly different from zero ( although correlations are strictly speaking not normally distributed ) . We found that only the correlation was significant ( mean , ) . We also analyzed the orientations of these covariance ellipses . Confidence limits on the orientation angle of the long axis of each ellipse were obtained by bootstrapping . The bottom-left cell of Figure 5 shows , for each subject , the mean angle ( thick line ) and the 95% confidence limits ( thin lines connected by curved arrows ) . The confidence limits are exclusively in the negative range for all but two subjects , while for all other pairings of elements confidence limits for most subjects overlap the or points indicative of an absence of correlation . The mean angle across subjects was ( 95% confidence limits obtained by bootstrapping of the best fits: to ) . We also found that the covariance angle was significantly positive ( mean across subjects , confidence limits to ) . Each subject participated in Session 2 between three and six days after Session 1 , and in Session 3 between seven and nine days after Session 2 ( Table 1 ) . These sessions both used a set of transformations whose distribution was chosen so as to be very different from the subjects' priors measured in Session 1 . This allowed us to examine whether we could change subjects' priors through experience . As subjects had priors with a strong negative correlation between elements and of the transformation matrix we used a ‘correlated distribution’ over transformations in which the correlation was set to , with an orientation angle of ( Figure 5 , top-right cell ) . Importantly , the two distributions used in Session 1 and in Sessions 2 & 3 were designed so that the distribution of evidence ( that is the relation between visual and actual hand locations ) shown on the first trial of each transformation batch was identical under the two distributions ( see Methods ) . Therefore any changes in behavior on the second trial ( which we use to estimate the prior ) arose because of changes in the subject's prior . The remainder of the trials within a batch have different statistics between Session 1 and Sessions 2 & 3 , so we did not use data beyond trial 2 to estimate the prior , although this could be used by the subjects to alter their internal prior . The priors fit to the data of the five subjects in Session 2 are shown in the middle-right cell of Figure 5 . We found that in Session 2 the correlations across subjects were now not significantly different from zero ( mean correlation coefficient , , t-test ) and were not distributed significantly non-uniformly across subjects ( , K-S test ) . Confidence limits ( Figure 5 , bottom-right cell ) on the covariance angle now overlapped for all but one subject , again implying the absence of correlation . Confidence limits on the mean covariance angle across subjects overlapped ( to , mean ) . A weak but significant correlation was now found ( mean , on t-test and K-S test ) , and the covariance angle continued to be positive ( mean , confidence limits to ) , although angles were not significant for any individual subject . In Session 3 ( see Figure 6 , which summarises changes in the relation across sessions ) the correlation was still not significant ( mean correlation coefficient , on t-test and on K-S test ) . The covariance angle confidence limits now overlapped zero within all subjects and across subjects ( to , mean ) . A weak but significant correlation was again found ( mean , on t-test and on K-S test ) , and the covariance angle continued to be positive ( mean across subjects , confidence limits to ) , although angles were only significant for three individual subjects . To assess the extent to which our Bayesian observer model explained the data , we compared the magnitudes of its errors in predicting hand positions to the errors made by four other models: ( A ) the ‘no-adaptation’ model , which assumes the hand hits the centre of the target on all trials; ( B ) the ‘shift’ model , which is also a Bayesian observer but assumes the transformation is a translation; ( C ) the ‘rotation & uniform scaling’ model , another Bayesian observer that assumes the transformation is a rotation combined with a scaling; ( D ) the ‘affine’ model , which is a Bayesian observer more general than the standard model in that it accounts for linear transformations combined with shifts . Comparisons of hand position prediction error were made for each trial of a transformed batch from the 2nd to the 7th , although it should be remembered that trials after the 3rd represent progressively fewer batches , with only 44% of batches lasting to the 4th trial and only 19% lasting to the 7th . The Bayesian observer models integrated information about a transformation from all previous trials of a batch when making a prediction for the next trial . Since the Bayesian observer models were all fit to data from the second trials of each transformed batch ( i . e . the standard model used the fits presented above ) , comparison of prediction errors on the second trials themselves was done using 10-fold cross-validation for these models , in order to avoid over-fitting by complex models . To compare the models we focus on trial 3 , which is late enough that the subjects have received a considerable amount of information about the transformation ( just enough to specify the whole transformation matrix , in noiseless conditions ) but early enough that all batches can be included . Figure 7 shows that on this trial the standard model makes smaller prediction errors for the hand positions ( averaged across all sessions ) than any other model . The next-best is the affine model ( mean error 4 . 50 cm , versus 4 . 34 for the linear model ) . On all other trials , the linear model is also superior to all other models . The failure of the affine model to perform better than the standard model shows that its extra complexity , which allows it to account for shifts , is not necessary . Accounting for shifts made little difference to the linear components of the fits: the correlation coefficients between pairs of elements of the transformation matrix were very similar to those in the linear model fits ( median absolute difference across all pairs: 0 . 11 ) , and the coefficients were again significantly negative in Session 1 ( on t-test and Kolmogorov-Smirnov test ) and ceased to be significantly different from zero in Sessions 2 and 3 . The covariance angles between pairs of elements were also very similar to those in the linear model fits ( median absolute difference: ) , and the angles were significantly negative in Session 1 ( 95% confidence limits: and ) and ceased to be significantly negative in Sessions 2 and 3 . We also varied the origin of the linear transformations that we used in the Bayesian observer model , to see if the coordinate system used by the experimental subjects was based around the starting point of the reaches ( small circle in Figure 8 ) , or about some other location such as the eyes ( cross in Figure 8 ) . The shading in Figure 8 represents the fitting error and shows that using the starting point of the reaches as the origin fits the data considerably better than any other position tested ( mean error: 3 . 49 cm for the starting point , versus 3 . 61 cm for the next best position ) . In particular , a repeated-measures ANOVA ( using subject number and session as the other two factors ) shows that using the starting point as origin gives significantly lower errors than using the eye position ( ) .
Previous studies have attempted to determine the natural co-ordinate system used for visuomotor transformations . The dominant paradigm has been to expose subjects to a limited alteration in the visuomotor map and examine generalisation to novel locations in the workspace . These studies show that when a single visual location is remapped to a new proprioceptive location , the visuomotor map shows extensive changes throughout the workspace when examined in one-dimensional [37]–[40] and in three-dimensional tasks [41] . These studies are limited in two ways in their ability to examine the prior over visuomotor transformations . First , they only examine how subjects generalize after experiencing one ( or a very limited set of ) alterations between visual and proprioceptive inputs . As such the results may depend on the particular perturbation chosen . Second , while the generalization to novel locations can provide information about the co-ordinate system used , it provides no information about the covariance structure of the prior . Our paradigm is able to address both these limitations using many novel visual-proprioceptive mappings to estimate the full covariance structure of the prior over visuomotor transformations . To study this covariance structure in the fitted priors , we analyzed both the correlation coefficients between elements of the transformation matrix – as a measure of the strength of the relationship between elements – and also the orientation of the covariance ellipses of pairs of elements – as a measure of the slope of the relationship . A significant strong negative correlation was seen between the off-diagonal elements of the transformation matrices in the priors found in Session 1 . Such a relation is found in a rotation matrix , as this corresponds to and in our transformation matrix . This similarity suggests a bias for subjects to interpret transformations as conforming to rotation-like structures . The and relations would still exist if a rotation were combined with a uniform scaling . We do not claim that subjects believe the transformations to be only rotations and uniform scalings . If they did , we should have found a relationship between and in the prior and a strong relationship , but the covariance angle was around and the correlation was weak . Rather , it seems likely that the subjects believed many of the transformations in Session 1 to be rotations combined with other perturbations . Vetter and colleagues [41] also found an apparent bias for rotations . However , these were rotations about the eyes , whereas the centre of the coordinate system in our model is the starting circle , approximately 30 cm in front of the eyes . We showed that our subjects' data across all sessions is best explained using the starting circle as the origin of transformations , rather than the eyes or any other location ( Figure 8 ) . The two studies are not contradictory , because our subjects were shown the cursor on top of the start circle at the start and end of every trial , and so would have been likely to learn that it was the origin of the transformations . Importantly , to measure the prior we ensured that the distribution of transformations in the first session was relatively unstructured in the space of the four elements of the transformation matrix , and in particular the distribution of transformations used had only a very small correlation between the off-diagonal elements . Therefore , it is unlikely ( particularly given the adaptation results discussed below ) that the prior for rotations came about because of the particular set of transformations used in our paradigm . Our approach of probing a subject's prior with many transformations would be disrupted if the learning of these transformations interfered with each other . Many studies have shown interference between the learning of similar but opposing visuomotor perturbations [42]–[44] , similar to that found between two dynamic perturbations [45] , [46] . However , subjects in those experiments were trained for dozens of trials on each perturbation; learning of individual transformations over just a few trials in our experiment would have been much less resilient to overwriting with new memories . Additionally , the veridical batches between each transformation in our experiment would have washed out any perceptual or non-cognitive component of learning [38] , [47]–[50] . The previous work on visuomotor generalization cited above [37]–[39] , [41] , which found that experiencing single visual-proprioceptive pairs induced remapping throughout the workspace , justifies the assumption made in the analysis of the current study that perturbations experienced at one location will induce adaptive responses throughout the workspace . In addition , our analysis shows that the magnitude of the adaptive response on the second trial does not decrease with the angular deviation of the second target from the first , providing further support for global generalization under terminal feedback . Another reaching study [51] found much more limited generalization across locations , but was criticized [41] on the grounds that the starting point of reaches was not controlled , and that subjects were constrained to make unnatural reaching movements at the height of the shoulder . Work with visual feedback of the hand position throughout the reach has found that scalings are generalized throughout the workspace but rotations are learned only locally [52] . This lack of generalization is clearly at odds with the weight of evidence from terminal-feedback studies . The difference is perhaps due to differing extents of cognitive adaptation under the two feedback conditions . Recent studies have shown that when exposed to tasks that follow a structured distribution , subjects can learn this structure and use it to facilitate learning of novel tasks corresponding to the structure [53] . In the current study , when participants were exposed to a structured distribution of transformations in Sessions 2 & 3 we found that participants' priors changed to become closer to the novel distribution . The estimated prior's negative correlation between the off-diagonal elements observed in the Session 1 priors was abolished by training on a distribution of transformations in which these off-diagonal elements were set to be equal and therefore perfectly positively correlated . This abolition in the fitted priors is evidenced both by the orientations of the covariance ellipses between the off-diagonal elements , which became clustered around , and by the correlation coefficients for this pair of elements , which also clustered around zero . Importantly , the perturbations on the first reach of each transformed batch in Sessions 2 & 3 were generated identically to those in Session 1 so that we can be sure it is the prior that has changed , as the evidence shown to the subject was identically distributed and only varied in terms of the feedback on the second and subsequent trials . Previous studies have also demonstrated the ability of people to learn priors over novel sensorimotor tasks . For instance , one study showed that subjects learned a non-zero-mean Gaussian prior over horizontal shifts [19] , while reaction-time studies [54] succeeded in teaching subjects non-uniform prior distributions over potential targets for a saccade . Similarly , other studies have shown that priors , such as the relation between size and weight [55] and over the direction of light sources in determining shape from shading [8] , can be adapted through experience of a training set which differs from the normal prior . In many of these previous studies only the mean of the learned prior was measured , and the priors were generally one-dimensional whereas in the current study we expose subjects to distributions in which there is a novel and multi-dimensional covariance structure . This difference in dimensionality may also explain why a one-dimensional structure of visuomotor rotations [53] could perhaps be learned faster than the three-dimensional structure of transformations used in Sessions 2 & 3 in the present study , which was never learned fully . As dimensionality increases , the amount of data required by a subject to specify the structure increases dramatically . In the current study we have made a number of simplifying assumptions which facilitated our analysis but which we believe in future studies could be relaxed . First , we have analysed the prior within the Cartesian coordinate system in which the prior is over the elements of the set of transformation matrices . We believe this coordinate system to be a reasonable starting point for such research , since the visuomotor generalization studies cited above found visuomotor generalization to be linear [37] , [38] , [41] . In particular , the bias seems to be for rotations [41] rather than shifts in Cartesian space , which are not linear transformations; some studies describe generalization of shifts but as they either only examine a one-dimensional array of targets [37] , [38] or a single generalization target [56] their results can not distinguish between rotations and shifts . Furthermore , the comparison of different models in this paper ( Figure 7 ) shows that our linear-transformations model performs better than a more complex affine-transformations model and simpler models such as the shift model . This suggests that our linear-transformations model is of the right level of complexity for explaining subjects' performance in this paradigm . That the shift model performed considerably better than the no-adaptation model does not show that subjects believed any transformations to have a shift component and that the extra complexity of the affine-transformations model is therefore necessary . Rather , the shift model may have simply managed to approximate linear transformations ( such as small rotations ) as shifts . A further simplifying assumption was that the prior takes on a multivariate Gaussian distribution over elements of the transformation matrix . The true prior could be both nonlinear and non-Gaussian in our parameterization and as such our estimation may be an approximation to the true prior . While it may be possible to develop techniques to find a prior which has more complex structure , such as a mixture of Gaussians , such an analysis would require far more data for the extra degrees of freedom incurred by a more complex model . Another model assumption is that the subject uses the MAP transformation to choose his hand position . Although it is common for Bayesian decision models to use point estimates of parameters when making decisions , different rules that also take into account the observer's uncertainty over the transformation may better model the data . Our model was purely parametric , with the observer performing inference directly over the parameters of the transformation matrix . In the future it will be interesting to consider hierarchical observer models which would perform inference over structures of transformations , such as rotations , uniform scaling or shearings , and simultaneously over the parameters within each structure , such as the angle of the rotation . This observer would have a prior over structures and over the parameters within each structure . Nevertheless , our study shows that we can estimate the full covariance structure of a prior in a sensorimotor task , that this prior has similar form across subjects and that it can be altered by novel experience .
All eight subjects were naïve to the purpose of the experiments . Experiments were performed using a vBOT planar robotic manipulandum [57] . Subjects used their right hand to grasp the handle , which they could move freely in the horizontal plane . A planar virtual reality projection system was used to overlay images into the plane of movement of the vBOT handle . Subjects were not able to see their arm . | When learning a new skill , such as riding a bicycle , we can adjust the commands we send to our muscles based on two sources of information . First , we can use sensory inputs to inform us how the bike is behaving . Second , we can use prior knowledge about the properties of bikes and how they behave in general . This prior knowledge is represented as a probability distribution over the properties of bikes . These two sources of information can then be combined by a process known as Bayes rule to identify optimally the properties of a particular bike . Here , we develop a novel technique to identify the probability distribution of a prior in a visuomotor learning task in which the visual location of the hand is transformed from the actual hand location , similar to when using a computer mouse . We show that subjects have a prior that tends to interpret ambiguous information about the task as arising from a visuomotor rotation but that experience of a particular set of visuomotor transformations can alter the prior . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neuroscience/behavioral",
"neuroscience",
"neuroscience/cognitive",
"neuroscience",
"neuroscience/motor",
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] | 2011 | Inferring Visuomotor Priors for Sensorimotor Learning |
A central challenge in interpreting personal genomes is determining which mutations most likely influence disease . Although progress has been made in scoring the functional impact of individual mutations , the characteristics of the genes in which those mutations are found remain largely unexplored . For example , genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants . Until now , however , it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale . Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project ( ESP ) . Specifically , we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene . To illustrate the utility of this intolerance score , we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease , but with striking variation in intolerance among genes causing different classes of genetic disease . We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations .
Many approaches are available that attempt to prioritize mutations in terms of their prior probabilities of conferring risk of disease , notably including population allele frequency and measures of conservation at either the phylogenetic level [1] or in terms of amino acid characteristics [2]–[6] . However , few analogous approaches are available for prioritizing the genes in which the variants are found , despite the fact that all groups performing contemporary sequencing studies have learned that some genes are much more likely to show at least modest ( but unconvincing ) evidence of association with risk across multiple disease areas than other genes . One reason for this outcome is that some genes carry many more putatively interesting variants in the general population , leading to more potential to show association for such variants . Here , we seek to develop a gene-level assessment that ranks genes in terms of their real likelihoods to influence disease . The basis of our approach is to rank all protein-coding human genes in terms of their intolerance to standing functional variation . This scheme is intended to rank genes on the basis of the strength and consistency of purifying selection acting against functional variation in the gene . We note , however , that any such scheme will inevitably also reflect the action of other kinds of selection ( for example , balancing selection ) . Such a scoring system can be constructed in many ways , but it would need to be standardized for gene size and total mutational rate . Using publically available data from the NHLBI Exome Sequencing Project ( ESP ) [7] we introduce a scoring system that predicts the expected amount of common functional variation based on the total amount of variation in each gene . The intolerance score itself is a measure of the deviation from this prediction . We evaluate this scoring system by examining correlations between gene scores and whether genes do or do not cause known Mendelian diseases [8] . We further evaluate how well this approach prioritizes candidate de novo mutations identified in patient genomes [9]–[15] . Critical to interpreting personal genomes , we show how our gene-level score can be integrated with well-established variant-level scores to highlight candidate causal mutations .
The primary motivation behind a gene based intolerance score is to quantitatively distinguish two categories of genes . On one hand , the ATP1A3 gene has very few functional mutations in the general population , which makes it all the more striking when 70% of patients with alternating hemiplegia of childhood were found to carry de novo missense mutations in the gene [19] . On the other hand , olfactory receptor genes often carry non-conservative amino acid substitutions and stop mutations at high frequencies in human populations yet trigger no clinical diagnosis . Clearly , to suggest causation , it would take more observations of functional mutations in patients in an olfactory receptor gene than in ATP1A3 . To quantitatively capture this difference , we derive a score , based on the combined ESP6500 dataset that assesses the degree to which genes have either more or less common functional variation than expected for the genome as a whole given the amount of presumably neutral variation they carry . We define the threshold dividing “common” and “rare” variants as ρ . We then define Y as the total number of common ( Minor Allele Frequency [MAF]>ρ ) missense and “truncating” SNVs ( including splice and nonsense variants ) and X as the total number of protein-coding variants ( including synonymous variants , regardless of frequency in the population ) observed within a gene . We then regress Y on X ( Figure 1 ) and take the studentized residual as the Residual Variation Intolerance Score ( RVIS ) . Thus , the raw residual is divided by an estimate of its standard deviation and accounts for differences in variability that come with differing mutational burdens . The RVIS then provides a measure of the departure from the ( genome-wide ) average number of common functional mutations found in genes with a similar amount of mutational burden . When S = 0 , the gene has the average number of common functional variants given its total mutational burden; when S<0 , the gene has less common functional variation than predicted; when S>0 , it has more . Although multiple population genetic forces could influence the RVIS value of a gene , negative scores are likely to often reflect purifying selection , whereas positive scores are likely to reflect either the absence of purifying selection , the presence of some form of balanced or positive selection , or both . Scores for the 16 , 956 assessed genes are available in Dataset S2 , and a histogram of the distribution of S is available in Figure S1 . Here , we have set ρ = 0 . 1% MAF in the combined ESP6500 population . However , we also explored the behaviour of the score for ρ of 0 . 01% and 1% , and found both of these to be strongly correlated with ρ = 0 . 1% ( Pearson's r = 0 . 849 and Pearson's r = 0 . 813 , respectively ) ( Figure S2 ) . To facilitate interpretation , we also present the RVIS values as percentiles that reflect the relative rank of the genes , with the lowest scores being the most intolerant genes . The residual variation intolerance score is derived using the combined European American ( EA ) and African American ( AA ) data . Detailed studies of the EA and AA data , within the exome sequencing project ( ESP ) , have been published elsewhere [16] . Here , we show that there is a strong correlation between RVIS values based on the combined population compared to scores based on either the EA samples or AA samples: Pearson's r = 0 . 86 and Pearson's r = 0 . 91 , respectively ( Figure S2 ) . To address whether the RVIS is a predictor of “common” mutations and mutational burden , we also compared a score derived from the EA polymorphism data to the score derived from the AA polymorphism data . These two populations generate two independently derived RVISs for each gene . For the EA versus AA RVIS comparison , the Pearson's r correlation is 0 . 73 ( Figure S2 [G] ) . To assess whether the RVIS can discriminate genes that do and do not cause disease , we compared the RVIS values for genes causing different kinds of Mendelian diseases . Using keyword searches in OMIM , we extracted six gene-lists reflecting different contexts: OMIM genes , “haploinsufficiency , ” “dominant-negative , ” “de novo” disease causing , “recessive , ” and we indirectly derived a non-disease gene list ( Methods , Table 1 , and Dataset S1 ) . Using a logistic regression model , we found that genes causing Mendelian diseases have lower RVIS values than those that do not , with the strongest correlations observed for haploinsufficiency ( p = 1 . 6×10−31; β = −0 . 71 [95%CI −0 . 82–−0 . 59] ) and de novo disease-linked events ( p = 2 . 7×10−36; β = −0 . 57 [95%CI −0 . 65–−0 . 48] ) ( Table 1 , Figure 2 ) . ROC curves were generated to illustrate the capacity of the RVIS to predict the OMIM gene lists ( Figure 2B ) . We also investigated RVIS values for other gene lists of interest , including 91 genes that are human orthologs of “lethality” genes from the Mouse Genome Informatics ( MGI ) database [20] ( mouse knockouts associated with embryonic [MP:0008762] , prenatal , [MP:0002080] or perinatal [MP:0002081] lethality ) , 95 human orthologs of “seizure” genes ( mouse knockouts associated with seizures phenotype [MP:0002064] ) , a set of genes identified as essential in a recent publication by Georgi and colleagues ( 2013 ) [21] , and the 108 OMIM “haploinsufficiency” genes with de novo mutation variants reported ( Table 1 , Figure 3 and Dataset S1 ) . We then explored a derivative of the RVIS that is further informed , among the missense mutations , by PolyPhen-2 [2] qualitative predictions ( RVIS-PP2 ) . In summary , RVIS-PP2 considers the PolyPhen-2 “benign” classifications as “non-functional” variants ( Methods , Table 1 , Figure S3 ) . On average , based on the 6503 individuals in the NHLBI-ESP , applying this PolyPhen-2 filter resulted in a 33% reduction of missense variants in any given gene . The RVIS-PP2 values achieved a Pearson's correlation of 0 . 76 with the RVIS and remain significant across the OMIM disease groups ( Table 1 ) . In part , as the RVIS values reflect the selection pressures acting on genes , one obvious question is the extent to which the RVIS correlates with other measures of selection on genes . One phylogenetic approach is to compare non-synonymous substitutions per non-synonymous site ( dN ) to the synonymous substitutions per synonymous site ( dS ) , as reflected in ω ( aka Ka/Ks , dN/dS ) . To determine whether the RVIS correlates with ω , we compared a subset of the genome ( the orthologs between human and chimp for human chromosomes 1–5 ) to three estimates drawn from a separate study ( codeml [22] , LWL [23] , and NG [24]; estimates of ω were kindly provided by Dr . Chuanzhu Fan ) [25] ( Methods ) . Using a Pearson's r correlation , we find that the RVIS is not strongly correlated with these three estimates of ω: codeml ( r = 0 . 11 ) , LWL ( r = 0 . 02 ) , and NG ( r = 0 . 04 ) . Moreover , the capacity for the estimates of ω to predict OMIM disease genes is inferior to that of RVIS across all investigated gene lists ( Table S1 and Figure S4 ) . These analyses suggest that genes that are intolerant to genetic variation in the human population are more likely to cause some disorders than genes that either tolerate functional variation or have been under some form of selection promoting functional variation . It remains possible that some kinds of diseases show a different pattern from this overall one . To investigate this possibility we directly assess the gene-lists that make up the 22 disorder classes defined by Goh et al . ( 2007 ) [26] . For each disorder class , we assess the average RVIS values ( Table S2 ) . This analysis shows striking variation among types of disorders . Some closely follow the overall pattern of being influenced primarily by genes intolerant to functional variation , including “developmental” disorders with an average RVIS of −0 . 56 ( corresponding to 19 . 54 percentile ) , “cardiovascular” at −0 . 45 ( corresponding to 24 . 00 percentile ) , and “skeletal” at −0 . 36 ( corresponding to 28 . 64 percentile ) . At the other extreme there are some disorder classes where it is precisely the genes most enriched in common functional variation that are most likely to cause disease ( Table S2 ) . This contrast is illustrated starkly by comparing the two disorder classes with the highest and lowest average RVIS values: developmental diseases and immunological diseases , where we observe that the genes linked to the immunological disorder class have significantly greater tolerance to standing functional variation ( Figure 4 , p = 1 . 4×10−5 , 2-tail Mann-Whitney U test ) . In the former category , approximately half of all OMIM genes causing developmental disorders are found among the genes within the 25th percentile of intolerance and only 10% are found among genes above the 75th percentile . The pattern for immunological disorder OMIM genes is essentially the reverse: only 16% are found among the most intolerant 25th percentile , and 35% above the 75th percentile . One obvious question is whether genes that cause early onset diseases tend to have lower RVIS values than genes that cause later onset diseases . This is not easy to assess overall , especially given that there are sharp differences in the distributions of ages of onset of diseases in the different categories considered above , and also that not only age of onset but mode of inheritance will influence RVIS ( Figure 2 ) . However , to at least partially assess this question , we consider epileptic encephalopathies ( EE ) and amyotrophic lateral sclerosis ( ALS ) as two diseases with sharply different ages of onset . We then exclude all EE and ALS OMIM genes reporting only recessive forms ( Methods ) . Of the 10 EE genes linked to a dominant mutation model , the average RVIS = −1 . 41 ( corresponding to the 4 . 1% most intolerant genes ) . Of the 13 ALS genes linked to a dominant mutation model , the average RVIS = −0 . 29 ( corresponding to the 33 . 3% most intolerant genes ) . Thus , we have described two clearly genetic disorders , differing in age of onset , with an upwards shift in the RVIS corresponding to a later-onset . These analyses suggest that the use of the RVIS values should be tailored , wherever possible , to the RVIS values for genes already securely implicated in the phenotype under study . Focusing on the 25th percentile intolerant genes helped the Epi4K consortium successfully adopt the RVIS to identify epileptic encephalopathy genes within their de novo mutation data [15] . So far we have demonstrated the utility of the RVIS to discriminate between OMIM disease genes , and also the disease-causing genes specific to various physiological systems . A recent Epi4K trio sequencing paper illustrated the value of the RVIS in interpreting the de novo mutation data from a cohort of sequenced epileptic encephalopathy trios [15] . Here , we show how the residual variation intolerance scores can facilitate the analysis of de novo mutations observed in patient genomes . We consider de novo mutations observed in patients with severe intellectual disability ( ID ) , epileptic encephalopathies ( EE ) , and autism spectrum disorders ( ASD ) , as well as in control individuals ( unaffected siblings that were sequenced across the studies ) [9]–[15] ( Table S3 ) . Focusing on the 4 , 264 genes in the most intolerant 25th percentile of RVIS values ( Figure S1 ) , we observe an increasing enrichment among intolerant genes for the more extreme mutations ( Figure 5 and Table S3 ) . Synonymous de novo mutations show no enrichment for intolerant genes in any of the datasets ( Figure 5 ) . Taking the pooled synonymous data across all cohorts ( n = 417 synonymous de novo mutations ) and correcting for the four tests performed , we observe that the functional mutations ( missense and likely gene disrupting [LGD] ) in the severe ID cohort are significantly enriched for more intolerant genes ( p = 1×10−4 , 2-tail Mann-Whitney U test ) . Similarly , comparing the EE and ASD cohorts reflect enrichment of likely functional de novo mutations preferentially occurring among the most intolerant genes ( p = 6 . 8×10−3 and p = 1 . 3×10−2 , respectively ) ( Methods and Table S3 ) . We observe no significance among the functional de novo mutations within the control samples , p = 0 . 12 , 2-tail Mann-Whitney U test . Thus , the excess of functional de novo mutations observed in intolerant genes among the cohorts ascertained for disease is difficult to explain unless some of those de novo mutations actually increase risk of disease . The above analyses suggest that gene-level information reflected in the RVIS values can help discriminate between genes that do and do not cause disease . Given the well-established literature that prioritizes variants for their likely pathogenicity , a natural question arises as to whether integrating gene- and variant-level information can improve our ability to pinpoint causal mutations . As the simplest possible illustration of an integrated scheme , we consider two-dimensional ( 2D ) analyses that use the RVIS percentiles for genes ( y-axis ) and Polyphen-2 quantitative scores for missense mutations ( x-axis ) . We then analysed missense de novo mutations observed in the ID , EE , and ASD studies referenced earlier [9]–[15] . We found that , compared to those of controls , de novo mutations seen in the exomes of patients showed a striking concentration of density among the most damaging region of the 2D space ( Figure 6 [A–D] ) . A simple interpretation of these data is that while in the general population de novo mutations can occur in intolerant genes , and putatively “damaging” de novo mutations can occur in the exome , it is much less common for damaging mutations to occur in the most intolerant genes , unless those mutations are contributing to disease . In particular , concentrating only on the lower right-hand-side ( y< = 0 . 25 , x> = 0 . 95 ) , we found that the severe ID ( Figure 6B ) and EE ( Figure 6C ) missense de novo mutations had a significant excess p = 3 . 9×10−7 and p = 5 . 1×10−6 , respectively , compared to control exomes ( Figure 6A ) , and significant , but less enriched , for ASD missense de novo mutations ( p = 1 . 2×10−3 ) ( Figure 6D and Dataset S3 ) .
The residual variation intolerance score has obvious implications for human disease gene discovery . Of particular relevance is quantifying gene intolerance to functional mutations , genome-wide . Qualitatively , at least for some categories of disease , the genes most likely to influence disease are those that are the most intolerant of functional variation in the human population . More generally , ranking genes based on their RVIS values will clearly help in developing more formal quantitative frameworks that assign weights to genes based on RVIS or elaborations of RVIS . Several directions for future research could lead to improved gene-based intolerance scoring systems . As both the amount of sequence data and our knowledge of different functional domains of proteins increase , intolerance scoring systems can be developed that subdivide genes based on protein domains as opposed to single gene units . Such approaches could be informative , as certain regions of the gene could be much more constrained than others . Another future direction could be to leverage information from the entire site frequency spectrum ( SFS ) of mutations within a gene , instead of focusing on functional variation above a given frequency threshold . A gene-based score that incorporated the shifts in the SFS between functional and non-functional variants could produce a more sensitive discriminator of gene intolerance . To better discriminate the putatively non-functional from the functional missense mutations , yet another future direction could be to incorporate variant-level information in the form of conservation scores ( e . g . GERP++ ) [1] or in silico protein-damaging characterizing tools ( e . g . PolyPhen-2 [2] or SIFT [4] ) , as we briefly explored in this paper with the RVIS-PP2 . A slightly different approach would be to leverage from both a gene-level ( RVIS ) and a variant-level ( e . g . , PolyPhen-2 ) score in prioritizing individual mutations . Initial data ( Figure 6 ) indicate that this approach is particularly promising . Importantly , we have shown that to prioritize causal variants , incorporating both gene- and variant-level information has a demonstrated ability to improve our interpretation of personal genomes .
We first determine exactly what portion of the whole genome real estate any given gene covers in the ESP6500 database . This step required three parameters: Coding-sequence source: We adopt the CCDS public transcripts as our coding-sequence source data ( CCDS Release 9 , Assembly GRCh37 . p5 ) , further extending exonic positions by two base pairs , either side of an exon , to permit inclusion of putative splice acceptor and donor sites . For HGNC genes with multiple CCDS transcripts , we merge all transcripts of that gene into a single CCDS boundary . This allows assessment of the overall possible functional burden , correcting for variant annotations based on multiple public CCDS transcripts of HGNC genes . EVS Ethnicity: The ESP6500 database provides information for variants based on European American ( EA ) , African American ( AA ) , or combined ( ALL ) . For assessing gene intolerance to standing functional variation we adopt the combined ( ALL ) data . But further compare those results to the EA and AA data ( Figure S2 ) . Minimum Average Coverage: We adopt a minimum average coverage of at least 10-fold for any given CCDS site in the ESP6500 dataset for that site to contribute to assessment of intolerance . With the above three parameters we extracted data from ESP6500 for each HGNC gene with at least a single public CCDS transcript , including the number of possible sites within the CCDS after the splice acceptor and donor adjustment . We then determined how many of those CCDS defined sites for the HGNC gene had at least 10-fold coverage within the ESP6500 database . Of the 18 , 474 gene ids available in CCDS Release 9 , 1 , 518 ( 8 . 2% ) of genes were determine un-assessable due to having either less than 70% of the possible CCDS covered with at least 10-fold coverage in the ESP6500 database , or , for not having a “public” transcript within CCDS Release 9 . This resulted in 16 , 956 assessable HGNC genes . We only consider ESP6500 single nucleotide variants ( SNV ) with a “PASS” filter status , as described on the Exome Variant Server ( http://evs . gs . washington . edu/EVS/HelpDescriptions . jsp ? tab=tabs-1#FilterStatus; last accessed 12th December 2012 ) . Variant Function: The coding variant annotations considered for CCDS defined sites include: “missense” , “coding-synonymous” , “stop-gained” , “missense-near-splice” , “coding-synonymous-near-splice” , “stop-lost” , “splice-5” , “splice-3” , “stop-gained-near-splice” , and “stop-lost-near-splice” , as provided by the Exome Sequencing Project , and described in Tennessen et al 2012 . Of these variant annotations , we consider “missense” , “stop-gained” , “missense-near-splice” , “stop-lost” , “splice-5” , “splice-3” , “stop-gained-near-splice” , and “stop-lost-near-splice” as putatively “functional” variant annotations , while we considered “coding-synonymous” and “coding-synonymous-near-splice” as putatively “non-functional” variants . Minor Allele Frequency: We rely on the combined EA and AA cohorts , and thus rely on the ESP6500 “All” component of column “MAFinPercent ( EA/AA/All ) ” for the minor allele frequency of any given CCDS variant . For the primary analysis we consider the MAF cut-off at 0 . 1% frequency in the combined population . However , we further considered what effect on score alternating MAF cut-offs might have to better understand the residual variation intolerance scores' behaviour across frequency spectrum cut-offs of 0 . 01% and 1% , Figure S2 . To explore alternative genome-wide scoring that leverages from additional variant-level data we informed the RVIS score with the inclusion of PolyPhen-2 in silico predictions , as annotated in the NHLBI-ESP . We considered PolyPhen-2 “benign” qualitative assessments as “non-functional” , and PolyPhen-2 “probably , possibly , and unknown effects” as “functional” . Then , as before , we defined the threshold dividing “common” and “rare” as 0 . 1% minor allele frequency ( MAF ) . We defined Y as the total number of common , MAF>ρ “functional” missense and “truncating” SNVs ( including splice and nonsense ) and let X be the total number of variants ( including synonymous and “non-functional” missense mutations , regardless of frequency in the population ) observed within a gene . We regressed Y on X and took the studentized residual as the score ( S ) , as was described for the RVIS . In this manuscript , we refer to this revised RVIS score as the RVIS-PP2 . The Pearson's r correlation comparing the RVIS and the RVIS-PP2 was 0 . 76 [95% CI 0 . 75–0 . 77] . Results of the correlation between the RVIS-PP2 and OMIM disease genes are presented in Table 1 . As a primary assessment of score behaviour , we determine how well the scores predict known gene-lists from six different contexts , extracted from the OMIM database ( accessed 3rd December 2012 ) : OMIM disease genes , “recessive” , “haploinsufficiency” , “dominant-negative” , “de novo” disease-causing , and indirectly derive an OMIM “non-disease” gene list . For the five disease gene lists we filter only for gene entries that are annotated with a ( * ) indicating genes with known sequence and ( # ) indicating that a phenotype description and molecular basis is known . Moreover , we restrict it to records with “Allelic variants” and a “Gene Map Locus” . For the “recessive” ( n = 881 genes ) , “haploinsufficiency/haploinsufficient” ( n = 251 genes ) , “dominant negative” ( n = 387 genes ) and “de novo” ( n = 507 genes ) lists , we adopted those keywords , understanding that pulling out by keyword will identify some instances where the keyword is used for one reason or another even though the gene in question does not follow the indicated genetic model . We directly estimate this misclassification rate by inspecting a random subset of 30 genes from each of the OMIM categories and found it varied from a zero misclassification rate to a maximum of 30% . For the “haploinsufficiency/haploinsufficient” list we did manually curate each event to restrict to events with a confident haploinsufficient relationship ( n = 202 genes ) ( lists are available in Dataset S1 ) . For the OMIM disease gene list ( n = 2 , 329 ) we did a universal capture of all genes linked to disease , excluding genes linked to disorders with the following criteria: “resistance” , “cancer” , “somatic” , “susceptibility” , “carcinoma” and “tumor” . We further refined that list to only genes without the following annotations: braces “{” reflecting mutations contributing to susceptibility to multifactorial or infectious diseases , brackets “[]” reflecting genes linked to non-disease traits and question mark “ ? ” indicating an unconfirmed or possibly spurious mapping . We found that 56 . 5% of the genes from the OMIM disease gene list overlap with at least one of the four additional OMIM contexts , described earlier . Moreover , we observe that 5 . 3% of OMIM recessive genes were also annotated to OMIM haploinsufficiency , while 61 . 7% of OMIM haploinsufficiency genes overlapped with the “de novo” gene list ( Dataset S1 ) . The OMIM non-disease gene list ( n = 14 , 712 genes ) is derived by excluding , from the list of 16 , 956 HGNC assessable genes , any genes overlapping with at least one of the five described OMIM disease gene lists . To compare the RVIS to measures of omega ( ω ) , we consider HGNC genes in the subset of the human genome ( chromosomes 1–5 ) that have been derived and kindly provided by Dr . Chuanzhu Fan [25] . Dr . Chuanzhu Fan and colleagues calculated Ka/Ks for the orthologs between human and chimps for chromosomes 1–5 , using codeml [22] , NG [24] , and LWL [23] . For our comparisons , we relied on the subset of 2 , 963 genes across chromosomes 1–5 , where a score was available for all four scoring systems: RVIS , codeml , LWL , and NG . Where a gene had multiple transcripts , we considered the average Ka/Ks across those transcripts for each omega scoring system . Across these 2 , 963 genes , the highest correlation between the four scores was found for the pair-wise comparison between LWW and NG ( Pearson's r = 0 . 82 ) , and the second highest was a Pearson's r of 0 . 11 for RVIS and codeml . Thus , it is clear that there is low correlation between the RVIS score and these ratios of Ka/Ks . To address the question as to whether the Ka/Ks scores were better correlated to OMIM disease gene lists , we directly compared all four scores to the subset of gene annotations for the 2 , 963 genes . We found that , across the OMIM disease gene lists , the AUC consistently remained higher for the RVIS ( Table S3 ) . Most notably the de novo specific haploinsufficiency list , using RVIS as the predictor , obtained an AUC of 0 . 76 [95% CI 0 . 66–0 . 87] , while , in comparison , the highest AUC among the three omega scores was for NG , AUC of 0 . 61 [95% CI 0 . 47–0 . 75] . The closest comparison between the RVIS score and the omega scores was for the All OMIM gene list , where the RVIS score obtained an AUC of 0 . 56 [95% CI 0 . 53–0 . 59] , compared to NG , AUC = 0 . 52 [95% CI 0 . 49–0 . 55] . ROC curves for each of the investigated lists are available ( Figure S4 ) . We assessed the sensitivity of the 0 . 1% Minor Allele Frequency ( MAF ) residual variation intolerance score in the combined ESP6500 population by comparing it to the European and African American subpopulations , and to varied thresholds of 0 . 01% and 1% MAF ( Figure S2 ) . First , we regenerated the scores based on altering the MAF cut-off in the combined cohort from ρ = 0 . 1% to ρ = 0 . 01% and subsequently , ρ = 1% . We then compared the residual variation intolerance scores under the alternative MAF thresholds to that obtained using the 0 . 1% MAF . We obtained Pearson's r correlation coefficients of 0 . 849 [95%CI 0 . 845–0 . 853] comparing 0 . 1% MAF and 0 . 01% MAF , and 0 . 813 [95%CI 0 . 808–0 . 818] for the comparison between 0 . 1% MAF and 1% MAF ( Figure S2 [A and B] ) . We then regenerated the residual variation intolerance scores for the 0 . 1% MAF threshold based on the two sub-populations comprising the European Americans ( EA ) and African Americans ( AA ) . In doing so , 124 ( 0 . 7% ) of the 16956 HGNC assessable genes were identified as un-assessable for having insufficient coverage in one of the two separate populations , and were omitted from these comparisons . We found that the combined residual variation intolerance score ( ALL ) obtained a Pearson's r correlation coefficient of 0 . 862 [95%CI 0 . 858–0 . 865] for the comparison with the EA , and 0 . 908 [95%CI 0 . 905–0 . 911] for the comparison between AA and the combined ( ALL ) cohort ( Figure S2 [C and D] ) . We then investigated the effects on the MAF comparison when stratifying by sub-population to eliminate the effect of sample size differences in the MAF comparisons previously performed on the combined cohort of EA and AA . Using a MAF comparison of 0 . 1% and 1% in each of the EA and AA sub-populations , we obtain a Pearson's r correlation coefficient of 0 . 836 [0 . 832–0 . 841] for the EA 1% versus EA 0 . 1% MAF thresholds , and 0 . 850 [0 . 846–0 . 855] for the AA 1% versus AA 0 . 1% MAF thresholds ( Figure S2 [E and F] ) . We could not do a similar comparison for the 0 . 1% versus 0 . 01% MAF threshold in the sub-populations due to resolution limitations at such a low frequency , but given the current evidence from the comparisons we are encouraged that it will remain high . Finally , we showed that , while there is minor fluctuation in the curves , the signals did not differ when stratifying to the EA or AA sub-populations for the capacity to associate with OMIM disease genes ( Figure S5 ) . Likewise , the overall signals did not differ when adjusting ρ to 0 . 01% or 1 . 0% MAF for the capacity to associate with OMIM disease genes ( Figure S6 ) . The slight dip in performance for the 0 . 01% MAF is likely a result of the reduced resolution to sufficiently assess variants at that frequency level among a cohort of approximate 6503 combined samples . We found no correlation , Pearson's r of 0 . 005 [95%CI −0 . 010–0 . 020] , between the RVIS ( 0 . 1% MAF , combined population ) to ( X ) the number of variants observed in the corresponding gene . This is consistent with the expectation that the raw residuals and X are independent by construction . Furthermore , there was a very weak correlation , Pearson's r correlation of −0 . 099 [95%CI −0 . 114–−0 . 084] , between the RVIS and the coverage-corrected gene size . We did not find strong correlation between RVIS and the percentage GC content of the gene ( www . ensembl . org/biomart/martview ) , Pearson's r of −0 . 03 . Thus , it is clear that the information captured by the RVIS is not systematically biased by the number of variants in a gene , gene size , or the percentage GC content of the gene . In addition to the primary OMIM gene lists , we assessed the behaviour of the residual variation intolerance score within four alternatively derived lists of interest . Two lists were derived from the Mouse Genome Informatics ( MGI ) database ( last accessed 3rd December 2012 , http://www . informatics . jax . org/ ) , and a third was the combination of overlapping entries between OMIM “haploinsufficient” and OMIM “de novo” lists ( n = 108 ) . The first MGI-derived list focused on “lethality” genes ( n = 91 ) , which represent human orthologs , with public CCDS transcript ( s ) , where mouse knockouts have resulted in embryonic [MP:0008762] , prenatal , [MP:0002080] or perinatal [MP:0002081] lethality . The second list focused on “seizure” genes ( n = 95 ) , which represent human orthologs , with public CCDS transcript ( s ) , where mouse knockouts have resulted in a phenotype with a seizure presentation ( MP:0002064 ) . Gene lists are available in Dataset S1 . While we do not expect all the mouse knockout “lethality” and “seizure” genes to have identical consequence in humans , they are comparable proxies that are expected to be enriched for genes that when disrupted could have comparable phenotypes . A fourth list comprised of genes considered “essential” in a recent paper by Georgi et al . ( 2013 ) [21] . Of the 2 , 472 “essential” genes , 2 , 288 ( 92 . 6% ) had an available RVIS score . The remaining 7 . 4% of “essential” genes were unavailable due to having either less than 70% of the gene assessed within the NHLBI-ESP , as described in earlier methods , or not matching a public CCDS Release 9 transcript . To determine the disorder classes that are most likely to be affected by mutations in intolerant genes , we rely on previously curated lists of OMIM genes categorised into the 22 disorder classes by Goh et al . 2007 as part of the human disease network diseasome mapping effort [26] . The disorder class annotations are published in Goh et al . ( 2007 ) “Supporting Information Table 1” . [http://www . pnas . org/content/suppl/2007/05/03/0701361104 . DC1/01361Table1 . pdf - last accessed 27th December 2012] . We filtered only for HGNC genes within the source list that were assigned an RVIS value . We summarized the RVIS within each of the 22 disorder classes ( Table S2 ) . To compare RVIS values in an early versus late-onset genetic disorder context , we took epileptic encephalopathy ( EE ) genes from OMIM to represent “early-onset”: ARX ( EIEE1 – OMIM# 308350 ) , CDKL5 ( EIEE2 – OMIM# 300672 ) , SLC25A22 ( EIEE3 – OMIM# 609304 ) , STXBP1 ( EIEE4 – OMIM# 612164 ) , SPTAN1 ( EIEE5 – OMIM# 613477 ) , SCN1A ( EIEE6 – OMIM# 607208 ) , KCNQ2 ( EIEE7 – OMIM# 613720 ) , ARHGEF9 ( EIEE8 – OMIM# 300607 ) , PCDH19 ( EIEE9 – OMIM# 300088 ) , PNKP ( EIEE10 – OMIM# 613402 ) , SCN2A ( EIEE11 – OMIM# 613721 ) , PLCB1 ( EIEE12 – OMIM# 613722 ) , SCN8A ( EIEE13 – OMIM# 614558 ) , KCNT1 ( EIEE14 – OMIM# 614959 ) , MAPK10 ( LGS EE – OMIM# 606369 ) . Of these 16 EE genes , ARX was not assigned an RVIS score because it was insufficiently covered ( less than 70% of gene ) in the NHLBI-ESP ( Methods ) . Of the remaining 15 genes , ST3GAL3 , ARHGEF9 , SLC25A22 , PNKP , and PLCB1 lacked OMIM annotation for a dominant model . The genes considered for amyotrophic lateral sclerosis ( ALS ) , a “late-onset” severe neuronal disorder , were similarly extracted from OMIM: SOD1 ( ALS1 – OMIM# 105400 ) , ALS2 ( ALS2 – OMIM# 205100 ) , SETX ( ALS4 – OMIM# 602433 ) , FUS ( ALS6 – OMIM# 608030 ) , VAPB ( ALS8 – OMIM# 608627 ) , ANG ( ALS9 – OMIM# 611895 ) , TARDBP ( ALS10 – OMIM# 612069 ) , FIG4 ( ALS11 – OMIM# 612577 ) , OPTN ( ALS12 – OMIM# 613435 ) , VCP ( ALS14 – OMIM# 613954 ) , UBQLN2 ( ALS15 – OMIM# 300857 ) , SIGMAR1 ( ALS16 – OMIM# 614373 ) , CHMP2B ( ALS17 – OMIM# 614696 ) , PFN1 ( ALS18 – OMIM# 614808 ) , C9orf72 ( ALS – OMIM# 105550 ) . Of the 15 ALS genes , ALS2 and SIGMAR1 lacked OMIM annotation for a dominant model . OMIM susceptibility genes “{” were not considered , and only genes with reported causal genetic variants were eligible . Using a 25th percentile intolerance threshold to define the quarter of genes , genome-wide , that are most intolerant , we observed an increased enrichment of de novo mutations in the disease cohorts for the more damaging mutation types ( Figure 5 , Table S3 ) . Larger numbers of sequenced trios among these groups will facilitate improved interpretation of the enrichment for de novo mutations in intolerant genes among children affected by neurological/developmental disorders . Limitations interpreting these data include that 6 . 1% of the de novo mutations reported from the autism studies arose from multiplex families . Moreover , there is literature supporting overlaps between autism , EE , and severe ID; however , the exact percentage of the autism samples sequenced across the four ASD studies that had severe ID , EE , or both , were not readily available . To illustrate constructing a multidimensional prioritizing scheme for mutations we first collect all the publically available de novo mutations published across the autism , severe ID , epileptic encephalopathies , and control data from recently published papers [9]–[15] . We collectively annotated all de novo mutations to extract the de novo missense mutations using ensembl variant effect predictor v2 . 6 ( Ve ! P ) . Only mutations reported in CCDS transcripts [17] were considered . Restricting to missense CCDS mutations , for each de novo mutation we consider the most damaging PolyPhen-2 CCDS annotation . As the most likely de novo mutation genetic model is a single causal de novo mutation , for samples with multiple missense de novo mutations , we used the single most damaging de novo based on the lowest RVIS value ( i . e . , the most intolerant gene affected ) . Finally , we split the remaining pooled de novo missense mutations into the four groups: Control ( Figure 6A ) , Severe ID ( Figure 6B ) , Epileptic Encephalopathy ( Figure 6C ) , and Autism ( Figure 6D ) . We plotted each of the de novo missense mutations in the 2D space ( x-axis = PolyPhen-2 quantitative score; y-axis = Residual Variation Intolerance Score percentile ) . We considered the high-interest region “hot zone” to correspond to highly-predicted “functionally damaging” PolyPhen-2 missense mutations ( x≥0 . 95 ) , and RVIS within the lowest 25% of genes ( y≤0 . 25 ) ( Figures 6 [A–D] ) . We list the de novo mutations within the high-interest region , for each cohort , in Dataset S3 . While other elaborations of this multidimensional approach are possible , including higher dimensions that incorporate additional variant-level quantitative scores , such as SIFT , GERP++ , MAPP , etc . , here we aim to provide the simplest proof-of-concept for how this can be conceptualized , and ultimately adopted within relevant contexts . For simplicity we presented only 2D plots that considered the missense de novo mutations from the corresponding studies ( Figure 6 ) . However , it is certainly plausible to incorporate the information from other SNV effect types . For example , nonsense and essential splice site SNVs can be included in the assessment under a recoded PolyPhen-2 probabilistic damaging score of 1 , likewise , silent de novo mutations can be recoded with a probabilistic damaging score of 0 . With the inclusion of these additional SNV effect types , the preferential enrichment for each of the cohorts in this most damaging “hot zone” ( PolyPhen-2≥0 . 95 and RVIS≤0 . 25 ) for controls is 11 . 54% , compared to severe ID ( 48 . 96% , p = 9 . 4×10−14 , 2-tail Fisher's Exact test ) ; EE ( 30 . 86% , p = 5 . 9×10−7 , 2-tail Fisher's Exact test ) ; and ASD ( 23 . 25% , p = 1 . 9×10−5 , 2-tail Fisher's Exact test ) . | This work uses empirical single nucleotide variant data from the NHLBI Exome Sequencing Project to introduce a genome-wide scoring system that ranks human genes in terms of their intolerance to standing functional genetic variation in the human population . It is often inferred that genes carrying relatively fewer or relatively more common functional variants in healthy individuals may be judged respectively more or less likely to cause certain kinds of disease . We show that this intolerance score correlates remarkably well with genes already known to cause Mendelian diseases ( P<10−26 ) . Equally striking , however , are the differences in the relationship between standing genetic variation and disease causing genes for different disease types . Considering disorder classes defined by Goh et al ( 2007 ) human disease network , we show a nearly opposite pattern for genes linked to developmental disorders and those linked to immunological disorders , with the former being preferentially caused by genes that do not tolerate functional variation and the latter caused by genes with an excess of common functional variation . We conclude by showing that use of an intolerance ranking system can facilitate interpreting personal genomes and can facilitate identifying high impact mutations through the gene in which they occur . | [
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] | 2013 | Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes |
Humans , like other animals , are exposed to a continuous stream of signals , which are dynamic , multimodal , extended , and time varying in nature . This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions . To simplify this process , it's been suggested that the brain exploits statistical regularities in the stimulus space . Tests of this idea have largely been confined to unimodal signals and natural scenes . One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech . We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself . In essence , we do not have a clear understanding of the natural statistics of audiovisual speech . In the present study , we identified the following major statistical features of audiovisual speech . First , we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope . Second , we found the strongest correlation between the area of the mouth opening and vocal tract resonances . Third , we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range . Finally , we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms . We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled , multisensory event , whereby the outputs of the signaler are matched to the neural processes of the receiver .
Organisms are exposed to a continuous stream of signals which are dynamic , multimodal , extended , time varying in nature and typically characterized by sequences of inputs with particular time constants , durations , repetition rates , etc . [1] . This complex input space is transduced and sampled by the respective sensory systems and transmitted to the brains of the organisms where they modulate both neural activity and behavior over multiple time scales [2] . The shaping of brain activity and behavior by the environment has long been noted . Barlow [3] , for example , suggested that exploiting statistical regularities in the stimulus space may be evolutionarily adaptive . According to this approach , sensory processing would encode incoming sensory information in the most efficient form possible by exploiting the redundancies and correlation structure of the input . In essence , neural systems should be optimized to process the statistical structure of sensory signals that they encounter most often [4] . Often overlooked in these studies is recognition that an organism's experience of the world is profoundly multisensory , and it is likely that multiple overlapping and time-locked sensory systems enable it to perceive events and interact with the world [5] . One class of multisensory signals for which a careful input space characterization is unavailable is human speech . To be sure , there are numerous acoustic analyses of human speech , but there are relatively few for audiovisual speech . Multisensory speech is the primary mode of speech perception and it is not a capacity that is “piggybacked” on to auditory speech perception [6] . Human speech is bimodal , dynamic and forms a large proportion of the sensory signals encountered by humans in social contexts . Although a remarkable amount of research effort has been expended on the psychophysics and neural basis of audiovisual speech ( reviewed in [7] ) , a mechanistic account of audiovisual speech processing remains elusive . Several issues , such as the type of information extracted from each modality , static versus dynamic features , and the stage of integration for visual and auditory inputs are unresolved [6] , [7] . We do not know what features the brain has to actively extract and combine from the audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself . In essence , we do not have a clear understanding of the natural statistics of audiovisual speech . In this study , we examine the natural statistics of audiovisual speech in a framework similar to prior studies of natural visual scenes [8] and auditory soundscapes [9] . We analyzed speech from three different databases comprised of two different languages , meaningful versus nonsensical sentences , and staged versus conversational speech . Our analyses were conducted using methods developed to analyze neurophysiological data [10] . The analysis was motivated by recent neural theories of speech and audiovisual speech perception [11] , [12] . We identified the following major statistical features of audiovisual speech . First , we observed robust correlations and close temporal correspondence between the area of the mouth opening ( or inter-lip distance ) and the auditory envelope [13] . Second , we found that the strongest correlation between the area of the mouth opening and vocal acoustics occurred in two bands–below 1 kHz and between 2 and 3 kHz—the latter corresponding to formants ( or vocal tract resonances ) . Third , we observe that both facial movements and voice envelopes are temporally modulated in the 2–7 Hz frequency range , which overlaps with the timescale of the syllable [11] , [14] . This result was consistent across two languages and different speech contexts . Finally , we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms . We suggest that these statistical properties of audiovisual speech agrees are consistent with ideas suggesting that speech is inherently amodal [15] and are well matched to the properties of neural circuits involved in the processing of speech signals .
Our measurements were performed on two different audiovisual databases and an x-ray database of natural speech to ensure that our conclusions were not constrained by the methodology , language or the context and length of the speech segments . One audiovisual database was in British English , the other consisted of conversational French . The x-ray database was in American English . Descriptions of the databases and their features are provided below . All analyses were carried out in MATLAB ( Mathworks , Natick , USA ) using a mixture of custom built scripts and native functions . A Pearson correlation analysis was performed to relate the visual components of speech as indexed by the mouth area function/inter-lip distance and the auditory component , which could be either wideband or narrowband envelopes . As outlined in the previous section , the envelope was first computed and then re-sampled to 25 Hz to ensure that both auditory and visual signals shared the same sampling rate . Since such correlations can arise purely due to chance , we performed a shuffled correlation analysis on a subject-by-subject basis . Shuffled correlations were computed by correlating the mouth area function from one sentence with the auditory envelopes from all other sentences produced by that particular subject . This provided a rigorous test to ensure that the correlations were not due to arbitrary covariation between two positive signals . In the case of the Wisconsin x-ray database , speech lengths were not consistent across multiple passages . We therefore chose the function ( inter-lip distance or the envelope ) , which was smaller in length for the two modalities and performed subsequent shuffled correlations . Since we were interested in how audiovisual speech signals may relate structurally to neural signals , we measured them using the same approaches and spectral analysis techniques used in the neurophysiology literature [25]–[28] . We describe these here . To estimate temporal frequency modulations , we used multitaper Fourier methods ( Chronux toolbox , www . chronux . org ) to derive the spectra for the visual ( mouth area and the inter-lip distance ) and auditory signals . Since natural signals and sound typically display a 1/f spectrum , we fitted a function of the form Y = Af −α . Deviations from the 1/f fit suggest certain rhythmic aspects of the signal . The parameter A is a scaling parameter and alpha is the exponent . The analysis was performed on the logarithmic version of the same equation , i . e . logY = logA−α logf . This allowed for linear methods and avoided nonlinear fitting . Corresponding R2 values and parameters are reported . We used coherence to estimate the relationship between the visual and auditory signal as a function of modulation frequency . Coherence is a metric , which estimates the relationships between two time series as a function of frequency and does not have normalization problems [10] , [29] . We estimated coherence between auditory spectral frequency bands and the visual signals ( mouth area or inter-lip distance ) . The coherence was estimated as follows . For each sentence from each subject , we computed the coherence between the narrowband envelope and the mouth area function ( or the inter-lip distance ) . This provided , for each spectral frequency band , an estimate of the coherence between the auditory signal in that frequency band and the visual signal . Computing across all the spectral frequency bands gave us an estimate of the coherence for each sentence as a function of both spectral and temporal frequency . We then averaged this coherence estimate across all sentences for each subject and then subsequently across subjects . The time delay between the onset of mouth movements and onset of the voice component is the ‘time-to-voice’ . It is shaping up to be an important feature in determining audiovisual interactions in the neocortex [25] , [30] and an important part of neural theories of audiovisual speech [12] . All time-to-voice measurements were performed using the Wisconsin x-ray database because of its high 146 Hz sampling rate , which allowed us to get smooth lip motion trajectories . We only measured the inter-lip distance for labial consonants because the Wisconsin x-ray database provides only one dimension of lip motion . Additionally , measuring time-to-voice for labial consonants is useful since they are often used in experiments investigating audiovisual speech perception [31] , [32] . We estimated the time-to-voice for consonants either when they appeared at the beginning of a word such as “PROBLEM” , “BUT” , “FORM” , and “MAJOR” or produced in a vowel-consonant-vowel ( VCV ) context such as “ABA” , “APA” , “AMA” and “AFA” . Consonants typically involve a rapid closing of the mouth followed by an opening for subsequent sounds . This consistent close-to-open state allows us to identify , across subjects , the time for such a gesture . Inspecting the velocity profiles along with the inter-lip distance profile allows one to identify the dynamics of the mouth preceding the closing gesture of a consonant . Therefore , we fixed the onset of the sound as the first landmark and then looked backward in time until the point where the inter-lip distance started to decrease . At this point , the velocity of the lower and upper lips should increase because the mouth is beginning to close . We then identified the second point as the point at which the lip velocity again changes direction; this gives us the point at which the mouth is half closed and the closing gesture is slowing down . The time in milliseconds between either of these two points and the onset of the sound provided us with estimates of the time-to-voice for each speaker . We aligned the time courses of lip markers to the sound onset to plot individual gestures and averages .
Prior studies of audiovisual speech have suggested that the envelope of the auditory signal and the area of the mouth opening were correlated [13] , [33] . However , such measurements were done on only a few sentences . Therefore , as our first comprehensive analysis of the statistics of audiovisual speech , we correlated the mouth area function with the wideband auditory envelope for several sentences from 20 different subjects from the GRID corpus . Figure 2A plots the wideband auditory envelope and the mouth area function ( left panel ) and a scatter plot of the two variables ( right panel ) for a single sentence spoken by a single subject . The auditory envelope was highly correlated with the mouth area function for this sentence ( R = 0 . 742 , p<0 . 0001 ) . Similar results were observed across the population of subjects ( n = 20 ) . The bar graph in Figure 2B plots the average rank-ordered correlation coefficient between the mouth area function and the wideband auditory envelope for the 20 subjects in our population . The average correlation across subjects between mouth area function and wideband envelope was 0 . 45 ( SD = 0 . 1 ) and the correlations ranged from 0 . 31 to 0 . 65 . This suggests that there is a strong relationship between the temporal patterns of the mouth area function and the acoustic envelope , but significant intra- and inter-talker variability . To ensure that such correlations were not due to broad statistical relationships between the auditory and visual signals , we performed a shuffled correlation analysis . For a given subject , the mouth area function from one sentence was correlated with the auditory envelope of all the other sentences analyzed for that subject . This was repeated for all the sentences , providing a rigorous measure of the baseline level distribution of the correlations between auditory and visual information . Figure 2C plots the mean intact correlation versus the mean shuffled correlation . For every subject in our dataset , the mean intact correlation was significantly higher than the corresponding shuffled correlation ( t ( 19 ) = 24 . 03 , p<0 . 0001 ) . Mean intact and shuffled correlations for the population are shown in Figure 2D . To test whether these same auditory-visual correlations held true for longer speech streams ( beyond just single , canonical sentences ) , we analyzed the correlations between the inter-lip distance and the auditory envelope for long passages of prose ( durations spanning 16–25 seconds ) from the x-ray database . Figure 3A plots the envelope and the inter-lip distance from a single subject speaking a 20-second passage . The bottom panel of the same figure shows an expanded view of the 8–12 second time segment . Consistent with the results from the GRID corpus , clear correspondences were observed between the visual signal and the auditory envelope . Figure 3B shows the scatter plot of the envelope power and the inter-lip distance . The correlation between the inter-lip distance and the envelope for this twenty-second segment was 0 . 49 ( p<0 . 0001 ) . Figure 3C plots the rank-ordered intact correlation along with the shuffled correlation for the 15 subjects in this dataset . The average intact correlation across subjects was 0 . 278 ( SD = 0 . 05 ) and correlations ranged in magnitude from 0 . 197 to 0 . 375 . Although small , the importance of such an effect is revealed when contrasted relative to the magnitude of the average shuffled correlations . The average shuffled correlations for the subjects was −0 . 0067 ( SD = 0 . 0293 ) , ranging from −0 . 045 to 0 . 0265 . Again , a paired t test across subjects revealed that the intact correlations were significantly different from the shuffled correlations ( t ( 14 ) = 23 . 16 , p<0 . 001 ) . One potential confound is that such correlations may be unique to the English language and would not be observed in other languages . We therefore analyzed the audiovisual correlations for spontaneous speech from two different French speakers in the same way as the GRID corpus by correlating the extracted inter-lip distance with the wideband envelope of the auditory signal . Mean intact correlation between the visual and auditory signals for the two speakers in this database were 0 . 22 and 0 . 29 and mean shuffled correlations were 0 . 01 and −0 . 01 respectively . The mean intact correlations were again significantly greater than the shuffled correlations for both speakers ( speaker 1 , t ( 727 ) = 8 . 1 , p<0 . 0001 , speaker 2 , t ( 721 ) = 5 . 97 , p<0 . 0001 ) . Therefore , in three different databases and across two languages ( English and French ) , visual information as indexed by either inter-lip distance or mouth area function were robustly correlated with the wideband envelope of the auditory signal . Such correlations were robust for both small sentences as well as longer , more extended passages . Having established that the wideband envelope of the auditory signal was robustly correlated with the area of the mouth opening , we next tested whether such correlations between the envelope and the visual signal varied as a function of the spectral frequency band of the acoustic signal . Since formant frequencies are important for perception of speech sounds such as vowels , correlations between formants and visual signals could provide important cues to articulatory dynamics especially in noisy auditory conditions . Figure 4A shows the intact and shuffled correlations between the mouth area function and the narrowband envelope as a function of spectral frequency for two different subjects from the GRID corpus . Two regions of high correlations separated by a region of relatively low correlation were observed . The first peak spanned a range 300–800 Hz and the second , larger peak , spanned a range from 1 to 3 . 5 KHz . This second 1–3 . 5 KHz region overlaps with the F2–F3 region of formant space . Across the population of subjects , the same pattern was found with correlations again peaking at ∼600 Hz and in the 3 KHz range ( Figure 4B ) . In particular , the first peak was observed at 648 Hz ( SD = 182 Hz ) and the second peak at 2900 Hz ( SD = 398 Hz ) . We observed no correlations for frequencies greater than 5 KHz . The same analyses were conducted on the Wisconsin X-ray database . Figure 4C plots the intact and shuffled correlations between the inter-lip distance and the narrowband envelopes averaged across subjects . Two peaks were again observed in the correlation . The first peak was found at 720 Hz , and the second peak at 2 . 44 KHz . For the French audiovisual database , corresponding peaks in the correlation were observed at 530 Hz and 2 . 62 KHz . Thus , the results from all three databases suggest that the F1 region ( 300–800 Hz ) and the F2–F3 region ( ∼3 KHz ) are closely related to visual information . The next analysis we performed was to analyze the temporal structure of the auditory and visual components of speech . We performed this for two reasons . First , as speech inherently evolves in time , no set of natural statistics of speech would be complete without investigating its temporal dimensions . Second , research suggests that the time-varying dimensions of visual speech are probably one of the most important information bearing dimensions [34] and so far there has been only a limited description of the temporal properties of visual speech [35] . We therefore computed the frequency spectra of the envelopes of the auditory signal and the corresponding mouth area function to identify the temporal structure of the auditory envelope and the movement of the mouth . Figure 5A shows the average spectrum of the audio envelope and the mouth area function for a single subject across 25 sentences . As expected , both the mouth area function and the audio envelope possessed a 1/f spectrum . Prominent peaks were observed between 2–7 Hz for both the mouth area function and the auditory signal suggesting that these modulations could perhaps drive the correlations we observed between visual and auditory signals . Figure 5B plots the spectrum of the wideband envelope and the mouth area function for the same data shown in Figure 5A on a log-log plot . In a logarithmic form , a 1/f function should have a linear relationship between logarithm of the power and the logarithm of frequency . The spectra were well fitted by a function of the form log Y = logA−α logf ( R2 = 0 . 85 , p<0 . 0001 for the auditory envelope; R2 = 0 . 88 , p<0 . 0001 for the mouth area ) , but significant deviations from the 1/f fit were observed in the 2–7 Hz region for both the auditory envelope and the mouth area function . The alpha values were −2 . 1 for the auditory envelope and −2 . 62 for the mouth area function . We averaged these spectra across all 20 subjects for both the mouth area function and the sound and observed a similar pattern for the population . Figure 6A shows the average spectra across all 20 speakers in the GRID corpus for the audio envelope and the mouth area function . We also fitted the population average with the same function . The fitted value of α was −2 . 59 for the mouth area function and the corresponding value for the auditory envelope was −1 . 92 . Similar modulations were observed for the inter-lip distance and the auditory envelope from the x-ray database . Figure 6B plots the average spectrum of the auditory envelope and the inter-lip distance over all 15 subjects in the x-ray database . Again significant fits were observed ( R2 = 0 . 85 , p<0 . 0001 ) for both inter-lip distances and the envelope . Consistent with the results from the GRID corpus , modulations were found in the 2–7 Hz frequency range . The fitted value of α was −1 . 71 for the audio envelope and −2 . 88 for the inter-lip distance . Finally , Figure 6C shows similar spectra for the auditory envelope and the mouth area function for the two speakers in the French audiovisual database . Our results therefore suggest that some of the relevant information in audiovisual speech indexed by the envelope of the sound and the mouth area function is present in the 2–7 Hz temporal frequency band . Thus far , our analyses revealed three important results . First , auditory , and visual signals were highly correlated with each other . Second , such correlations were maximal in the F2–F3 region of formant space . Third , both visual signals and auditory signals were modulated in a 2–7 Hz frequency band . We next examined whether there was a relationship between the temporal modulations of audiovisual speech and the formant space . To do this , we adapted a coherence measure to analyze the relationship between the visual and auditory signals as follows . For each sentence , we took the narrowband envelopes and the mouth area function and then computed the coherence as a function of the temporal modulation frequency . This provided us with an estimate of the coherence for each spectral frequency band . We then averaged this coherence estimate across all sentences for each subject and then subsequently across subjects . Figure 7A ( left panel ) shows the coherence as a function of both spectral and temporal frequency for a single subject from the GRID corpus . A clear region spanning 2–7 Hz was observed for multiple spectral frequency regions . Consistent with our prior results , coherence was maximal for the F2–F3 regions and robust for the F1 region . The right panel shows similar coherence estimates from another subject . Figure 7B shows the average coherence across all 20 subjects in the GRID corpus database . Maximal coherence is observed in three regions one centered at ∼165 Hz , the second at ∼450 Hz and the third the F2–F3 formant region between 1–3 KHz . To illustrate this further , Figure 7C shows coherence as a function of temporal frequency for a selected narrowband in these different frequency band ranges — 161 Hz , 460 Hz and 2300 Hz in comparison to the 8800 Hz frequency band . Maximal relationships are observed in the 300 to 800 Hz frequency band and in the 1–3 KHz bands with slightly smaller coherence also present in the 165 Hz frequency bands . Such coherence appears maximal in the 2–7 Hz temporal frequency bands . Results from the two other databases closely followed the pattern found for the GRID corpus . Figure 7D shows the coherence between the auditory envelope and the inter-lip distance for the Wisconsin x-ray database . The coherence computations in this case were performed on 6-second segments shifted by 5-second increments to ensure that coherence was computed on similar time scales across all three databases . Again , coherence was maximal in the 2–7 Hz region . Figure 7E shows the average coherence between the auditory envelope and the mouth area function for the French spontaneous audiovisual speech database with very similar results . Since there were only two subjects , estimates of coherence are noisier , but are observed again in the 2–7 Hz temporal frequency region and for the two spectral frequency ranges of F1 and F2–F3 . Thus , auditory signals as represented by envelopes of different spectral frequencies and the visual signals as represented by the mouth area function or the inter-lip distance are closely related to one another and are together modulated in a 2–7 Hz temporal frequency region . One characteristic feature of speech is that the onset of visual signals usually precedes the onset of the sound . This can for example be seen in Figure 2 , where the area of the mouth opening increases a few hundred milliseconds prior to the onset of the sound . Other researchers have noted this and shown ( or suggested ) that the onset of visible articulatory motion in the face before the onset of the mouth may be important for the processing of speech [12] , [31] , [36] . Yet , there has been no systematic measurement of the delays between the onset of such facial motion and the onset of the voice . We therefore used the high temporal resolution of the Wisconsin x-ray database to identify the dynamics of lip movements before the onset of the voice . Since this database only provides 1-dimensional measures of the lip motion , we only analyzed the labial consonants such as ( /p/ , /b/ , /m/ ) and the labiodental consonant ( /f/ ) . Such bilabial consonants are often used in studies of audiovisual speech perception [31] , [37] . A description of the analysis we used to estimate the time-to-voice for bilabial consonants is as follows . The production of consonants involves a transition from an open to a closed mouth state . Our reference was the onset of the sound , which we estimated from the speech waveform . We then looked back in time until we found the point where the inter-lip distance began to decrease . This is the point at which the mouth begins to close . To estimate the point at which the inter-lip distance decreases one can use the derivative , which in our case is the velocity of the lips . This is marked by the point ( 2 ) and a red dotted line in Figure 8A and marks the beginning of the closing gesture . We next identified the point at which the mouth is half open because eye movement data suggest that both monkeys and humans fixate on the mouth at the onset of mouth movements , not necessarily when it is fully open [38] , [39] . At the half-open point , the lips are no longer accelerating towards one another , and at this point , there should be a reversal in the velocity of the lips . This is marked by the point ( 1 ) and a red dot on the inter-lip distance on the lower lip velocity profile ( Figure 8A ) . The time between the “half-open” point and the onset of the sound provides an estimate of the time-to-voice . Figure 8A plots the velocities of the upper and lower lip along with the inter-lip distance and sound for the word “PROBLEM” which starts with the bilabial consonant /p/ . As the velocity profiles reveal , the upper and lower lip seem to have very similar kinematics . As is characteristic of bilabial plosives , the mouth opens and rapidly closes until just before the release of the sound . To estimate the time-to-voice ( the delay between the onset of the mouth movement and the sound ) , we took the point of zero ( marked by ‘2’ ) in Figure 8A ) and maximum lower lip velocity ( marked by ‘1’ ) as two landmarks . As the dotted lines show , the zero velocity point corresponds to the point where the mouth is maximally open before beginning the closing motif and the maximum velocity point corresponds to the half “maximum” of this closing action . The time between each of these points and the time of sound onset provides an estimate of the “time-to-voice” . In this particular example , the estimated time-to-voice at the half-open point was 179 milliseconds . Figure 8B shows the mean subtracted inter-lip distance for the word PROBLEM aligned to the onset of the sound for the 32 speakers analyzed from the database . The grey lines denote the traces from individual subjects with the means subtracted . Figures 8C , 8D and 8E show corresponding plots for the words “MAJOR” , “BUT” and “FORM” which correspond to the two other bilabial plosives , /b/ and /m/ , and the labiodental , /f/ . Figure 8F shows the mean time-to-voice ( mean range = 137–240 ms ) for the four consonants across all speakers in the database ( mean±sd: /p/ , 195±60 ms ( n = 32 subjects ) ; /m/ , 137±64 ms ( n = 33 subjects ) ; /b/ , 205±69 ms ( n = 31 subjects ) ; /f/ , 240±38 ms ( n = 27 subjects ) ) . We next analyzed whether the time-to-voice varies with the context in which the consonant sounds are produced . That is , does the timing of lip movements vary when the mouth goes from open-to-close-to-open states ? We estimated the lip kinematics for the same set of consonants when they are produced in a vowel–consonant–vowel ( VCV ) syllable to identify how context modifies the lip closure dynamics . Figure 9A shows the velocity profiles for the lower and upper lips as a function of time , for the word APA produced by a single speaker . The time from half-open state is 100 ms for this example , which is different from the 179 ms we observed when the consonant /p/ was at the beginning of a word . Figures 9B shows the inter-lip distance traces for the words APA , AMA , ABA and AFA aligned to the sound onset . The mean range for these VCV syllables was 127 to 188 ms . For each VCV utterance independently: /p/ , 127±21 ms ( n = 20 subjects ) ; /m/ , 129±19 ms ( n = 22 subjects ) ; /b/ , 128±20 ms ( n = 20 subjects ) ; /f/ , 188±29 ms ( n = 18 subjects ) . These data suggest that , even in a different articulatory context , the time-to-voice variable is within a time window of 100–300 ms , though it is clear that movement is faster than when that consonant is at the beginning of a word .
The time course of opening and closing of the mouth is tightly locked to the acoustic envelope . Does close temporal correspondence between vision and audition offer any advantage for speech perception or is it just an incidental consequence of the speech production mechanism ? Psychophysical results suggest that the close temporal correspondence between faces and voices mediates at least some of the observed behavioral effects in audiovisual speech perception . For example , reversing or shifting the auditory components of sentences in time leads to a loss of multisensory advantages [40] and speech detection in noise is enhanced for matched compared to unmatched audiovisual sentences [33] . Indeed , research suggests that the time-varying dimensions of visual speech are probably one of the most important information bearing dimensions in speech [34] . For example , experiments with point light-displays , where the dynamic face is replaced by 12–20 points which mimic its movement , reveal that such purely dynamic information can enhance speech perception [41] and can even be used to induce McGurk effects ( albeit at a reduced level ) [42] . Similarly , spatial low pass filtering of the moving face while keeping temporal characteristics intact preserves the audiovisual gain for understanding spoken sentences [43] . Computationally , audiovisual coherence in speech has been exploited to separate out an acoustic speech signal from other acoustic signals [44] , and temporal synchrony is a key parameter used to segregate audiovisual sources , especially speech sources [45] . Macroscopic relationships between the state of the mouth and the amplitude of the sound have always been known . For example , sound amplitude is generally larger when the mouth is opened compared to when it is closed . However , beyond such simple relationships , our data show that the dynamics of the opening and closing of the mouth can be linked to specific frequency bands in the acoustic speech signal: they correlate well with spectral regions corresponding to formant frequencies . This observation is well supported by previous attempts to measure the correlation between visual and auditory components of speech . Grant and Seitz [33] , using methods similar to ours , reported robust correlations between the F2–F3 component and the mouth area for three sentences from a single speaker . This relationship between the opening and closing of the mouth and the spectral structure of the sound is somewhat surprising given that the production of speech requires a precise orchestration of the activity of several different components of the vocal tract anatomy . Besides the mouth and the lips , which form the most visible parts speech production process , complex adjustments of the pharynx , tongue and jaw are also required to actually produce a speech utterance [46] , [47] . It is important to note that our measurements only take into account one feature of the available visual signals from a dynamic face during speech . Specifically , we concentrated on the spreading motion of the mouth since we could measure that in a straightforward manner from the videos found in these databases and with the existing computer vision algorithms . However , facial regions beyond the mouth are also linked to the dynamics of the vocal tract and thus to speech acoustics . In a series of studies , Munhall , Vatikiotis-Bateson and collaborators used three dimensional kinematic tracking of facial and interior articulatory movements during speech and analyses of the corresponding acoustic signal to study the relationship between the two modalities [35] , [48]–[50] . High correlations were observed between the visual markers and the acoustic dimensions such as RMS amplitude and spectral structure . Even head movements can provide informative visual cues [49] , [50] . Indeed , the multitude of visual cues can provide information about not only what is being said , but also who is saying it [51] , [52] . Finally , we did not measure the dynamics of structures such as the tip of the tongue and its placement relative to the teeth [53]; this relationship can also be a visual marker of speech production . Further studies of the dynamics of these structures are needed to establish the role they may play in audiovisual speech and whether their dynamics are different from the ones we observed for the opening and closing of the mouth . That said , it should be noted that movements of the tongue–an articulator not necessarily coupled with the face–can be well-estimated just by using facial motion; it frequently displays the same temporal pattern as the mouth during speech [48] . What advantage does this close relationship between spectral structure and the opening and closing of the mouth provide ? On the perceiver's side , estimating the articulatory goals of the producer is probably useful for robust speech perception , since it allows one to make better inferences about the auditory signal . It is therefore fortuitous that the movements of the mouth can actually provide information about the spectral content of the auditory signal . This suggests that the brain can use the kinematics of the face to access the articulatory processes involved in the production of the acoustic signal and thereby refine its interpretation of the acoustic signal . One attractive hypothesis is that audiovisual speech perception is mediated , at least in part , by “peak listening” , which suggests that peaks in the visual signal ( e . g . mouth opening ) , provide cues to the peaks in the auditory signal ( e . g . formant peaks ) [40] . Our data suggest an extension of the peak listening theory: the mouth opening and closing may not only enhance perception at the peaks , but also track the auditory envelope more generally and is thus a robust , continuous cue . The importance of a continuous visual signal is suggested by the extensive psychophysical data on speech perception . These data point to the critical contribution of the acoustic envelope for intelligibility [23] , [24] , [54] , [55] . Further support comes from MEG/EEG studies which show that sentence comprehension correlates with how well the auditory cortex can track the acoustic envelope [56]–[58] . Close temporal correspondence between mouth area function and the acoustic envelope could provide an important boost during the information rich regions of the auditory signal [12] . Our statistical analyses reveal that temporal modulations in speech are about 2–7 Hz for both vision and audition . These measurements concur with prior reports of the temporal dynamics of speech . Ohala [59] measured the intervals between successive jaw movements during reading and estimated a rate of approximately 5 Hz as the underlying time scale for speech . Munhall and Vatikiotis-Bateson [35] used infrared emitting diodes to measure the movements of the face and estimated temporal modulations in visual speech to be in the range 0–10 Hz . This range of temporal modulations is important for audiovisual speech perception . For example , reduction of the frame rate of visual speech removes the audiovisual benefit [7] , [60] , [61] . Similarly , audiovisual advantages disappear for synthetic talkers speaking at rates faster than normal speech [40] . These studies along with the corresponding studies of auditory-only speech [24] , [54] , [55] suggest that slow temporal modulations in the 2–7 Hz range across modalities are indeed important for the perception of speech . Our analyses of the natural statistics of audiovisual speech show similar temporal modulations for the visual and auditory signals and reveal that the area of the mouth opening is closely related to formant frequencies . These results suggest that , at multiple levels , there are several redundant cues which all point to a description of the speech gesture . Taken together , they indicate that the speech signal is , to some degree , agnostic with regard to the precise modality in which it is perceived . These observations correspond to the tenets of an amodal , or modality-neutral , description of speech [15] , [53] . According to this viewpoint , visual and acoustic signals are shaped in the same way by a speech gesture from a signaler . Integration is therefore a consequence and property of the information in the input . By this it is meant that speech perception does not involve a series of complex transformations in each modality independently followed by a mechanism to bind this information . On the contrary , auditory and visual components share several redundancies and the process of speech perception involves the extraction of these redundancies . Our measured parameters are consistent with an amodal description of a speech gesture . As formant frequencies are predicted by area of the mouth opening and vice versa , they point to a modality-free description of the vocal tract . Similarly , both the acoustic envelope and the area of the mouth opening share a common temporal axis of 2–7 Hz . This common modulation has been previously suggested as a possible amodal metric of speech [15] , [53] . The suggestion that speech ( and other vocal communication systems ) could be processed in an amodal fashion is also attractive from a neurophysiological perspective [62] . Research suggests that most , if not all , neocortical areas are multisensory , including areas such as primary visual and auditory cortex , which have long been considered the foundation of cortical unimodal processing [63] . This strongly suggests that both auditory and visual components of speech are processed together at the earliest level possible in neural circuitry . Recent theories of speech suggest that the temporal modulations in speech are well matched to two key brain rhythms in the same frequency range [11] , [12] . In an expanded form , these theories state that the importance of the 2–7 Hz temporal frequency modulations may be due to it being in the same range as two very important neocortical rhythms — the delta ( 1–4 Hz ) and theta ( 4–8 Hz ) band . Studies have shown that oscillations at different amplitudes seem to be phase amplitude coupled in a hierarchical fashion [64] , [65] . For example , this means that amplitude of a 25–50 Hz ( putative “gamma” ) frequency band is modulated according to the phase of an underlying 5–9 Hz oscillation . The amplitude of this 5–9 Hz ( putative ‘theta’ ) oscillation is in turn controlled by the phase of a slower 1–2 Hz ( usually termed ‘delta’ ) oscillation . The activity in the lowest frequency bands in the auditory cortex are known to be entrained by rhythmic stimulation [64] . Our finding that audiovisual speech modulations in the 2–7 Hz range could be such a rhythmic input , entraining oscillations in the delta and theta bands . In other words , the activity of circuits in auditory cortex in the delta and theta band would be modulated by the slow modulations in audiovisual speech . In addition to this entrainment of slow brain rhythms to speech modulations , a provocative theory of speech , termed the “sampling-in-time” , suggests that auditory-only speech is preferentially processed in two time windows [11] . The first time window is in the timescale of a stressed syllable [14] or ∼150 to 300 ms and is mediated by the theta ( 3–8 Hz ) rhythm endogenous to the auditory cortex [64] , [66] . The second window is between 20–40 ms and thought to process formant transitions and probably mediated by the gamma rhythm . Evidence for this first , slower time window , is considerable . For example , observers who listen repeatedly to sentences where local segments of varying durations are time-reversed have few difficulties recognizing the content of the sentence , but only when the reversed segment durations are less than 100 ms [67] . When segment durations exceed 130 ms ( i . e . , disruptions that fall across the theta range ) , intelligibility drops to chance levels . Similarly , when speech rates are faster than 8 Hz , auditory cortical activity cannot track its modulations and there is a corresponding loss of intelligibility [56] . Finally , recent experiments reveal that the phase of theta band ( 3–8 Hz ) activity in auditory cortex actively tracks the intelligibility of sentences [58] . Our data suggest that the sampling-in-time theory could be extended to audiovisual speech . According to our data , visual speech could assist in the segmentation and processing of sounds especially at the syllabic level . Since mouth movements are in a similar syllabic time scale , they could be useful in assisting in such chunking , perhaps replacing a modality when the signal from the other modality is ambiguous . Open and close states of the mouth could provide important cues to start and end points of syllables . Vision might also lead to phase resetting in auditory cortex , perhaps at the start of each syllable , thereby priming the auditory cortex to be maximally sensitive to auditory inputs it encounters subsequently [12] , an issue which we explore in the next section . The last observation from our measured statistics is that the time-to-voice ( the delay between onset of mouth movements and the onset of the voice ) is between 100 and 300 milliseconds . This was true for words with bilabial consonants at the beginning , as well for VCV sounds where the consonant ( and thus the mouth closure ) is embedded . Therefore , under naturalistic conditions , structures mediating audiovisual speech perception must be tolerant to delays between visual and auditory signals . This observation is consistent with behavioral studies of audiovisual speech . Auditory speech must lag behind matching visual speech by greater than 250 milliseconds before any asynchrony is perceived by human subjects [68] . In stark contrast , detecting audio-visual asynchrony in artificial stimuli requires timing differences of as little as 20 milliseconds [69] . Similarly , the McGurk illusion is robust with vision leading audition by up to 240 milliseconds [32] , [70] . Thus , the time-to-voice range ( 100–300 ms ) that we find for audiovisual speech is consistent with the sensitivity of human subjects: if the auditory component lags the visual component with delays beyond this range , then speech perception is disrupted . Furthermore , this time interval is in accord with the electrophysiological constraints for auditory versus visual latencies in brain structures involved in speech perception [12] . Schroeder et al [12] hypothesized that the onset of mouth motion prior to the voice could also lead to more complex network dynamics such as phase resetting and that subsequent auditory signals falling on high excitability peaks of this reset oscillation will lead to the amplification of speech perception . This idea is borne out of a study of multisensory responses in the primary auditory cortex , where it was observed that somatosensory stimuli , which are generally ineffective at eliciting supra-threshold responses in auditory cortex , nevertheless reset the phase of ongoing oscillations in auditory cortex . Auditory stimuli which subsequently arrived at the low excitability phase of this reset oscillation were suppressed , while responses to stimuli which arrived at the high excitability phase were enhanced [26] . A similar pattern of results has also been reported for visual-auditory interactions [71] . Some evidence for this process also comes from an EEG study of audiovisual speech which showed a latency facilitation for audiovisual speech compared to auditory alone speech in auditory regions which depended on the degree to which visual signals predicted the auditory signal . [31] . However , it is unclear how continuous mouth motion seen during the production of a sentence would modify such processing in primary auditory cortex or how shifting eye movements of observers [38] , [39] , [72] would modify the phase resetting of ongoing oscillations [73] . Here is perhaps a more plausible scenario that builds on Schroeder et al . 's [12] framework for the visual amplification of speech but that incorporates eye movements and facial dynamics . A dynamic , vocalizing face is a complex sequence of sensory events , but one that elicits fairly stereotypical eye movements: we and other primates fixate on the eyes but then saccade to mouth when it moves before saccading back to the eyes [38] , [39] . Eye position influences single neuron and local field potential activity in multiple regions of auditory cortex [74] , [75] . Therefore , one possibility is that the eye fixations at the onset of mouth movements send a signal to the auditory cortex which resets the phase of an on-going oscillation . Such effects have been for example already seen in the primary visual cortex [76] . This proprioceptive signal thus primes the auditory cortex to amplify or suppress ( depending on the timing ) the neural response to a subsequent auditory signal originating from the mouth . Given that mouth movements precede the voiced components of both human [77]and monkey vocalizations [25] , [30] , the temporal order of visual to proprioceptive to auditory signals is consistent with this idea . This hypothesis is also supported ( though indirectly ) by the finding that the sign of face/voice integration in the auditory cortex and the STS is influenced by the timing of mouth movements relative to the onset of the voice [25] , [30] . One cautionary note is that our analysis of the time-to-voice focused only on bilabial consonants . Naturally , there is a far greater range of articulatory contexts which need to be explored . We focused on bilabial consonants because our X-ray database provided only a one-dimensional measure of mouth opening . Some prior work has attempted to measure the range of lip and jaw movements for a larger range of vowels and consonants , but only with a few subjects; estimates from this study were similar to ours [78] . Future work using the methods outlined in [78] or using the chroma key system [22] could be used to measure the time-to-voice for a larger range of articulatory contexts . Our analysis of the natural statistics of speech suggests that the statistical structure of audiovisual speech might be well adapted to the circuits which are involved in its processing . This fits well with current ideas about the emergent nature of communication and behavior — that is they emerge via the interactions between the brain , the body and the environment [4] , [5] , [79] . Key features of this ‘embodiment’ approach are three-fold . First , our experience is multimodal . This is certainly true for speech [6] . Second , multiple overlapping and time-locked sensory systems enable the agent to act in the world [5] . The revelation that most , if not all , of the neocortex speaks to this claim generally [63] , and the interactions between visual and auditory cortical regions which mediate face/voice integration speaks to vocal communication specifically [80] , [81] . Finally , sensory and motor systems will be coupled so that stable features of the brain , body , and/or environment can be exploited to simplify perception and action [2] , [4] , [5] . It is this last principle that the data presented in this current study address . The statistical structure of audiovisual speech is such that it seems well matched to the circuits involved in processing it . First , modulations in the 2–7 Hz temporal frequency region overlap with the temporal sensitivity of neurons in the primary auditory cortex and , to some extent , the oscillatory structure of cortical networks more generally [82] . Second , the relative delay between visual and auditory inputs seems well matched to the relative latencies and processing delays for the respective modalities thereby ensuring that visual speech can help in the efficient processing of auditory speech . | When we watch someone speak , how much work is our brain actually doing ? How much of this work is facilitated by the structure of speech itself ? Our work shows that not only are the visual and auditory components of speech tightly locked ( obviating the need for the brain to actively bind such information ) , this temporal coordination also has a distinct rhythm that is between 2 and 7 Hz . Furthermore , during speech production , the onset of the voice occurs with a delay of between 100 and 300 ms relative to the initial , visible movements of the mouth . These temporal parameters of audiovisual speech are intriguing because they match known properties of neuronal oscillations in the auditory cortex . Thus , given what we already know about the neural processing of speech , the natural features of audiovisual speech signals seem to be optimally structured for their interactions with ongoing brain rhythms in receivers . | [
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] | 2009 | The Natural Statistics of Audiovisual Speech |
Chikungunya virus ( CHIKV ) is responsible for acute febrile polyarthralgia and , in a proportion of cases , severe complications including chronic arthritis . CHIKV has spread recently in East Africa , South-West Indian Ocean , South-Asia and autochthonous cases have been reported in Europe . Although almost all patients are outpatients , medical investigations mainly focused on hospitalised patients . Here , we detail clinico-biological characteristics of Chikungunya ( CHIK ) outpatients in Reunion Island ( 2006 ) . 76 outpatients with febrile arthralgia diagnosed within less than 48 hours were included by general practitioners during the CuraChik clinical trial . CHIK was confirmed in 54 patients and excluded in 22 . A detailed clinical and biological follow-up was organised , that included analysis of viral intrahost diversity and telephone survey until day 300 . The evolution of acute CHIK included 2 stages: the ‘viral stage’ ( day 1–day 4 ) was associated with rapid decrease of viraemia and improvement of clinical presentation; the ‘convalescent stage’ ( day 5–day 14 ) was associated with no detectable viraemia but a slower clinical improvement . Women and elderly had a significantly higher number of arthralgia at inclusion and at day 300 . Based on the study clinico-biological dataset , scores for CHIK diagnosis in patients with recent febrile acute polyarthralgia were elaborated using arthralgia on hands and wrists , a minor or absent myalgia and the presence of lymphopenia ( <1G/L ) as major orientation criteria . Finally , we observed that CHIKV intra-host genetic diversity increased over time and that a higher viral amino-acid complexity at the acute stage was associated with increased number of arthralgia and intensity of sequelae at day 300 . This study provided a detailed picture of clinico-biological CHIK evolution at the acute phase of the disease , allowed the elaboration of scores to assist CHIK diagnosis and investigated for the first time the impact of viral intra-host genetic diversity on the disease course .
Chikungunya virus ( CHIKV ) is an arbovirus ( genus Alphavirus , family Togaviridae ) , transmitted by the bite of infected mosquitoes ( Aedes aegypti = Stegomya aegypti and Aedes albopictus = Stegomya albopicta ) , that causes Chikungunya fever ( CHIK ) , an acute febrile illness characterized by severe and often debilitating arthralgia [1] . CHIKV was first isolated in 1952 in East-Africa [2] . The name ‘Chikungunya’ refers to the stooped posture that develops in an individual as a result of arthritic symptoms and comes from the Bantu language of the Makonde people ( Tanzania and Mozambique ) [2] . Until recently , knowledge of CHIK clinical features was based on its first description in the 1970s [3] , [4] . Since 2005 , CHIK outbreaks of unprecedented magnitude have occurred in South-Asia and the Indian Ocean islands , including Reunion Island with an estimated 266 , 000 cases , accounting for roughly one third of the population [5] . In 2007 , a viraemic traveller from India introduced CHIKV into northern Italy , resulting in an outbreak with 292 suspected cases [6] . Thus , CHIK constitutes a serious threat numerous tropical and temperate areas due to the fact that Aedes albopictus is now widespread , notably in Southern-Europe [6] , [7] , and displays seasonal synchronicity within potential endemic areas [8] . Although almost all CHIK patients are outpatients , most clinical and laboratory investigations of CHIK focused on hospitalised patients ( i . e . mostly with severe presentations that represented a very small proportion of total infections ) [9] , [10] , [11] , [12] . Our attempts to describe the clinical and biological features of chikungunya acute disease took advantage of data collected during the CuraChik clinical trial , performed on Reunion Island during the 2006 Indian Ocean outbreak [13] . CuraChik provided a unique opportunity to collect detailed clinical and biological information from CHIKV infected patients with the most common forms of clinical presentation , recruited by general practitioners .
Amongst 76 patients included at D1 , the diagnosis of CHIKV was confirmed in 54 patients ( CHIKV+ve patients ) and excluded in 22 patients ( CHIKV−ve patients ) . Since the clinical and biological assessment at D1 was obtained prior to the beginning of the treatment , all CHIKV+ve patients could be used for analysis at the time of inclusion . By contrast , only the patients who received the placebo ( 27 CHIKV+ve and 13 patients CHIKV−ve , placebo group ) were used to describe the evolution of the disease . Lymphopenia was frequent at inclusion ( 94% of cases with a value <1 . 5 Giga per Litre ( G/L ) ; 79 . 6% with a value <1 G/L ) . Thrombocytopenia ( <150 G/L ) was noted in 24% of cases and neutropenia ( <2 . 5 G/L , but always >1 G/L ) in 33% of cases . Abnormal liver function ( ALT >45 International Unit per Litre ( IU/L ) and AST >35 IU/L ) was found in 14% and 28% respectively . C Reactive Protein ( CRP ) was >15 mg/L in 82% of cases , >50 mg/L in 33% and >100 mg/L in 12% ( Table 1 ) . There was no leukocytosis ( >10 G/L ) . Five patients had anaemia ( <12 g/dl ) , including one patient had sickle disease ( 8 g/dl ) . The average viral load at D1 was 1 . 2×109 ( 3 . 7×105–1 . 4×1010 , SD = 2 . 3×109 RNA copies/ml ) . In multivariate analysis , a lower lymphocytosis was associated with a shorter time between onset of symptoms and inclusion ( p = 0 . 053 , β = 0 . 249 , CI ( −0 . 03;0 . 502 ) ) and a higher viral load ( p<0 . 05 , β = −0 . 144 , CI ( −0 . 284;−0 . 004 ) ) . A higher viral load was associated with an increase of age ( p<0 . 05 , β = 0 . 024 , CI ( 0 . 001;0 . 047 ) ) and a decrease of delay of inclusion ( p<0 . 05 , β = −0 . 608 , CI ( −1 . 093;−0 . 124 ) ) .
The clinical presentation of CHIK at inclusion revealed a quite severe impact of the disease on quality of life , with more than half of the patients' scores <30/100 . It conformed with the canonical presentation previously reported in Reunion Island and in the recent Indian reports , which included fever and symmetrical poly-arthralgia [9] , [10] , [34] , [35] , [36] , [37] , [38] . However , this simplistic association ( fever+polyarthralgia ) seems to perform modestly for the specific diagnosis of CHIK: in a retrospective serologic survey of the CHIK outbreak in Mayotte Island [28] , the PPV was as low as 74% . In our study , despite the great recent clinical experience of general practitioners , the PPV was similar ( 71% ) . Looking into further details , it appears that arthralgia was most commonly observed in small joints ( i . e . , wrists , ankles , hands ) and knees , as reported from both in- and outpatients [9] , [10] , [29] , [34] , [39] . More precisely , this study highlighted the massive number of arthralgic joints ( 16 , on average ) and the specific importance of ( i ) small joint pain such as MCP/PIP or wrist and ( ii ) a minor or absent myalgia for the diagnosis of CHIK at the onset of the disease . This strongly suggests that a convenient diagnostic score may profitably guide the diagnosis of CHIK at the acute stage of the disease . We proposed a very simple and purely clinical score ( Figure 4 ) which reached 87% PPV in our population ( i . e . , outpatient 18 to 60 years old , examined before the second day of illness ) . For convenience , results were categorised as ‘probable’ , ‘possible’ and ‘not probable’ . Our data also highlighted the high level of viral load ( 1 . 2×109 RNA copies/ml on average ) at inclusion . It was slightly higher than in other reports [10] , [40] , possibly due in the current study , to the very short delay between onset of disease and inclusion . Viral load at D1 significantly increased with age but no relationship with clinical presentation or co-morbidity could be identified . In contrast with previous studies dedicated to hospitalised patients [9] , [41] , we did not identify a relationship between the level of CRP and transaminases , a reduction of the polymorphonuclear neutrophil level or other biological abnormalities , and the intensity and number of arthralgia or the quality of life at D1 . However , as previously reported from inpatients [10] , we observed that lymphopenia ( <1G/L ) was closely related to the level of viraemia . It constituted an important clue for the diagnosis of CHIK as illustrated by our clinico-biological score ( Figure 4 ) , which again , classified the patients as ‘probable’ , ‘possible’ or ‘not probable’ , as a function of the probability to be infected by CHIKV , based on clinical presentation and lymphocyte count . The PPV of this score reached 94% in our population , for a threshold of 0 . 579 . The most original input from the CuraChik protocol was the detailed information collected ( patient self-assessment from D1 to D14 , medical consultations ( D1 , D7 , D25 ) , biological analyses ( D1 , D6 , D16 ) ) , which altogether provided an accurate description of the evolution of patients during the acute stage of the disease . Deciphering these data indicated that the acute disease includes 2 distinct stages However , despite clinical improvement , it is probable that immune mechanisms are still involved [48] at this stage . The final outcome ( complete clinical recovery or persistent pain and chronic joint inflammation ) appears likely to depend upon a series of genetic , viral and immunologic factors that operate at the acute and convalescent stages . On Reunion Island [31] , [43] and India [39] but not Singapore [44] , late complications were associated with severe acute disease . Here , in agreement with the clinical pattern observed on Reunion Island , patients with a high number of arthralgic joints at disease onset reported more frequently persistent arthralgia at D300 . The early and convalescent immune response may be , in addition to putative yet uncharacterised viral factors , modulated by innate ( genetic ) and acquired factors . The latter certainly include age , which appears in many studies to be a major determinant of the clinical presentation and outcome . Here , we found that an increase of age was an independent risk factor for symptomatic illness at the time of disease onset ( number and intensivity of joint pains ) and at D300 ( number of cases with persistent arthralgia ) . At D300 , the patients who did not report recovery and who reported persistent arthralgia were significantly older . These results are consistent with studies on hospitalised patients and Indian report which reported that elderly patients more frequently presented with atypical feature or a severe course [10] , [49] , [50] with persistent arthralgia [31] , [39] , [43] , [49] , [50] , [51] . Genetic factors presumably trigger different immune responses which may account for the inter-individual and inter-ethnic variability of clinical presentation . Amongst them , gender is of specific interest . A single report mentioned a higher susceptibility of males , to CHIKV infection [52] but globally , previously published data suggest that symptomatic CHIK is more frequent in women [35] , [38] . On Reunion Island , women were over-represented based on reported cases [24] , [25] whereas cross sectional studies , based on representative groups of the population , found similar seroprevalence values in females and males [32] , [53] . In two other studies , at the late stage of the disease , female gender was associated with persistent arthralgia [41] or light cerebral disorder or fatigue [27] . In the current study , females were independently associated with a high number of painful joints at disease onset and at D300 . They also reported non-recovery more frequently at D300 . It is difficult at this stage to distinguish between gender-related specific clinical susceptibility and a different perception of the disease in males and females . The interplay between the immune response and viral evolution most probably constitutes an important issue for disease outcome . A non-primate animal model [42] showed that CHIKV could persist much longer than previously believed and such persistence may imply the existence of specific adapted variants , or at least depend on the kinetics of viral clearance by the immune system . To our knowledge , no study had previously described the intra-host genetic diversity of CHIKV in human samples . We observed that CHIKV was represented in serum by a variety of closely related genomes and that genetic diversity increased over time and was correlated with the decrease of viral load . This observation relied on the analysis of sequential serum samples from 2 patients , but was also supported by the analysis of other sera for which analysis of the intra-host genetic diversity of CHIKV was made available: the later the serum was sampled , the higher was the intra-host genetic diversity , based on percentage of mutant clones and average π aa and dN . These data are consistent with a mechanism in which acute infection produced an accumulation of mutations over time ( resulting in an increased intra-host genetic diversity ) , associated with a lower number of virions and , possibly , an increased potential for persistence . Interestingly , we found that a higher amino-acid complexity at the acute stage was associated with increased reporting of arthralgia and intensity of sequelae at D300 . This may indicate that the immunological processes associated with the initial viraemia decline or are partly circumvented , thus enhancing the opportunity for onset of virus persistence and long term clinical complications . In an experimental model , Coffey et al . previously observed that a CHIKV variant with high fidelity polymerase produced truncated viraemia and lower organ titres and suggested that reduced genetic diversity impacts negatively on virus fitness in both invertebrate and vertebrate hosts [54] . Moreover in the macaque model , long-term CHIKV infection was observed in joints , muscles , lymphoid organs , and liver which could explain the long-lasting CHIK symptoms observed in humans [42] . Altogether , these data are consistent with the hypothesis that CHIKV displays increased intra-host diversity which may be associated with prolonged viraemia , higher organ viral load and an increased risk of chronic disease . If this is the case , such mechanisms appear to be quite different from those previously observed in the case of dengue fever , where lower intra-host diversity has been associated with more severe cases [55] On the other hand , our results appear more closely related to previous reports on the relationship between intra-host genetic diversity , fitness and virulence in the examples of chronic infections by induced HIV or HCV [56] , [57] . Some additional studies are needed to further characterise the intra-host genetic diversity of CHIKV at the acute phase of the disease . Sequential analysis provided by New Generation Sequencing tools may provide a more accurate picture of this diversity and allow a powerful analysis of the relationship between the structure and evolution of intra-host viral genetic diversity and the clinical evolution of CHIKV infected patients . | The mosquito-transmitted chikungunya virus is responsible for acute febrile polyarthralgia and , in a proportion of cases , complications including chronic arthritis . Since 2005 , it has massively re-emerged in the Old World . Although the large majority of patients are outpatients , the most detailed studies have focused previously on hospitalised patients ( i . e . , severe cases ) . Here , we report the detailed clinico-biological characteristics of ‘standard’ clinical presentations in patients followed-up by general practitioners in Reunion Island ( 2006 ) during the CuraChik clinical trial . At the onset of the disease , two stages were observed: ( i ) a ‘viral stage’ during the first 4 days , associated with an acute febrile polyarthralgic syndrome and a subsequent rapid clinical improvement; the main clinico-biological characteristics during that period were used to elaborate supportive chikungunya diagnostic scores , ( ii ) a ‘convalescent stage’ ( days 5–14 ) with no detectable viraemia but a slower clinical improvement . Woman and elderly patients were found at risk for more symptomatic forms of the disease at both the acute and late stages ( day 300 ) and we observed that the viral intra-host genetic diversity increased over time and that a higher viral amino-acid complexity at the acute stage was associated with more symptomatic illness at the late stage of the disease . | [
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] | 2013 | Chikungunya Fever: A Clinical and Virological Investigation of Outpatients on Reunion Island, South-West Indian Ocean |
The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response . However , traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data . Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori , typically those with small , smooth , or sparse coefficients . Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality , a powerful form of prior information about receptive field structure . The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency , using an inference method known as empirical Bayes . We refer to our method as automatic locality determination ( ALD ) , and show that it can accurately recover various types of smooth , sparse , and localized receptive fields . We apply ALD to neural data from retinal ganglion cells and V1 simple cells , and find it achieves error rates several times lower than standard estimators . Thus , estimates of comparable accuracy can be achieved with substantially less data . Finally , we introduce a computationally efficient Markov Chain Monte Carlo ( MCMC ) algorithm for fully Bayesian inference under the ALD prior , yielding accurate Bayesian confidence intervals for small or noisy datasets .
A fundamental problem in systems neuroscience is to determine how sensory stimuli are functionally related to a neuron's response . A popular mathematical description of this encoding relationship is the “cascade” model , which consists of a linear filter followed by a noisy nonlinear spiking process . The linear stage in this model is commonly identified as the neuron's spatiotemporal receptive field , which we will refer to simply as the receptive field ( RF ) or “filter” . The RF describes how a neuron sums up its inputs across space and time . It can also be conceived as the spatiotemporal stimulus pattern that optimally drives the neuron to spike . A large body of literature in sensory neuroscience has addressed the problem of estimating a neuron's RF from its responses to a rapidly fluctuating stimulus , a problem known generally as “neural characterization” [1]–[17] . Here we focus on a highly simplified encoding model that describes neural responses in terms of a linear filter and additive Gaussian noise [5] , [11] , [18] . Although this model gives an imperfect description of real neural responses , the RF estimators that arise from it ( such as the spike-triggered average ) are consistent under a much larger class of models [7] , [19] , [20] . The maximum likelihood filter estimate under the linear-Gaussian model is the whitened spike-triggered average ( STA ) , also known as linear regression , reverse correlation , or the first-order Weiner kernel [1]–[3] . The STA has an extensive history in neuroscience and has been used to characterize RFs in a wide variety of areas , including retina [4] , [7] , [13] , [21] , [22] , lateral geniculate nucleus [23] , [24] , primary visual cortex [5] , [25] , and peripheral as well as central auditory brain areas [8] , [9] , [11] , [26]–[28] . The STA is often high-dimensional ( containing tens to hundreds of parameters ) and generally requires large amounts of data to converge . With naturalistic stimuli , the whitened STA is often corrupted by high-frequency noise because natural scenes contain little power at high frequencies . A common solution is to regularize the filter estimate by penalizing unlikely parameter settings , generally by biasing parameters towards zero ( also known as “shrinkage” ) . Statisticians have long known that biased estimators can achieve substantially lower error rates in high-dimensional inference problems [29] , [30] , and Bayesian methods formalize such biases in terms of a prior distribution over the parameter space . In neuroscience applications , priors for sparse ( having many zeros ) or smooth ( having small pairwise differences ) filter coefficients have been used to obtain substantially more accurate RF estimates [9] , [11] , [12] , [15] , [31] . However , neural receptive fields are more than simply sparse or smooth . They are localized in both spacetime and spatiotemporal frequency . This is a structured form of sparsity: RFs contain many zeros , but these zeros are not uniformly distributed across the filter . Rather , the zeros tend to occur outside some region of spacetime and , in the Fourier domain , outside some region of spatiotemporal frequency . Although this property of receptive fields is well-known [32] , [33] , it has not to our knowledge been previously exploited for receptive field inference . Here we introduce a family of priors that can flexibly encode locality . Our approach is to first estimate a localized prior from the data , and then find the maximum a posteriori ( MAP ) filter estimate under this prior . This general approach is known in statistics as parametric empirical Bayes [34] , [35] . Our method is directly inspired by previous empirical Bayes estimators designed to incorporate sparsity [36] and smoothness [11] . We show that locality can be an even more powerful source of prior information about neural receptive fields , and introduce a method for simultaneously inferring locality in two different bases , yielding filter estimates that are both sparse ( local in a spacetime basis ) and smooth ( local in a Fourier basis ) .
A typical neural characterization experiment involves rapidly presenting stimuli from some statistical ensemble and recording the neuron's response in discrete time bins . Let denote the ( vector ) stimulus and the neuron's ( scalar ) spike response at time bin . Here , is a vector of spacetime stimulus intensities over some preceding time window that affects the spike response at time bin . We will model the neuron's response as a linear function of the stimulus plus Gaussian noise: ( 1 ) where denotes the neuron's receptive field and is a sample of zero-mean , independent Gaussian noise with variance . This model is the simplest type of cascade encoding model ( depicted in Fig . 1 A ) , and plays an important role in the theory of neural encoding and decoding [5] , [11] , [17] , [28] , [37] , [38] . For a complete dataset with stimulus-response pairs , likelihood is given by ( 2 ) where is a column vector of neural responses and is the stimulus design matrix , with 'th row equal to . The maximum likelihood ( ML ) receptive field estimate is: ( 3 ) This estimate , also known as the whitened spike-triggered average , and is proportional to the ordinary spike-triggered average if the stimulus ensemble is uncorrelated , meaning . A major drawback of the maximum likelihood estimator is that it typically requires large amounts of data to converge , especially when is high-dimensional . This problem is exacerbated for correlated or naturalistic stimulus ensembles , because the high-frequency components of are not well constrained by the data . In the Bayesian framework , regularization is formalized in terms of a prior distribution , which tells us that we should bias our estimate of toward regions of parameter space that are more probable a priori . The posterior distribution , which captures the combination of likelihood and prior information , is given by Bayes' rule: ( 4 ) The most probable filter given the data and prior is known as the maximum a posteriori ( MAP ) estimator: ( 5 ) The log prior behaves as a “penalty” on the solution to an ordinary least-squares problem , forcing a tradeoff between minimizing the sum of squared prediction errors and maximizing . Biased estimators can achieve substantial improvements over the maximum likelihood , particularly for high-dimensional problems , without giving up desirable features such as consistency ( i . e . , converging to the correct value in the limit of infinite data ) . However , the important question arises: how should one select a prior distribution ? ( Choosing the wrong prior can certainly lead to a worse estimate ! ) One common method is to set the prior ( or “penalty” ) by cross-validation . This involves dividing the data into a “training” and “test” set , and selecting the prior for which ( estimated on the training set ) achieves maximal performance on the test set . However , this approach is computationally expensive and may be intractable for a prior with multiple hyperparameters . Empirical Bayes is an alternative method for prior selection that does not require separate training and test data . Empirical Bayes can be viewed as a maximum-likelihood procedure for estimating the prior distribution from data . It is also known in the literature as evidence optimization , Type II maximum likelihood , and maximum marginal likelihood [11] , [34] , [39]–[41] . The basic idea is that we can compute the probability of the data given a set of hyperparameters governing the prior by “integrating out” the model parameters . This probability is really just a likelihood function for the hyperparameters , so maximizing it results in a maximum-likelihood estimate for the hyperparameters . ( Technically , this is parametric empirical Bayes , since we will assume a particular parametric form for the prior; see [34] , [35] , [42] for a more general discussion ) . Let denote a set of hyperparameters controlling the prior distribution over , which we will henceforth denote . The posterior distribution over the RF ( eq . 4 ) can now be written: ( 6 ) The denominator in this expression is known as the evidence or marginal likelihood . ( Note that we ignored this denominator when finding the MAP estimate ( eq . 5 ) , since it does not involve ) . The evidence is the probability of the responses given the stimuli and the hyperparameters , which we can compute by integrating the numerator ( eq . 6 ) with respect to : ( 7 ) where is the parameter space for . Maximizing the evidence for therefore amounts to a maximum likelihood estimate of the hyperparameters . The MAP estimate for under this prior is an empirical Bayes estimate , since the prior is learned “empirically” from the data . Empirical Bayes can therefore be described as a two-stage procedure: ( 1 ) Maximize the evidence to obtain ; ( 2 ) Find the MAP estimate for under the prior . Fig . 1 shows a diagram for this hierarchical receptive field model the steps for empirical Bayesian inference . Following earlier work [11] , [36] , [43] , [44] , we will take the prior distribution to be a Gaussian centered at zero: ( 8 ) where is a covariance matrix that depends on hyperparameters in some yet-to-be-specified manner . This Gaussian prior together with a Gaussian likelihood ( eq . 2 ) ensures the posterior is also Gaussian: ( 9 ) where and are the posterior mean and covariance . The MAP filter estimate is simply the posterior mean , since the mean and maximum of a Gaussian are the same . Moreover , the evidence ( eq . 7 ) can be computed in closed form , since it is the integral of a product of two Gaussians . This allows for rapid optimization of . We will in practice maximize the log-evidence , given by: ( 10 ) where is the number of samples ( rows ) in and . All that remains is to specify the prior covariance , which we will explore in detail below . Before continuing , we wish to distinguish two distinct notions of “dimensionality” for a receptive field . First , dimensionality may refer to the number of parameters or coefficients in . We will refer to this as the parameter dimensionality of the filter , denoted . Second , dimensionality may refer to the dimensionality of the coordinate space in which the filter is defined . In this sense , a filter with elements arranged as a vector is 1-dimensional ( e . g . , a temporal filter ) , while a filter with the same number of elements arranged in a matrix is 2-dimensional ( e . g . , an image filter ) . We will refer to this as the coordinate dimensionality of the filter , denoted . We will examine three empirical Bayes RF estimators from the literature: ridge regression [45] , Automatic Relevance Determination ( ARD ) [36] , [43] , [44] , and Automatic Smoothness Determination ( ASD ) [11] . Fig . 2 provides an illustrative comparison of these methods , using a simulated example consisting with a 100-element vector filter ( ) , stimulated with correlated ( “1/F” ) Gaussian noise stimuli . The true filter was a difference of two Gaussians , and the maximum likelihood estimate ( middle left ) is badly corrupted by high frequency noise . First , ridge regression assumes a prior with covariance matrix proportional to the identity matrix: . This treats the filter coefficients as drawn i . i . d . from a zero-mean Gaussian prior with precision ( “inverse variance” ) . Ridge regression is penalized least-squares estimate with a penalty ( eq . 5 ) on the squared norm of the filter , given by . This penalty shrinks the coefficients of towards zero . Larger yields smaller filter coefficients , and in the limit of infinite , the MAP estimate shrinks to all-zeros . Set correctly , the ridge prior can provide substantial improvement over maximum likelihood , especially when the stimulus autocovariance is ill-conditioned , as it is for naturalistic stimuli ( see Fig . 2 ) . Ridge regression is perhaps the most popular and well-known regularization method . Although it is not usually employed in an empirical Bayes framework , it is straightforward ( and fast ) to maximize the evidence for the ridge parameter using a fixed-point rule [36] , [45] . ( See Methods ) . Second , Automatic Relevance Determination ( ARD ) [36] assumes a diagonal prior covariance matrix with a distinct hyperparameter for each element of the diagonal . This resembles the ridge prior covariance except that the prior variance of each filter coefficient is set independently . The prior covariance matrix can be written , where ranges over the number of elements in . It would be intractable to use cross-validation to estimate all the elements in ( a 100-element vector in Fig . 2 ) , so empirical Bayes plays a critical role for inference . In practice , evidence maximization drives many of the prior variances to zero , making the posterior a delta function at zero for those coefficients . The MAP estimate for these coefficients is therefore zero , making the ARD estimate sparse . The ARD estimate can be computed rapidly using fixed-point methods , expectation-maximization , or variational methods [43] , [44] , [46]–[49] . Fig . 2 ( middle column ) shows the ARD and the lasso estimate [50] , the latter of which is the MAP estimate under an exponential ( or ) prior . We set the lasso parameter here by cross-validation . Both estimates are sparse . The ARD estimate is actually sparser and less biased towards zero for large coefficients , but both fail to provide a close match to the smooth filter used in this example . Third , Automatic Smoothness Determination ( ASD ) [11] assumes a non-diagonal prior covariance , given by a Gaussian kernel [51] , which is parametrized so that the correlation between filter coefficients falls off as a function of their separation distance . The rationale here is that RFs are smooth in both space and time , so nearby coefficients should be highly correlated , while more distant ones should be more nearly independent . For a 1D filter , the ASD prior covariance takes the form of a “fuzzy ridge” , with Gaussian decay on either side of the diagonal . The 'th element is given by , where is the squared distance between the filter coefficients and in pixel space , and the hyperparameters control the scale ( analogous to the ridge parameter ) and smoothness ( the width of the fuzzy ridge ) , respectively . For filters with higher coordinate dimension ( e . g . , a 2D spatial filter ) , the hyperparameters include additional hyperparameters to control smoothness in each direction . Optimization of can be achieved by gradient ascent of the log-evidence ( see Methods ) . For our simulated example ( Fig . 2 , bottom middle ) , the ASD estimate is indeed smooth due to the correlations in the inferred prior . Note that for smooth RFs , the ASD prior covariance matrix becomes ill-conditioned , as some of its eigenvalues are very close to zero . This implies that the ASD estimate is sparse , but ( unlike ARD ) it is not sparse in the pixel basis . Rather , the ASD estimate is sparse in a basis that depends on the hyperparameters ( since the eigenvectors of the ASD prior covariance vary with the hyperparameters ) . The small-eigenvalue eigenvectors tend to have high-frequency oscillations , meaning that the ASD estimate is sparse in a Fourier-like basis , with the prior variance of high-frequency modes set near to zero . In our view , ASD is the current state-of-the-art method for linear filter estimation and indeed ( as shown in Fig . 2 ) it performs far better than previous methods for realistic neural RFs . The motivation for our approach is the observation that neural receptive fields tend to be localized in space , time , and spatiotemporal frequency ( i . e . , Fourier space ) . Neurons in the visual pathway , for example , tend to integrate light only within some restricted region of visual space and some finite window of time , and respond only to some finite range of spatiotemporal frequencies [25] , [32] , [52] , [53] . This is tantamount to a structured form of sparsity: large groups of coefficients ( e . g . , those outside some spacetime region ) that fall to zero in a dependent manner . Here we describe three prior distributions for exploiting this structure . We refer to these methods collectively as automatic locality determination ( ALD ) . To compare performance with previous receptive field estimators , we began with simulated data . We generated six different 2D spatial receptive fields with varying degrees of locality in space and frequency . Each filter consisted of a 2D array of pixels , making for a parameter space of dimensions . Noisy responses were simulated using 1600 samples of 1/F correlated Gaussian noise according to ( eq . 1 ) . Results are shown in Fig . 4 . Each row of Fig . 4 shows one of the six filters , and the estimates provided by maximum likelihood ( ML ) , ridge regression , ARD , ASD , and ( highlighted in blue ) ALDsf . The numbers in red below each estimate indicate the mean squared error between the true filter and the estimate . ( We did not show ALDs or ALDf because ALDsf always performed best of the three new methods ) . The simulated examples included: ( A ) a large Gabor filter; ( B ) a small Gabor filter; ( C ) a retina-like center-surround RF; ( D ) a grid cell RF with several non-zero regions; ( E ) circularly windowed Gaussian white noise; and ( F ) a pure Gaussian white noise filter . The grid cell filter did not exhibit strong locality in space , while the windowed white noise did not exhibit locality in frequency , and the pure white noise filter did not exhibit locality in either space nor frequency . Nevertheless , the ALDsf estimate had the smallest error by a substantial margin for all examples except the white noise filter . For the white noise filter , the ridge prior ( i . i . d . zero-mean Gaussian ) was in fact the “correct” prior . For this example , the ASD and ALDsf estimates were not distinguishable from the ridge regression estimate , consistent with the expectation that both should default to the ridge prior when the evidence did not favor smoothness ( ASD ) nor locality ( ALDsf ) . We examined the convergence properties of the various estimators as a function of the amount of data collected . We simulated responses from the first filter in Fig . 4A according to ( eq . 1 ) , using two kinds of stimuli: Gaussian white noise , and 1/F correlated Gaussian noise , which more closely resembles natural stimuli . The results ( Fig . 5 ) show that the ALDsf estimate achieved the smallest error for both kinds of stimuli , regardless of the number of training samples . The upper plots in Fig . 5 show that for white noise stimuli , traditional estimators ( ML and ridge regression ) needed more than four times more data than ALDsf to achieve the same error rate . For naturalistic stimuli , traditional estimators needed twenty to thirty times more data . The bottom row of plots shows the ratio of the average mean-squared error ( MSE ) for each estimate to the average MSE for the ALDsf estimate , showing that the next best method ( ASD ) exhibits errors nearly 1 . 8 times larger than ALDsf . Next , we compared the various estimators using neural data recorded from simple cells in primate V1 [53] . The stimuli consisted of 16 “flickering bars” aligned with each cell's preferred orientation . We took the receptive field to have a length of 16 time bins , resulting in a filter with two coordinate dimensions ( spacetime ) , resulting in a -dimensional parameter space . Because the “true” filter was not known , we quantified performance using relative cross-validation error , defined as the prediction error on an 8-minute test set ( See Methods ) . We varied the amount of data used for training , and performed 100 repetitions with randomly selected subsets of the full training data to obtain accurate estimates for each size training set . Fig . 6 ( left ) shows ML , ridge regression and ALDsf estimates for an example cell with a 1 , 2 or 4 minutes of training data . Numbers in red indicate the average cross-validation error of each estimate . Note that with only 1 minute of data , ALDsf performed nearly as well as ML and ridge regression with 4 minutes of data . The middle panel shows a summary of cross-validation error for each of the five empirical Bayes estimators discussed previously , as a function of the amount of training data . ALDsf once again achieved substantially lower error than other methods . The right panel shows how many times more data were required to achieve the same level of cross-validation error as ALDsf . On average , ALDsf required 1 . 7 times less data than the next best method ( ASD ) and five times less data than maximum likelihood . Fig . 7 shows the ML and ALDsf estimates for all 16 V1 simple cells in the population obtained with 1 minute of training data , as well as the ML estimate obtained using all the data available for each cell ( 40 minutes of data , on average ) . Note that for ALDsf recovers the qualitative structure of these RFs even when the underlying RF structure is barely discernible in the 1-minute ML estimate . Also note that the population exhibits substantial variability in RF shape , with many neurons whose RFs would not be well described by a fixed parametric form such as a Gabor filter . We examined a second dataset of retinal ganglion cells ( RGCs ) in primate retina , which stimulated with 2D spatiotemporal white noise ( “binary flicker” ) [54] , [55] . The RFs considered had 3 coordinate dimensions ( spacespacetime ) , and a 2500-dimensional parameter space ( pixels in space25 8 . 33 ms-bins in time ) . Fig . 8 shows the spatial ( 2D ) and the temporal ( 1D ) slices through the estimated 3D RFs ( schematized at left ) . Even with only 1 minute of training data , the ALDsf estimate recovered the qualitative structure of the RF at all time points , including the filters' departure from spacetime separability ( i . e . , the center pixel has different timecourse than surround ) . By contrast , the ML estimate is indistinguishable from noise in many places , indicating that ALDsf can reveal qualitative structure that is not visible in the ML estimate . We examined 3 ON and 3 OFF RGCs , and found that error was 18 times higher in ML estimates and 6 times higher in ridge regression estimates than in ALDsf ( where error was computed with respect to the ML estimate using a full 20 minutes of data ) . How can we quantify uncertainty in a receptive field estimate ? The error bars shown in Figs . 5 and 6 represent variability in across resampled or permuted datasets . However , we would like to be able to measure the uncertainty in a single estimate given a single set of training data . Given the hyperparameters , the model specifies a Gaussian posterior ( eq . 9 ) with mean and covariance . The diagonal of specifies the posterior variance for each element of , giving us 95% credible intervals ( Bayesian confidence intervals ) of the form ( 14 ) The interpretation of these credible intervals is that , given the data and , . More generally , for any unit vector , the credible interval of size ( ) for the projection is , where is the inverse normal cumulative density function . However , these credible intervals , and the associated Gaussian posterior for , are conditioned on maximum-evidence estimate of the hyper-parameters . These intervals fail to take into account uncertainty in , which may be substantial if the evidence is not tightly concentrated around its maximum . The true uncertainty in will therefore generally be greater than that captured by the posterior covariance . To accurately quantify uncertainty , we may wish to perform fully Bayesian inference under the priors introduced above . Empirical Bayes ( EB ) inference can be interpreted as an approximate form of fully Bayesian ( FB ) inference in a hierarchical model [35] , [45] . If we incorporate a prior over the hyperparameters at the top level of the graphical model shown in Fig . 1 B , also known as a hyperprior , we will have a complete hierarchical model of the neural response . The difference between EB and FB inference for comes down to the fact that the FB prior involves marginalizing over : ( 15 ) while the EB prior is just the conditional distribution . When are these priors equivalent or , more importantly , when do the EB and FB estimates agree ? The relationship between EB and FB inference can be understood by examining the posterior distribution over . The full posterior is ( 16 ) where is the posterior over given , and is proportional to the evidence ( i . e . the exponential of ( eq . 10 ) ) times the hyperprior: ( 17 ) where is a normalizing constant . Note that if the evidence is proportional to a delta function at its maximum , then the posterior over is itself a delta function , . The full posterior then reduces to ( 18 ) which is the EB posterior ( i . e . , the posterior over conditioned on ) . Thus , EB and FB inference are identical when the evidence is proportional to a delta function , and the two methods will in general give similar results whenever the evidence is highly concentrated around its maximum [45] . In general , EB and FB estimates will always agree given enough data , since by central limit theorem , the evidence will concentrate around its maximum with variance that falls as . However , for finite datasets , the two may differ . To examine the proximity of EB and FB estimates and credible intervals , we developed a sampling-based algorithm to perform FB inference under the ALD prior . The factorization shown in ( eq . 16 ) suggests an efficient method for sampling from via Markov Chain Monte Carlo ( MCMC ) , using a Markov chain over the space of the hyperparameters whose stationary distribution is proportional to the evidence . The summary of the algorithm for sampling is as follows: ( 19 ) A nice feature of this approach is that the hyperparameters live in relatively low-dimension ( e . g . , for a 1D filter and for a 2D filter under ALDsf ) . The Markov Chain therefore only has to explore this low-dimensional space , instead of the high-dimensional space of , which contains tens to thousands of parameters in typical cases [57] . Samples are obtained by drawing from the Gaussian conditioned on each MCMC sample . These samples may be averaged to the posterior mean , also known as the Bayes Least-Squares estimate , and their quantiles provide credible intervals . ( See Method ) . Fig . 9 shows a comparison of EB and FB estimates and credible intervals for the 1D simulated example shown previously . The hyperprior was taken to be uniform over a large region ( See Methods ) . For a small dataset , the FB credible intervals were noticeably larger than the EB credible intervals , as expected , owing to the effects of uncertainty in [35] . For larger datasets , this discrepancy was much smaller , and was smaller in general for ALDsf than ALDs or ALDf intervals . The EB and FB ( Bayes least-squares ) filter estimates , however , did not differ noticeably even for small amounts of data . Fig . 10 shows a comparison of EB and FB inference for the V1 neural data presented in Fig . 6 . For small datasets , the FB credible intervals were larger than EB intervals , but cross-validation error did not differ noticeably across dataset sizes . This suggests that the higher computational cost of FB inference may not be justified unless one is interested in obtaining accurate quantification of uncertainty from a small or noisy dataset .
We have described a new family of priors for Bayesian receptive field estimation that seek to simultaneously exploit locality in spacetime and spatiotemporal frequency . We have shown that empirical Bayes estimates under a localized prior are more accurate than those obtained under alternative priors designed to incorporate sparsity and smoothness . Although the ALD prior does not explicitly impose sparseness or smoothness , the estimates obtained with realistic neural data were both sparse and smooth . Sparsity arises from the fact that pixels outside a central region fall to zero , while smoothness arises from the fact that Fourier coefficients outside some low-frequency region fall to zero . However , for a receptive field dominated by high frequency components , ALD should outperform ASD and other smoothed estimates ( e . g . , smooth RVM [47] , fused lasso [58] ) , since it can also select regions centered on high frequencies . We have also derived an algorithm for performing fully Bayesian inference under ALD , ASD , and ridge regression priors . The algorithm exploits the low-dimensionality of the hyperparameter space and the tractability of the evidence to perform MCMC sampling of the posterior over hyperparameters . The full prior takes the form of a Gaussian scale mixture [59] , [60] , a mixture of zero-mean Gaussians with covariances and mixing weights , resulting in a Gaussian posterior over given that is trivial to sample . MCMC sampling allows for the calculation of fully Bayesian credible intervals over RF coefficients , which we found to be systematically larger than empirical Bayesian intervals . Nevertheless , we found no differences in the quantitative performance of EB and FB receptive field estimates with either simulated or real neural data ( Figs . 9 and 10 ) . Of course , both intervals rely on the linear-Gaussian model of the neural response , which may be inaccurate in cases where the neural response noise is highly non-Gaussian ( e . g . , heavy-tailed ) . More generally , this work highlights the advantages of locality as an additional source of prior information in biological inference problems . Shrinkage and sparsity have attracted considerable attention in statistics , and they have advantageous properties for a variety of high-dimensional inference problems [29] , [50] , [61] , [62] . ALD exploits a stronger form of prior information , assuming that large groups of coefficients go to zero in a correlated manner . This may not hold for generic regression problems; for a sparse filter with randomly distributed non-zero coefficients , the ARD estimate substantially outperforms ALD ( not shown ) , but such filters are unlikely to arise in neural systems . Two general ideas that arise from ALD may be useful for thinking about statistical inference in other biological and non-biological systems . The first is the idea of exploiting an underlying coordinate system or topography . Whenever the regression coefficients can be arranged topographically ( e . g . , temporally , spatially , spectrally ) , it may be possible to design a prior that exploits dependencies within this topography using a small number of hyperparameters . This idea is central to ALD as well as to ASD , which uses the distances between RF pixels to set their prior correlation . But other coordinates and prior parameterizations are possible . For example , although ALD performs reasonably well for a simulated grid cell ( Fig . 4 D ) , locality in space does not hold for grid cells , and a prior that exploits the “natural” parameters of grid cell responses ( e . g . , grid spacing , size , orientation , phase ) might perform even better . Optimizing the hyperparameters governing such a prior is tractable with empirical Bayes . The second idea that arises from ALD is that of simultaneously constraining a set of regression coefficients in two ( or more ) different bases . The ALDsf method combines a local prior in a spacetime basis and a local prior in Fourier basis via a “sandwich matrix” ( eq . 13 ) , which effectively applies prior constraints in series: first in spacetime and then in frequency . Another solution would be to combine the two priors symmetrically , e . g . , using prior covariance . ( This is the covariance that results from taking the product of the ALDs and ALDf Gaussian priors ) . We found this formulation to perform slightly worse on test data , but results were similar . Note that the sum of prior covariances would not achieve the desired goal of imposing the prior constraints simultaneously , since it would prune only those coefficients in the ( effective ) null space of both and . A large literature has examined regularization and feature selection in overcomplete dictionaries ( e . g . , “basis pursuit” ) [62]–[65] , but combining structured prior information defined in different bases poses an intriguing open problem . One potential criticism of ALD is that the linear-Gaussian encoding model ( eq . 1 ) is overly simplistic . Despite its simplicity , this model has a long history in the neural characterization literature [5] , [11] , [18] , and the estimators considered here are consistent ( i . e . , converge asymptotically ) for responses generated by any linear-nonlinear response model , so long as the stimuli are elliptically symmetric and the expected STA is non-zero [20] . We addressed whether the linear-Gaussian modeling assumption undermines our results by re-analyzing the V1 simple cell data with maximally informative dimensions ( MID ) [66] , an information-theoretic estimator that incorporates neural nonlinearities and Poisson spiking . The results ( shown in Supporting Information ( Text S1 ) , Fig . S1 ) , indicate that MID errors were large , comparable in size to those of the maximum likelihood ( linear regression ) estimate . Even when comparing to the MID filter computed from test data , ALDsf outperformed MID by a substantial margin . This shows that the limitations of the linear-Gaussian model do not substantially undermine its performance on simple cells . However , we have applied ALD only to neurons whose responses exhibit a quasi-linear relationship to the stimulus . ALD would indeed fail for a neuron with a symmetric nonlinearity ( e . g . , squaring ) and cannot recover multiple filters ( e . g . , those driving a complex cell ) . A variety of techniques exist estimating multi-dimensional feature spaces ( e . g . , spike-triggered covariance ( STC ) [67]–[69] , MID [20] , [66] , iSTAC [70] , spike-triggered ICA [71] ) . However , the “kernel trick” [17] , [41] , which involves using linear methods on nonlinearly transformed stimuli , provides the simplest method for extending ALD to nonlinear response models . Many nonlinear transformations ( e . g . , transforming the stimulus to its Fourier power [72] ) preserve the topography of the underlying stimulus , making this approach directly applicable to ALD . One advantage of the linear-Gaussian model is its computational tractability . ALD is fast because the evidence can be calculated and optimized entirely from the sufficient statistics , , and ( the raw stimulus covariance , the STA , and sum of squared responses , respectively ) . This means that the computational cost does not scale with the amount of data ( unlike MID and maximum-likelihood point process methods ) . Evidence optimization is also much faster than cross-validation , particularly with the hyperparameters employed by ALDsf . The computational cost of ALD is still at least in the number of filter coefficients , since evidence evaluation requires left-division by matrices of size . However , the number of approximately zero coefficients often falls considerably during optimization , and eliminating these coefficients by thresholding small eigenvalues of can speed convergence considerably . Given the hyperparameters , the log-posterior over is concave , with a single maximum that can be computed in closed form ( eq . 5 ) . Although the log-evidence ( eq . 10 ) is not concave in the hyperparameters , there are far fewer hyperparameters than parameters , making ALD far easier than non-convex optimization in the full space of ( e . g . , as in MID ) . We can maximize the evidence more rapidly by using its first and second derivatives , which we can compute analytically ( see Methods ) . We also exploit a heuristic strategy for initializing the ALDsf hyperparameters using the estimates from ridge regression ( to identify the scale ) , ALDs ( to identify a spatiotemporal region ) and ALDf ( to identify a Fourier region ) . Although it is substantially more computationally expensive , the fully Bayesian estimate based on MCMC avoids the issue of local maxima because it explores the entire evidence surface , not just its modes . However , we do not ultimately view ALD and other model-based or information-based methods as in conflict . Rather , we regard ALD as providing a prior distribution over RFs that can be combined with any likelihood . Computing and optimizing the evidence under nonlinear models with non-Gaussian noise represents an important direction for future work . We suggest that locality is a general feature of neural information processing and anticipate that it will be useful for neural characterization in a wide variety of brain areas , including those where response properties are not yet well understood [73] . We expect hierarchical models and empirical and fully Bayesian inference methods to find application to a wide range of problems where structured prior information can be usefully defined .
For the simulated data shown in Fig . 5 , we used a 2-dimensional Gabor filter ( shown in Fig . 4 A ) and two types of stimuli: Gaussian white noise and “naturalistic spectrum” noise–Gaussian noise with a power spectrum . Simulations were carried out with various numbers of stimulus samples , noise variance , signal variance of 1 , and a pixel filter ( coordinate dimension , filter dimension ) . To quantify performance , we defined the filter error as , where is the true filter and is an estimate . To obtain reliable estimates of mean error , we ran 100 simulations at each sample size . To calculate the relative error ( Fig . 5 B and D ) , we computed the error for each method , and then computed the geometric mean of the error ratio across datasets . For V1 data shown in Fig . 6 , the data and experimental methods are described in [53] . Briefly , cells were stimulated with 1D spatiotemporal binary white noise stimuli ( “flickering bars” ) aligned with each neuron's preferred orientation . Stimuli were presented at a frame rate of 100 Hz . The number of bars varied for different neurons , . The linear receptive field was assumed to extend over a time window of frames before a spike ( a 160 ms time interval ) . The full dimensionality of the filter was thus , ranging from 192 to 384 parameters . For retinal ganglion cell data shown in Fig . 8 , the data and experimental methods are described in [54] , [55] . Briefly , cells were stimulated with the spatiotemporal binary white noise stimuli presented at a frame rate of 120 Hz , contained in 10×10 pixels in space . We assumed the size of the linear receptive field to be pixel 25 time bin , making for total coefficients in the RF . We used cross-validation to quantify the performance of the various estimators ( Fig . 6 ) , and resampled the training data to examine performance as a function of training sample size . To quantify error reliably , we performed 100 repetitions for each sample size , drawing the training data randomly without replacement in blocks of size 2s , which helped to minimize the effects of non-stationarities in the data . To quantify cross-validation performance , we used relative cross-validation , defined as , where is the number of samples of test data , is a spike count in the 'th time bin in the test set , is the th row of the design matrix , is the RF estimate obtained by each method ( from training data ) , and is the ML estimate obtained on the test data . Essentially , this is the ordinary test error minus the error of the ML estimator trained on test data ( which provides an absolute lower bound on the performance of any linear model ) . We computed the relative cross-validation errors from five methods ( ML , Ridge , ARD , ASD , and ALDsf ) using 8 minutes of test data . In Fig . 6 , we normalized the errors by dividing them by maximum average error across methods ( the ML estimate using 30 seconds of data yielded the maximum cross-validation error ) . We computed the standard deviation of the normalized cross-validation error across 100 different training sets for each dataset size . To perform fully Bayesian inference , we used Metropolis-Hastings ( MH ) sampling to sample from the distribution over hyperparameters given the data . We used an isotropic Gaussian proposal distribution with variance given by the largest eigenvalue of inverse Hessian of the log-evidence around . ( More advanced proposal distributions and sampling methods are found in [76] , [77] , but this simple proposal sufficed for our purposes and mixed reasonably quickly ) . Thus , we first optimized the evidence to obtain the mode of , which is the mode of . We assumed a non-informative hyperprior , taken to be uniform over the range of values permitted during constrained optimization of the log-evidence ( see above ) . To carry out MH sampling , we sampled from the Gaussian proposal distribution centered on the current state of the Markov chain , , then computed , with the . We accepted the proposal randomly with probability , setting , and otherwise rejected it , setting . Given each sample , we drew a sample of the receptive field . These samples were averaged to compute the posterior mean ( or Bayes Least Squares estimator ) . Their quantiles were used to compute credible intervals for each filter coefficient . In Fig . 10 , we compared fully Bayesian ( FB ) and empirical Bayes ( EB ) filter estimates obtained from V1 simple cell data [53] . For each set of training data , we drew 5000 samples using MH to compute the posterior mean and credible intervals . The average acceptance rate of the MH sampler was 0 . 12 . For Fig . 10 A , we computed the average of the EB and FB error from 100 repetitions with independently drawn sets of training data . We computed the average cross-validation error of both estimates of the example cell ( in red ) . For Fig . 10 B , we computed the average posterior variance by averaging the posterior variances in the estimates from the 100 iterations in each cell , which we then averaged across all 16 cells . For Fig . 10 C , we computed the average cross-validation error by averaging the errors from the 100 iterations in each cell , and we averaged these across 16 cells . The same 8 minutes of held out test data was used for cross-validation , for all training iterations . | A central problem in systems neuroscience is to understand how sensory neurons convert environmental stimuli into spike trains . The receptive field ( RF ) provides a simple model for the first stage in this encoding process: it is a linear filter that describes how the neuron integrates the stimulus over time and space . A neuron's RF can be estimated using responses to white noise or naturalistic stimuli , but traditional estimators such as the spike-triggered average tend to be noisy and require large amounts of data to converge . Here , we introduce a novel estimator that can accurately determine RFs with far less data . The key insight is that RFs tend to be localized in spacetime and spatiotemporal frequency . We introduce a family of prior distributions that flexibly incorporate these tendencies , using an approach known as empirical Bayes . These methods will allow experimentalists to characterize RFs more accurately and more rapidly , freeing more time for other experiments . We argue that locality , which is a structured form of sparsity , may play an important role in a wide variety of biological inference problems . | [
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] | 2011 | Receptive Field Inference with Localized Priors |
Although retroviruses have been extensively studied for many years , basic questions about how retroviral infections are detected by the immune system and which innate pathways are required for the generation of immune responses remain unanswered . Defining these pathways and how they contribute to the anti-retroviral immune responses would assist in the development of more effective vaccines for retroviral pathogens such as HIV . We have investigated the roles played by CD11c+ dendritic cells ( DCs ) and by Toll-like receptor ( TLR ) signaling pathways in the generation of an anti-retroviral immune response against a mouse retroviral pathogen , Friend murine leukemia virus ( F-MLV ) . Specific deletion of DCs during F-MLV infection caused a significant increase in viral titers at 14 days post-infection , indicating the importance of DCs in immune control of the infection . Similarly , Myd88 knockout mice failed to control F-MLV , and sustained high viral titers ( 107 foci/spleen ) for several months after infection . Strikingly , both DC-depleted mice and Myd88 knockout mice exhibited only a partial reduction of CD8+ T cell responses , while the IgG antibody response to F-MLV was completely lost . Furthermore , passive transfer of immune serum from wild-type mice to Myd88 knockout mice rescued control of F-MLV . These results identify TLR signaling and CD11c+ DCs as playing critical roles in the humoral response to retroviruses .
The HIV pandemic has spurred intensive research into retroviruses , and yet an effective vaccine for HIV has remained elusive . Acute HIV infection stimulates both B and T cell responses , but the antibody response is ineffective , possibly due to shielding of neutralizing epitopes [1] , [2] . By contrast , HIV-specific CD8+ T cells are able to control infection early on , but become progressively less effective during the chronic phase of infection due to mechanisms that remain unclear [3] , [4] . Vaccines designed to stimulate protective B cell or T cell responses have been used in clinical trials , but have been unsuccessful at either preventing infection or reducing viral titers in infected individuals [5] . Thus , a more fundamental understanding of anti-retroviral immune responses is needed to develop an effective vaccine . Basic questions that have not been answered include: 1 ) Which antigen presenting cell populations are necessary or sufficient to generate an immune response ? 2 ) Which innate signaling pathways detect retroviral infection in vivo and are responsible for initiating adaptive immune responses ? There have been major advances during the past decade in our understanding of how the innate immune system functions to limit viral growth and stimulate T and B cell- dependent adaptive immune responses . It is now understood that microbial products that serve as pathogen-associated molecular patterns ( PAMPs ) are detected by germline-encoded innate immune receptors , such as the members of the Toll-like receptor ( TLR ) family [6] . These receptors are prominently expressed in antigen-presenting cells such as dendritic cells ( DCs ) that function at the interface between innate and adaptive immunity . Humans encode at least ten TLRs while mice encode at least twelve . Products of bacterial metabolic pathways are recognized by specific TLRs such as LPS by TLR4 and flagellin by TLR5 [7] . Viruses , by contrast , are thought to be detected by mechanisms that involve endosomal localization of viral nucleic acids . ssRNA is detected by TLR7 [8] , [9] , dsRNA is detected by TLR3 [10] , and CpG dsDNA is recognized by TLR9 [11] . All TLRs except for TLR3 signal through a pathway that involves the adaptor Myd88 [12] . Upon stimulation , Myd88 is recruited to the TLR as a dimer , and activates the kinases IRAK1 and IRAK4 . This activates a signaling cascade that ultimately leads to the activation of the pro-inflammatory transcription factor NF-κB , as well as the MAP kinase and JNK pathways [13] . In the absence of Myd88 , TLR3 and TLR4 are able to signal through another adaptor , TRIF [14] . Friend murine leukemia virus ( F-MLV ) is a complex gamma-retrovirus that has been used to understand basic principles of anti-retroviral immune responses and mechanisms of chronic infection [15] . It consists of a replication-competent helper virus and a replication-defective spleen focus-forming virus ( SFFV ) . SFFV encodes a protein , gp55 , which binds to the erythropoietin receptor and causes hyper-proliferation of erythroid precursors and splenomegaly . The susceptibility of mice to F-MLV is affected by a number of host genes . In mouse strains such as Balb/c , immune responses are ineffective and sustained high virus titers eventually lead to erythroid leukemia . In C57BL/6 mice , acute infection is controlled by CD8+ T cells and neutralizing antibodies , but the virus is not completely cleared and establishes a low level persistent infection [16] . Retroviral particles for HIV and F-MLV possess potential TLR ligands , and could conceivably be detected by TLR signaling [17] . Indeed , some evidence has indicated that plasmacytoid dendritic cells secrete type I interferons in response to HIV by way of a TLR7-dependent pathway [18] . However , the role played by TLRs in generating an anti-retroviral immune response in vivo is unknown . In this study , we addressed the roles played by CDllc+ DCs and Myd88-dependent signaling in the immune response to F-MLV infection in vivo . Using mice that permit specific deletion of CD11c+ DCs as well as Myd88-deficient mice , we determined that DCs and Myd88 contribute to , but are not required for , T cell responses to F-MLV , but are absolutely required for the generation of virus-neutralizing antibodies .
Previous reports demonstrated that F-MLV titers in the spleens of infected mice peak at 14 days post-infection ( dpi ) before they subside to a low level during the phase of chronic infection . However , F-MLV stocks used in the earlier studies were contaminated with Lactate Dehydrogenase-Elevating Virus ( LDV ) , which has been shown to delay the immune response to F-MLV [19] . To establish growth kinetics of F-MLV in vivo in the absence of LDV , we infected C57BL/6 mice with F-MLV and measured viral titers in the spleen at different times post-infection . The number of foci per spleen peaked around 7 dpi at approximately 106 foci per spleen , and by 14 dpi the viral titer had been reduced to between 102 and 103 foci per spleen ( Figure 1 ) . This growth curve is consistent with a recent report using LDV-free F-MLV [19] . Dendritic cells ( DCs ) are specialized cells that carry out a number of important roles . They detect pathogens through innate receptors and activate signaling pathways that lead to the expression of cytokines . Furthermore , they present foreign antigens to T cells by way of MHCI and MHCII molecules . Mouse splenic DCs consist of three major subsets with specialized functions . CDllc+ CD8α− B220− ( “myeloid” ) DCs are the classical antigen-presenting cell and are important for activation of CD4+ T cells . CDllcInt B220+ CD8α− ( “plasmacytoid” ) DCs are potent producers of type 1 interferons [20] . CDllc+ B220− CD8α+ DCs are specialized for cross presentation and are important for activating CD8+ T cells [21] . When DCs detect foreign pathogens they mature and up-regulate their antigen presenting machinery as well as costimulatory molecules such as CD80 and CD86 . To determine if F-MLV causes activation of specific DC subsets during infection , we infected C57BL/6 mice with F-MLV and examined the expression of CD80 and CD86 on all three splenic DC subsets at different times post-infection . CD80 and CD86 were upregulated on all three DC subsets beginning at 7 dpi ( Figure 2 ) . DC expression of CD80 and CD86 levels returned to baseline by 14 dpi . This indicates that all three DC subsets mature in response to F-MLV infection in vivo , and that DC activation peaks between 7–10 dpi . Immune control of F-MLV requires both CD8+ cytotoxic T lymphocyte ( CTL ) and neutralizing antibody responses [22] . Several cell types , including DCs , macrophages and B cells , are capable of antigen presentation to T cells , but for most pathogens it is unclear which of these cell types is explicitly required for the generation of specific immune responses . To investigate the role of CD11c+ DCs in immune function , we previously developed transgenic mice that express the diphtheria toxin receptor ( DTR ) under the control of the CD11c promoter [23] . Since expression of DTR renders murine cells susceptible to killing by diphtheria toxin ( DT ) , this transgene allows for specific deletion of CDllc-expressing DCs in vivo by the administration of DT . Because repeated administration of DT to these mice is lethal within 7 days due to an unknown mechanism , we generated bone marrow chimeras , by lethally irradiating C57BL/6 mice and reconstituting them with wild-type or CDllc-DTR transgenic bone marrow . Mice receiving the transgenic bone marrow are viable through at least 2 weeks of repeated DT injection . To investigate the role played by CD11c+ DCs in the immune response to a retrovirus , we infected the chimeric mice with F-MLV . To delete DCs , we injected 200 ng DT daily from one day prior to infection until 14 dpi . At 14 dpi , the mice were sacrificed , and the extent of DC depletion in the spleen was determined by flow cytometry . Deletion of CD11c+ cells was typically around 90% ( Figure 3A ) . Strikingly , the number of infectious foci in the spleens of DC-depleted mice was 100-fold higher than in non-depleted mice ( Figure 3B , left panel ) . This indicates that CDllc+ DCs play an essential role in immune control of F-MLV , and that other antigen presenting cells are not sufficient for full activation of anti-retroviral immune responses . DC-depleted mice also exhibited increased splenomegaly ( Figure 3B , right panel ) , indicative of higher levels of virus . To learn about the temporal requirement for DCs in immune control of F-MLV infection , we also depleted DCs from day 4 to 14 following infection . We had previously observed strong activation of DC populations in the spleen at 7–10 dpi ( Figure 2 ) . Interestingly , depletion from day 4–14 post-infection did not result in a significant increase in virus titers in the spleen relative to undepleted mice ( not shown ) . This suggests that the presence of DCs during the first four days of infection is sufficient to establish immune control , and that the mature DC populations visible later in infection are not required . Since DC-depleted mice exhibited reduced ability to control F-MLV , we wished to determine whether either the CTL or neutralizing antibody response was affected by the depletion . To measure the role of CD11c+ DCs in the CTL response , we infected chimeras reconstituted with wild-type or CDllc-DTR bone marrow with F-MLV . DCs were depleted by injection of DT from one day prior to infection until 14 days post-infection . At 14 dpi , splenocytes were stained with the Db-GagL tetramer . This tetramer typically stains 5–10% of CD8+ CTLs in a F-MLV–infected wild-type mouse , but does not stain CD8+ T cells from naïve mice [16] . We found that the proportion of CD8+ T cells that stained with Db-GagL was only slightly reduced in DC-depleted mice , suggesting that high levels of CD11c+ DCs are not required for the generation of CTL responses to F-MLV ( Figure 4A ) . It is possible that only a small number of DCs is sufficient for activating a CD8+ T cell responses , or that a non-depleted APC population mediates CD8+ T cell activation . To assess the role of DCs in the generation of neutralizing antibodies against F-MLV , we infected the bone marrow chimeras with F-MLV and depleted DCs by injection of DT from day −1 to day 14 post-infection . At 14 dpi , we harvested peripheral blood from infected mice and isolated serum . We then measured the titer of F-MLV–specific neutralizing antibodies in the serum by determining the maximum dilution of serum that was still sufficient to neutralize F-MLV infection of Mus Dunni cells in tissue culture . In wild-type infected mice , neutralizing antibody titers were typically between 40–160 . Strikingly , in DC depleted mice , neutralizing antibodies were undetectable , with titers<10 ( Figure 4B ) . These results demonstrate that CD11c+ DCs play an essential role in the generation of F-MLV–specific neutralizing antibodies , but are less important for CTL responses . Because F-MLV particles contain molecules such as ssRNA that could activate TLR signaling , we reasoned that infection might be detected via a TLR-dependent pathway . To test whether TLR signaling was required for immune control of F-MLV , we infected mice heterozygous or homozygous for a null Myd88 allele and analyzed virus levels in the spleens at various time-points post-infection . We also compared wild-type mice to heterozygous mice at several time-points over an eight week period and found no significant difference in viral titers , so all comparisons in subsequent studies were between heterozygous and homozygous mutant mice . In the first 7 days after infection , viral levels were slightly higher in Myd88 null mice than in heterozygotes , but they reached a similar peak level at one week post-infection ( wpi ) . At two wpi , however , viral titers were dramatically higher in Myd88 knockout mice . In heterozygous mice , titers dropped to between 102 and 103 foci per spleen , whereas in Myd88 knockout mice virus titers remained at between 105 and106 foci per spleen . Furthermore , at 8 wpi and 16 wpi , heterozygous mice had developed a low level chronic infection at 102–103 foci per spleen , while Myd88 knockout mice exhibited high ( 106–107 foci per spleen ) levels of virus infection ( Figure 5A ) . This suggested that some aspect of immune control of F-MLV is impaired in the absence of Myd88 . In contrast with DC depletion , splenomegaly was not enhanced relative to that in heterozygous mice at 2 wpi . By 16 wpi , however , some Myd88 knockout mice exhibited significantly enlarged spleens ( Figure 5B ) . Interestingly , Myd88 knockout mice did not show a significant increase in mortality relative to heterozygous mice over 16 weeks of infection , and infected knockout mice seemed otherwise healthy despite maintaining high virus titers ( Figure 5C ) . We had previously observed that the three major DC subsets in the spleen undergo maturation between 7 and 10 days post-infection . To determine if DC maturation was dependent on Myd88 , we analyzed expression of CD80 at 7 dpi on splenic DCs of Myd88 heterozygous or knockout mice that had been infected with F-MLV . As shown earlier ( Figure 2 ) , F-MLV infection caused up-regulation of CD80 on all three major splenic DC subsets in heterozygous mice . In the Myd88 knockout mice , the CD11c+ B220− CD8α− DCs exhibited little reduction in up-regulation of CD80 ( Figure 6 , left panel ) . CD11c+ B220− CD8α+ DCs and CD11cint B220+ CD8α− DCs , by contrast , exhibited more pronounced reduction in maturation , but still exhibited some response to F-MLV ( Figure 6 , middle and right panels ) . This suggests that both Myd88-dependent and independent pathways contribute to the maturation of DCs in response to F-MLV , and that the contribution made by Myd88-dependent signaling varies somewhat between different DC subsets . Previous studies have shown that immune control of F-MLV infection depends on all three of the major lymphocyte populations - CD4+ T cells , CD8+ T cells and B cells [24] . We wished to determine if T cell responses to F-MLV were defective in Myd88 knockout mice . We infected Myd88 heterozygous and homozygous mutant mice with F-MLV , and at 13 dpi analyzed CD8 T cells by staining with the Db-GagL tetramer ( Figure 7A ) . In heterozygous mice , approximately 5–8% of the CD8+ T cell population stained positive with this tetramer ( Figure 7A , left panel ) . In naïve mice , CD8 cells were not significantly stained ( 0 . 2% ) . In homozygous mutant mice , the proportion of CD8+ T cells that was Db-GagL+ was significantly reduced to approximately 2% ( Figure 7A , right panel ) . This indicates that the CD8+ T cell response to F-MLV is partially dependent on Myd88 signaling , but that Myd88-independent mechanisms can compensate in its absence . To determine if the CD4+ T cell response to F-MLV was affected by loss of Myd88 , we analyzed CD4+ T cells in infected Myd88 heterozygous or homozygous mutant mice by intracellular staining for interferon-gamma ( IFNγ ) expression . In naïve heterozygous and knockout mice , roughly 3% of the CD4+ T cells stained positive for IFNγ ( Figure 7B ) . In infected heterozygous mice , 11% of the CD4+ T cells were IFNγ positive , indicative of a robust Th1 response to F-MLV . By contrast , the proportion of IFNγ positive CD4 T cells in Myd88 knockout mice was reduced to 6% . These results indicate that both the CD8+ and CD4+ T cell responses to F-MLV are present but reduced in the absence of Myd88 . To determine whether the B cell/antibody response to F-MLV was affected by loss of Myd88 , we measured the titer of F-MLV–neutralizing antibodies in the serum of infected Myd88 mutant mice . At 14 dpi , heterozygous control mice exhibited a strong neutralizing antibody response to F-MLV with a titer typically in the range of 80 ( Figure 8 , upper panel ) . By contrast , in the serum of Myd88-deficient mice , neutralizing antibodies were undetectable . We also measured neutralizing antibody titers at eight weeks post-infection ( Figure 8 , lower panel ) , and found strong antibody titers in the serum of heterozygous mice but undetectable F-MLV–neutralizing activity in the homozygous mutant animals . Since it is known that B cells and neutralizing antibodies are required to control F-MLV , the results suggest that the lack of a neutralizing antibody response is a significant contributor to the failure of Myd88-deficient mice to control F-MLV . The absence of a detectable neutralizing antibody titer in Myd88-deficient or CD11c+ DC-deleted mice could reflect a general loss of serum antibodies to F-MLV or a defect specifically in antibodies that neutralize infection . To distinguish between these possibilities , we measured F-MLV–specific IgG levels in the serum of these mice at 14 dpi . F-MLV–specific antibodies were detected by incubating serum samples from infected mice with a suspension of an F-MLV–infected cell line , followed by staining for mouse IgG and flow cytometry . Staining activity was present in the serum of infected wild-type or Myd88 heterozygous mice , but absent from the serum of naïve mice ( Figure 9 ) . Also , serum from wild-type or Myd88 heterozygous mice did not stain an uninfected cell line , indicating that the staining was specific for F-MLV ( not shown ) . Significantly , F-MLV–specific IgG was completely absent from the serum of infected Myd88-deficient or DC-depleted mice . These data argue that Myd88 and CD11c+ DCs regulate the generation of total anti F-MLV IgG levels , and not only the amount of antibodies that neutralize viral infection . Since B cells , CD4+ T cells , and DCs are all known to regulate antibody responses , it is possible that an increased level of viral infection of one of these cell types during the acute phase was responsible for the antibody defect in Myd88-deficient mice . To examine this possibility , we examined the proportion of each of these cells that was infected with F-MLV at 6 dpi by staining splenocytes of infected mice for cell surface expression of the viral GlycoGag protein . Neither B cells , CD4+ T cells , nor DCs exhibited significantly higher proportions of infected cells in Myd88 knockout mice at this timepoint ( not shown ) . Because the most profound defect associated with the poor control of F-MLV infection in Myd88-deficient mice was the lack of a neutralizing antibody response , we asked whether passive transfer of immune serum from wild-type mice could rescue control of F-MLV in the mutant mice . We infected Myd88 heterozygous or homozygous mutant mice with F-MLV , and at 7 dpi homozygous mice were injected intraperitoneally with 0 . 5 ml of serum taken either from naïve C57BL/6 mice or from mice that had been infected with F-MLV 14 days earlier . Consistent with our previous observations , at 14 dpi heterozygous mice had low numbers of viral foci in their spleens , while Myd88-deficient mice exhibited approximately 1000-fold higher numbers of foci ( Figure 10 ) . Knockout mice that had received naïve serum had virus titers similar to untreated knockout mice . However , Myd88-deficient mice that had received serum from wild-type F-MLV–infected mice had virus levels that were more than 100-fold lower , with approximately 103 foci per spleen . This indicates that serum from wild-type infected mice can rescue control of F-MLV in Myd88-deficient mice , and is consistent with the finding that the most important role of Myd88 in F-MLV infection is the generation of a virus-specific neutralizing antibody response .
Although innate immune detection of viral pathogens is considered to be important for the initiation of adaptive immune responses , for most viruses , including retroviruses , the specific sensing pathway has not been identified . Our results demonstrate a crucial role for CD11c+ DCs and Toll-like receptor signaling in the antibody response to retroviral infection . We have also identified a significant but less critical role for TLRs in CD4+ and CD8+ T cell responses to retroviral infection . Myd88-deficient mice sustain high titers of F-MLV for several months post-infection , indicating that the response is not simply delayed . To our knowledge , this is the first report of Myd88 or TLR signaling playing an essential role in immune control of a virus that is primarily mediated via the anti-viral antibody response . Similarly , previous studies of the effect of DC depletion in immune control of pathogens have focused on T cell responses , rather than on antibody-mediated control [23] , [25] . Our results demonstrate that depletion of DCs leads to a loss of immune control of F-MLV , and this was found to coincide with a complete loss of F-MLV–specific IgG but no significant reduction in CD8+ T cells responses . The role of TLRs in B cell dependent immune responses has been somewhat controversial . Pasare and Medzhitov found that Myd88 knockout mice were defective at generating antibodies specific for the model antigens OVA and HSA in the presence of LPS [26] . Furthermore , they found this defect to be at least partly B cell intrinsic , since mice lacking mature B cells due to a disruption of the Ig μ chain ( μMT mice ) that had received B cells lacking Myd88 , but not mice that had received wild-type B cells , also had defective antibody responses . This view has been challenged by Gavin and coworkers [27] who found that Myd88−/−;TrifLPS2/LPS2 mice were able to mount apparently normal antibody responses to the model antigen trinitrophenol-hemocyanin given in complete Freund's adjuvant . However , neither of these studies examined the requirement of TLR signaling for an antibody response in the context of a live viral infection , nor did they examine the ability of the antibody response to neutralize an infectious pathogen . The issue of whether or not TLR signaling is important for effective neutralizing antibody responses is of profound importance for vaccine design , and the work that we describe here strongly suggests that , for retroviral pathogens , functional TLR signaling is indeed required for the antibody response . Some previous reports have indicated a role for TLRs in the development of antibody responses during viral infection . Heer and coworkers found that , in Myd88 knockout mice , antibodies specific for influenza exhibited defective class switching [28] . Furthermore , it has been shown that long term maintenance of antibodies to polyoma virus required Myd88 , although Myd88-deficient mice were still able to control polyoma virus infection in vivo [29] . Interestingly , Myd88-deficient mice that were infected with Vesicular Stomatitis Virus exhibited higher levels of VSV-specific IgG1 but lower levels of IgG2a relative to wild type mice , while the neutralizing antibody titer was somewhat lower [30] , [31] . Previous studies of the roles played by TLRs in anti-viral immune responses have shown significant differences from virus to virus . For example , Myd88 is required for the CD8+ T cell response to the arenavirus LCMV [32] , [33] , but not for the CD8+ T cell response to influenza [28] . The reason for the differential requirement for TLRs in the immune response to different viruses is not clear but could be related to the structural features of the virus , cell tropism , or the details of the replication strategy used by the virus . The specific cell type ( s ) in which TLR signaling is required for the antibody response to F-MLV is not clear . TLR signaling in DCs promotes the expression of costimulatory molecules such as CD80 and CD86 that are important for triggering CD4+ and CD8+ T cell responses . CD4+ T cells , in turn , play an important role in activating B cells . Thus , the effect of DCs and TLR signaling on B cell responses could be mediated through CD4+ T cells . However , upregulation of CD80 on the DC population ( CD11c+ , B220− , CD8α− ) that is known to control CD4+ T cell responses was only slightly reduced in the absence of Myd88 . Similarly , the CD4+ T cell response to F-MLV was reduced but not absent in Myd88 knockout mice . DCs are known to activate B cells directly in a T cell independent manner by FcγRIIB-mediated recycling of whole antigen , but this process is not known to be TLR dependent [34] . It is possible that DCs release cytokines in a Myd88-dependent manner that directly affects B cell responses . Also , since B cells express TLRs [35] , it is possible that TLR signaling is directly required in B cells to generate F-MLV–specific antibodies . Conditional deletion of Myd88 in DCs or B cells would address the relative contribution of Myd88-dependent signaling in these cell types . These results also suggest that DC-independent and Myd88-independent pathways contribute to the development of the T cell responses . Since retroviral genomes fold into structures that contain dsRNA , it is possible that TLR3 and TRIF could be involved . Also , since the retroviral replication cycle involves the generation of dsDNA in the cytoplasm of infected cells , it its possible that a cytoplasmic DNA sensor such as DAI contributes to retroviral detection [36] . Cellular molecules that are released from damaged cells , such as uric acid and ATP , have also been shown to have the ability to stimulate immune responses [37] , but most retroviruses , including F-MLV , are non-cytopathic and do not cause the death of infected cells . Since DC-depleted mice can still mount a significant CD8+ T cell response to F-MLV , it is possible that another antigen presenting cell type is sufficient to mediate this function . Depletion of other antigen presenting cells such as B cells and macrophages could be used to address this issue , although B cell-deficient μMT mice are still able to mount a CD8+ T cell response to F-MLV , indicating that an essential role for B cells in the CD8 T cell response is unlikely [38] . Could manipulation of TLR signaling potentially be used to enhance effectiveness of an HIV vaccine ? It has been argued that effective vaccines , such as live attenuated vaccines , are able to provide effective protection because they stimulate TLRs . Furthermore , most effective vaccines involve the use of adjuvants that contain TLR ligands , although for many of these the role of TLR signaling has not been conclusively demonstrated . One study found that administration of the TLR9 ligand CpG enhanced the cellular and humoral response induced by a Gag prime boost vaccine [39] . Also , it has been demonstrated that conjugating Gag to the TLR7 agonist R-848 enhanced Gag-specific Th1 and CD8 responses in mice [40] . It remains to be determined , however , whether either of these approaches will enhance protection from HIV infection . B cell vaccines and T cell vaccines have by themselves been unsuccessful in eliciting protection from HIV infection . For F-MLV , Messer and coworkers found that neither vaccination with the cell line FBL3 that expresses the viral protein GlycoGag to stimulate T cell responses nor passive transfer of neutralizing antibodies is sufficient to elicit sterilizing immunity [38] . However , the combination of immune serum and the FBL3 T cell vaccine did result in sterilizing immunity . The issue of why HIV infected patients fail to develop an effective neutralizing antibody response remains unresolved . If TLR signaling is involved in the antibody response to HIV , it is conceivable that viral disruption of TLR signaling could contribute to the failure of this response . However , virus specific antibodies are not absent in HIV infected persons , but they lack sufficient neutralizing activity to control the viremia [41] . Our findings indicate that Myd88 plays a fundamental role in the generation of a total retroviral antibody response rather than specifically neutralizing antibodies . In summary , these results demonstrate a critical role for DCs and TLR signaling in the humoral immune response to retroviral infection , and provide the first in vivo examination of the role played by TLRs in immune control of retroviral pathogens . Understanding precisely how TLR signaling contributes to the anti-retroviral immune response will help to guide the development of vaccines for retroviral pathogens such as HIV .
Wild-type C57BL/6 mice were obtained from Taconic Farms . Myd88-deficient mice were a gift from R . Medzhitov and were backcrossed to the C57BL/6 background for 5 generations . The Myd88-deficient mice were originally developed in the laboratory of S . Akira ( Osaka , Japan ) . For all experiments , littermate controls were used . CD11c-DTR mice have been previously described [23] , and had been generated on C57BL/6 background . Ethical approval for mouse experiments was obtained from the NYU Medical Center Institutional Animal Care and Use Committee . F-MLV stocks were generated by infection of Balb/c mice with 5000 focus-forming units of F-MLV . At 14 dpi , a 10% spleen homogenate suspension was made and titered by focus-forming assay . For experiments , mice at 6–8 weeks of age were infected with 7000 focus-forming units of F-MLV by retro-orbital intravenous injection . Splenocytes in suspension were serially diluted and plated on a subconfluent monolayer of the F-MLV–susceptible Mus Dunni cell line . For platings , 5×101–5×106 splenocytes were added to 2×105 Mus Dunni cells in 6 well plates . After 3 days of co-culture in RPMI medium , the cells were fixed with methanol and stained for 2 hours with the monoclonal antibody mAb720 , which recognizes the F-MLV envelope glycoprotein . The cells were washed three times with phosphate-buffered saline and stained with anti-mouse IgG1 horseradish peroxidase for one hour . Foci were then visualized by adding Amino ethyl Carbazole/Hydrogen peroxide substrate ( Sigma ) . Wild-type C57BL/6 mice at 6–8 weeks of age were lethally irradiated with two doses of 550 Rads separated by six hours and then injected intravenously with bone marrow cells from wild-type or CDllc-DTR transgenic mice . The chimeras were then kept for 6 weeks to permit engraftment of donor bone marrow . During this period , the mice were supplied with antibiotics in their drinking water to prevent infection . Depletion of CD11c+ dendritic cells was carried out by fifteen days of daily intraperitoneal injection of 200 ng of diphtheria toxin ( Sigma ) . Splenocytes were plated in RPMI containing 50 ng/mL PMA and 1 uM ionomycin plus GolgiStop ( BD Biosciences ) . After 3 h stimulation , cells were washed in PBS and stained for cell surface markers . The cells were then washed in PBS and resuspended in permeabilization-fixing buffer ( BD Biosciences ) . After incubation at 4°C for 45 minutes , the cells were washed and resuspended in permeabilization/wash buffer containing anti-IFNγ-PE antibody . Staining was carried out at for 45–60 min at 4°C . Finally , the cells were washed in 1–2 mL BD perm/wash buffer and resuspended in PBS for analysis . To measure neutralizing antibody titers in infected mice , peripheral blood was obtained by retro-orbital bleed , and serum extracted by centrifugation . The serum was diluted in a two-fold dilution series , and mixed with an F-MLV sample before plating on Mus Dunni cells . Three days later , when the Mus Dunni cells were confluent , infected foci were visualized . The neutralizing antibody titer was defined as the maximum serum dilution that reduced the number of F-MLV foci by at least 75% . Serum samples from infected or naïve mice were diluted in phosphate buffer saline with 2% fetal bovine serum . Samples were then incubated with a suspension of the chronically F-MLV–infected cell line NRK/SFFV-57 on ice for 45 minutes to capture F-MLV–specific antibodies . 100 , 000 cells were used per staining . The cells were then washed twice and resuspended in PBS/FBS . Secondary staining using Allophycocyanin-conjugated anti-mouse IgG ( eBiosciences ) was performed , then the cells were analyzed by flow cytometry . To determine statistical significance , P values for specific experiments were calculated using the Student's T- test for Figures 3B , 4A , and 7 , and by Mann-Whitney test for Figures 3A , 4B , 8 , and 10 . | Efforts to develop vaccines against the retrovirus HIV by inducing immune responses involving antibodies or T cells have been unsuccessful . Although antibodies can be generated against HIV , they fail to neutralize the virus . Thus , a more fundamental understanding of how neutralizing antibody responses to retroviral pathogens are generated is required . We have used a mouse retrovirus to demonstrate that Myd88 , a molecule centrally involved in innate immune system signaling , is required to generate an antibody response during retroviral infection . Myd88 also contributed to , but was not strictly required for , the T cell response . Myd88 is known to participate in a signaling pathway that activates inflammation in response to microbial molecules . Understanding how this pathway contributes to anti-retroviral antibody responses may be useful for the development of a vaccine that can effectively block HIV . | [
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] | 2009 | Myd88 Is Required for an Antibody Response to Retroviral Infection |
Budding yeast , which undergoes polarized growth during budding and mating , has been a useful model system to study cell polarization . Bud sites are selected differently in haploid and diploid yeast cells: haploid cells bud in an axial manner , while diploid cells bud in a bipolar manner . While previous studies have been focused on the molecular details of the bud site selection and polarity establishment , not much is known about how different budding patterns give rise to different functions at the population level . In this paper , we develop a two-dimensional agent-based model to study budding yeast colonies with cell-type specific biological processes , such as budding , mating , mating type switch , consumption of nutrients , and cell death . The model demonstrates that the axial budding pattern enhances mating probability at an early stage and the bipolar budding pattern improves colony development under nutrient limitation . Our results suggest that the frequency of mating type switch might control the trade-off between diploidization and inbreeding . The effect of cellular aging is also studied through our model . Based on the simulations , colonies initiated by an aged haploid cell show declined mating probability at an early stage and recover as the rejuvenated offsprings become the majority . Colonies initiated with aged diploid cells do not show disadvantage in colony expansion possibly due to the fact that young cells contribute the most to colony expansion .
Budding yeast Saccharomyces cerevisiae has been an ideal model system to study many biological processes crucial to the development of uni-cellular or multi-cellular organisms , such as cell polarization , cytokinesis and cell aging . It became a favorable model system because of its experimental tractability and the existing extensive studies over the decades . Yeast cells exist in haploid and diploid forms and they form colonies via sexual or asexual reproduction depending on the environmental cues [1] . Both haploid and diploid yeast cells can reproduce asexually by budding , in which a small bud emerges from the mother cell , enlarges until reaching a certain size , and then separates from the mother cell . The haploid cells have two mating types a and α , and they mate with their mating partners of the opposite mating type to form a diploid cell of type a/α . Under extreme conditions such as stress or starvation , diploid cells can undergo sporulation , by entering meiosis and producing four haploid spores [1 , 2] . The life cycle of budding yeast is illustrated in Fig 1 . Yeast budding is an important process to understand cell polarization and symmetry breaking . Studies using both experimental or modeling approaches have been extensively conducted on yeast budding [2–5] During budding , a new daughter cell emerges from a mother cell through polarized cell growth [2] . Haploid cells bud in an axial manner in which both mother and daughter cells have their bud sites adjacent to their previous division sites; diploid cells bud in a bipolar manner in which mother cells have a new bud site either adjacent or opposite to the previous division site , whereas daughter cells mostly choose a new bud site opposite to their birth site [1 , 2 , 6] . This budding event involves a key polarized protein , Cdc42 GTPase , which is highly conserved from yeast to human and plays a central role in polarity establishment [7–9] . Cdc42 localizes and interacts with other players in the pathway that ultimately lead to polarized growth and the emergence of a bud [2 , 4] . This polarization is oriented by spatial cues that are distinct in each cell type [6]: proteins such as Bud3 and Bud4 are thought to function as a transient spatial cue in the axial budding pattern [10 , 11] , while Bud8 and Bud9 are the spatial cues in the bipolar budding pattern [12] . Previous efforts have been made to understand bud emergence at the molecular and mechanistic level; however , not much is known about why the haploid and diploid cells bud in different patterns . A long-standing speculation is that different budding patterns give rise to different biological functions specific to each cell type [3 , 4 , 13]: the axial budding pattern may facilitate mating by generating a tighter cluster of cells with opposite mating types , and the bipolar budding pattern may maximize the expansion of the colony , allowing a wider nutrient search in new territory . An interesting feature of haploid budding yeasts is their ability to switch the mating type . Homothallic haploid yeast strains are able to switch between two mating types during mitotic growth [1 , 14] and generate a colony that is a mixed population of both haploid and diploid cells . Mating type switch has an advantage of allowing haploid cells to change their mating type in daughter cells to generate a compatible mating partner , but it may come at a cost of forming diploid cells between closely related cells ( mother-daughter or siblings ) , resulting in inbreeding which reduces genetic variation and fitness of offsprings [1] . How the cells balance these benefits and costs from mating type switch is yet unclear and could be related to the frequency of mating type switch [1 , 14–16] . An unavoidable factor affecting all the processes discussed above , as well as almost all the other biological functions , is aging . Budding yeast renders itself as a useful tool to study the evolutionary conserved aspects of eukaryotic aging [17] . Individual yeast cells divide limited times before they die , and the number of cell divisions is defined as their replicative age [18] . It is known that certain cellular functions or processes are associated with replicative age , for example , the mortality rate , cell cycle length , cell size and the sensitivity to environment ( such as response to mating pheromone and nutrients ) [18] . It was also observed that the cellular spatial order declines with replicative age , and interestingly , by tracking individual yeast cells , experiments showed that the probability of normal budding decreases with age [19 , 20] . While it is still elusive whether the change of budding pattern is the cause or consequence of aging , a natural but unanswered question is how this loss of correct orientation in old cells impacts the colony at the population level . Mathematical modeling has served as a useful tool to successfully address many important questions regarding cell polarization . Similar to the previous experimental works on yeast , most modeling works for budding yeast are on the molecular level to understand the pathways and mechanisms in cell polarization [5 , 21 , 22] . Modeling works that study yeast from the population point of view is very limited . In [23] , an agent-based model was proposed to study the effects of different budding patterns and growth inhibition ( induced by crowding effect ) on colony morphology at the single-cell level . Their simulations demonstrated that growth inhibition and polar budding pattern are the most significant factors driving colony expansion . In [24 , 25] , agent-based models were proposed to simulate yeast colony growth , which includes a size-controlled module to govern cell proliferation and a cell-cell interaction module to arrange spatial positions of cells; the authors discussed the influence of cell-cell cohesion force and budding patterns on the colony shape and size [24] , and they studied a variety of diameter growth time and reproduction time to better match the exponential growth in experiments [25] . However , the studies in [23–25] did not consider the intrinsic difference between budding patterns of haploid and diploid cells , nor did they discuss how budding patterns and cell types affect the growth of colonies . In addition , the existing models did not include the interaction between cells and their living environment . In this paper , we present a novel and more comprehensive agent-based type model to study how the budding patterns in yeast cells affect colony growth . Our model incorporates many important biological processes in yeast cells , colony spatial arrangement through cell-cell mechanical iterations , and cell-environment interactions . To be more specific , the key biological processes include budding , mating , mating type switch , changes in cell cycle length and cell size due to aging , and cell death; cell-cell mechanical interaction is modeled through a contractive component due to cell adhesion and a repulsive component due to elastic compression [26–29]; a nutrient field is introduced and the nutrient is consumed by cells while growth inhibition is induced if the nutrient level is too low . Our major findings include that ( 1 ) mating type switch frequency controls the trade-off between diploidization and inbreeding; ( 2 ) axial budding pattern in haploid yeast cells facilitates mating at an early stage of colony expansion; ( 3 ) bipolar budding is necessary for a branched colony under limited nutrient; ( 4 ) mating efficiency is lower in aged colonies but colony expansion does not depend on the overall age of the colonies . It is worth remarking that our modeling framework is not restricted to budding yeast and could be applied to study other systems , such as fungi , bacteria and stem cells . The paper is structured as follows . A detailed description of the model is given in Models Section . In Results Section we present and analyze the results . Conclusions and discussions are given in Discussion Section . Supporting figures and texts can be found in Supporting Information .
The lifespans of yeast cells can be measured by either their replicative potential ( replicative lifespan ) or the maximal survival time of a non-dividing cells ( chronological lifespan ) [17 , 18] . In the literature both lifespans are used to study different aspects of aging: the replicative lifespan is associated with the total number of cell division , and the chronological lifespan is related to the physical time . Only replicative lifespan is considered in this paper because we are mainly interested in the budding event and budding patterns . It is known that during budding , yeast cells undergo asymmetric division , in which mothers give rise to daughter cells with full lifespan capacity [18] . Therefore , in our model , upon cell division the age of the mother cell is increased by one , while the initial age of daughter cell is set to be zero . In experiments , the replicative lifespan is measured by counting the number of total bud scars [19 , 20] , and the average replicative age is approximately 30–50 cell divisions [20] . The death probability , denoted by Pd ( a ) , represents the probability that a cell with age a dies before reaching age a + 1 . Although this important quantity is not directly observable , its companion , the survival fraction Fs ( a ) of the population , can be measured in experiments . Since the survival fraction Fs ( a ) can be viewed as the probability that cells survive up to an age greater than a , Fs ( a ) and Pd ( a ) are related by the following formula F s ( a ) = ( 1 - P d ( a ) ) F s ( a - 1 ) for a ≥ 1 , with Fs ( 0 ) = 1 − Pd ( 0 ) . Thus F s ( a ) = ∏ i = 0 a ( 1 - P d ( i ) ) , for a ≥ 1 . As cells bud many times , their death probability becomes higher [18] . In our model the death probability Pd ( a ) is assumed to take the following form P d ( a ) = 1 - e - k 1 a , ( 1 ) and thus the corresponding survival fraction is F s ( a ) = e - k 1 ∑ i = 0 a i = e - k 1 a ( a + 1 ) 2 , ( 2 ) which is a sigmoid function . This is consistent with the shape of survival curves measured in experiments , regardless of the cells’ genetic background [18] . Previously , Jazwinski and Wawryn [20] measured the survival fraction of a population of haploid yeast cells through different ages . Using the experimental data in [20] , the value of k1 is estimated to be 0 . 006 for haploid cells . As for diploid cells , there is no available data of survival fraction to our knowledge; however , it has been reported that diploid cells are longer-lived than haploid cells [32] , so k1 = 0 . 004 is chosen for diploid cells . These estimated survival fractions are shown in S1A Fig . Yeast cells undergo polarized division by budding at specific sites determined by their cell types . Wild type haploid cells bud in an axial manner: mother cells form new buds adjacent to previous bud site and daughter cells bud next to their birth site . On the other hand , wild type diploid cells bud in a bipolar manner: mother cells can choose a new bud site either adjacent ( proximal pole ) or opposite to ( distal pole ) the previous bud site and daughter cells bud at the site opposite to the birth site ( distal pole ) [10–12] . The schematic diagram of bud sites is shown in Fig 2C . Interestingly , as a cell ages , its budding pattern , a representation of the cellular spatial order , appears to be disrupted with a manifestation of budding randomly at a higher frequency [19 , 20] . Based on single-cell observations in [19] , which tracked the budding patterns throughout lifespans of cells , we assume the probability of random budding to be an increasing function of the replicative age a: P b ( a ) = 1 - e - k 2 a , ( 3 ) where k2 is estimated to be 0 . 024 . The estimated probability of random budding for haploid cells is shown in S1B Fig . Due to the lack of experimental data , diploid cells are assumed to have the same random budding probability as haploid cells . Morphological and physiological changes were observed during the aging process of yeast [17 , 18] . For example , cell size and cell cycle length were shown to gradually increase with successive divisions [18] . It was shown in [31] that the average radius of diploid cells increases from 3 . 5μm to 5 . 5μm from birth to death . Cells of the first generation are usually small and require a long cell cycle to reach a critical size to bud . To simplify the calculation , we skip the growing process when daughter cells are attached to the mother cells . We model daughter cells after they detach from the mother cells and set the initial size to be nonzero . In our model , the radius of a newborn diploid cell is set to be 3 . 5μm and increases by 1 a d at each division after its cell size reaches the critical size 4 . 375μm . Thus , the radius function for diploid cells can be formulated as r d ( a ) = { 3 . 5 if a = 0 , 4 . 375 + a / a d if a > 0 , ( 4 ) where ad = 30 . Since the average cell size of diploid cells is approximately 1 . 25 times that of the haploid cells [30] , the radius of a newborn haploid cell is assumed to be 2 . 8μm and increases by 1 a h at each division after its cell size reaches the critical size 3 . 5μm . Similarly , the radius function for haploid cells takes the form r h ( a ) = { 2 . 8 if a = 0 , 3 . 5 + a / a h if a > 0 , ( 5 ) where ah = 25 . The comparison of changes in cell size with respect to age is shown in S1C Fig . Cell cycle lengths of budding yeast cells have been shown to increase with age [18] , with possibly the exception of newborn cells because they have a longer G1 phase before the initiation of budding . While cell cycle lengths vary from cell to cell and depend on the strain background and growth media , the model is simplified by ignoring the variations among the cells and the stochasticity due to other factors . In addition , the cell cycle lengths of haploid and diploid cells are assumed to be the same because no significant difference in average generation time has been observed in experiments [30] . Thus , for both types of cells , the cell cycle length λ ( a ) is modeled as λ ( a ) = { g 0 if a = 0 , g 1 g 11 + g 12 / ( a + 1 ) if a > 0 . ( 6 ) Based on the experimental data in [30 , 31] , the parameters are chosen as follows: g0 = 101 . 25 mins , g1 = 90 mins and g 11 = g 12 = 2 3 . The estimated curve for cell cycle length is shown in S1D Fig . Mating is a process in which a haploid a cell and a haploid α cell come into physical contact and once successful , these two cells fuse into a diploid a/α cell . It is known that haploid cells of opposite mating types tend to mate to form a diploid cell [33] . Yeast cells select their mating partners and preferentially mate with the cell that produces the highest level of pheromone [34 , 35] . However , cells become less sensitive to mating pheromone and become sterile as they grow old . Experiments showed that the frequency of successful matings dropped significantly when one mating partner was relatively old [18 , 35] . Interestingly , when cells of different ages mate , the replicative age of the zygote is set to be that of the older haploid cell , indicating that age is a dominant phenotype [35] . Within a colony whose haploid cells have one dominant mating type , the chances of forming a diploid cell can be enhanced by mating type switch , a process in which haploid a cells and α cells switch their mating types . The ability to switch mating type is restricted to cells that have budded at least once [1 , 14] . This process is regulated by the HO gene , which may be activated in mother haploid cells during the G1 phase [16] . Mating type switch is not a rare event: previous experiments suggested that mating type switch occurs with a high frequency , usually greater than 50% [1] . Hence even if a colony starts with one single haploid cell , both a and α cells will be present in that colony . The high frequency of mating type switch and the tendency of mating between haploid cells with opposite mating types result in the prevalence of diploid cells in a colony . On the other hand , the high frequency of mating type switch may also lead to inbreeding , which reduces genetic variation [14–16] . Based on biological observations , the following simplified rules are used during the mating process in our model ( Fig 2D ) : ( i ) if two haploid cells of opposite mating types are in direct contact , the frequency of successful mating Pm will drop as cells age: P m ( a ) = 0 . 75 - a k 3 , ( 7 ) where k3 is estimated as 80; ( ii ) newborn cells are not allowed to switch mating types , while experienced cells have a constant mating type switch frequency Ps; ( iii ) cells will preferentially choose the youngest of neighbors of the opposite type to mate; ( iv ) a newly formed diploid cell has a circular shape and has the same age as the older haploid cell prior to mating , and its volume is the sum of two mated cells; ( v ) inbreeding is defined as mating between mother and daughter cells or among siblings . The growth of individual yeast cells and the expansion of the colony depend on nutrient supply . Decrease in nutrient concentration will slow down cell growth by prolonging cell cycle length [36 , 37] . The level of nutrient also affects cell-cell adhesion and cell-media adhesion because nutrient depletion may activate certain genes to express corresponding cell-wall proteins that are essential for cell filamentous growth [26] . In our model , a nutrient field u , as a function of space x → and time t , is introduced across the domain . The change of nutrient concentration is due to diffusion of nutrient and consumption by live cells . The consumption rate is assumed to be highly localized around cells and decreases exponentially with the distance from cells . The dynamics of nutrient concentration is described by ∂ u ∂ t = D Δ u - ∑ k = 1 N ( t ) c r e - | x → - x → k | c d u , ( 8 ) where x → k denotes the coordinates of the center for the k-th cell , N ( t ) is the total number of cells at time t , cr is the consumption rate of a single cell , and cd controls the degree of local consumption . The diffusion coefficient D is selected depending on the growth media: larger values of D for more liquid media and smaller values for more solid media . The initial nutrient field is assumed to be homogeneous with a value U0 . To account for the growth inhibition induced by nutrient depletion for each individual cell , the cell cycle lengths are assumed to depend on local nutrient concentration uloc , and therefore g0 and g1 in Eq ( 6 ) are replaced by g0 f ( uloc ) and g1 f ( uloc ) , respectively , where f is a decreasing function . It is reasonable to assume that f is 1 when nutrient is rich , decreases slowly under nutrient consumption , and drops rapidly when the nutrient supply is very limited . Therefore in our model f is defined by f = max{f1 , 1} , where f 1 ( u l o c ) = { c u 1 - c u 2 u l o c if u t h d ≤ u l o c ≤ U 0 , c u 3 - c u 4 log ( u l o c ) otherwise , where c u 1 , c u 2 , c u 3 , c u 4 and uthd are constants . In the numerical implementation , the average local nutrient concentration for a cell centered at x → with radius r can be approximated by u l o c ( x → ) ≈ 1 n ∑ m u m ( 1 - H ( | x → m - x → | / r - d n u r ) ) , where um is the nutrient concentration at a grid point x → m , dnur is the range that a cell can sense nutrient , n is the total number of m such that | x → m - x → | / r ≤ d n u r , and H denotes the Heaviside function . As a cell grows older , its response to environment becomes less sensitive . To model this effect , the sensitivity coefficients to nutrient , c u 2 and c u 4 , are assumed as decreasing functions of age of the following form c u i ( a ) = c u i 1 + a / k 4 , where c u i and k4 are constants and i ∈ {2 , 4} . The values of these parameters are shown in Table 1 . In this paper , we used the off-lattice modeling approach , in which the positions of cell are not confined on mesh grids and the colony spatial arrangement is completely determined by budding , mating ( haploid ) and cell-cell interactions . This modeling approach gives more freedom and higher accuracy to model the location of new bud in different budding patterns and study how they affect colony formation , which is the primary subject in our paper . The spatial distribution of a colony of cells depends on the response of cells to forces exerted by their neighboring cells . For example , yeast cells in physical contact can form adhesive bonds , which result in adhesive force , by certain proteins located on the surface of cell walls [26 , 27] . Yeast cells can also resist the compression by other cells due to the incompressibility of their cell wall [28 , 29] . Many models are proposed to model cell-cell adhesive and repulsive forces [23 , 38–40] . The repulsive force is mainly designed to avoid overlap between two agents in the model . Experiments have shown that that linear elastic constitutive equation can be used to describe cell wall material of a yeast cell [28 , 29] . In this paper , forces between cells are modeled by linear contractile-repulsive springs as in [23] . Consider the i-th cell centered at x → i . The repulsive force between the i-th cell and the j-th cell centered at x → j is assumed to increase with the overlap Δd = ( ri + rj ) − dij , where d i j = | x → i - x → j | and ri , rj are the corresponding radii of the cells . Then this repulsive force is given by F → r i , j = { k r Δ d v ^ i , j if d i j < r i + r j , 0 otherwise , where kr is a spring stiffness constant and v ^ i , j = x → i - x → j d i j is the unit vector in the direction of x → i - x → j . The adhesive force is assumed to be proportional to the overlap between a cell and its neighbors , and is defined by F → a i , j = { - k a Δ d v ^ i , j if d i j < r i + r j , 0 otherwise , where ka is a spring stiffness constant . Thus the overall force exerted on the i-th cell centered at x i → is given by F → i = ∑ j = 1 , j ≠ i N ( t ) ( F r i , j + F a i , j ) . According to the Newton’s second law , the acceleration a → i is proportional to force F → i . Assuming that initial velocity of the cell is 0 , the instantaneous velocity is V → i = a → i Δ t and V → i = α F → i Δ t , where α is taken to be constant for simplicity . Thus the current position x → i n + 1 of the i-th cell can be approximated by x → i n + 1 = x → i n + V → i Δ t for n ≥ 0 , where x → i n denotes the previous position of the i-th cell , and the time step Δt is chosen as 1 . 8 mins in our simulations , which is sufficiently small compared to cell cycle length .
Mating type switch allows haploid cells to divide and change their mating type to generate a compatible mating partner . A single homothallic haploid cell will generate a colony with a mixed population , which contains both diploid a/α cells and haploid cells of a and α types . Diploidization is advantageous because diploid cells are better than haploid cells at coping with DNA damage . However , mating type switch is likely to come with a cost [15] . For example , mating type switch may cause replicative delays , and the presence of switching mechanisms increases DNA replication errors . In addition , the formation of diploid a/α cells from closely related cells ( mother-daughter or siblings ) results in inbreeding and reduces the genetic variation , which is the primary selective force . While many homothallic strains of yeast cells switch their mating type at a very high frequency ( about 70% of the total cell divisions ) , it is not understood why the switch does not happen at an even higher frequency , such as 100% [1 , 14 , 16] . To study the benefit and cost of mating type switch in budding yeast colonies , we examined two corresponding indicators: the percentage of diploid cells and the percentage of inbreeding among all mated pairs . In our simulations , three frequencies , 50% , 70% and 90% , of mating type switch were tested based on a simple setting: a colony starts with a single haploid a cell , and this cell will bud and its offsprings are likely to switch their mating types , which eventually leads to mixed types of cells ( a sample colony is shown in S2 Fig ) . 1000 samples were simulated for each mating type switch frequency and the statistics are summarized in Fig 3 . It can be seen that , in wild type axial budding haploid cells , higher mating switch frequency leads to higher percentage of diploid cells ( Fig 3A left panel ) but meanwhile also leads to higher percentage of inbreeding ( Fig 3A right panel ) . Fig 3B shows that this observation does not depend on the budding pattern of these haploid cells , and the same conclusion also holds for random budding haploid cells ( new buds emerging at random positions ) . These results reveal the trade-off between diploidization and inbreeding controlled by the frequency of mating type switch , and may explain why the mating type switch frequency for wild type cells is approximately 70% instead of 100% . In the remainder of this paper , the mating type switch frequency is set to be 70% , unless otherwise specified . Different budding patterns are thought to contribute to different biological functions specific to each cell type [3 , 4 , 13] . Some researchers believe that axial budding pattern helps generate a tighter cluster of cells and facilitates the mating of haploid cells of opposite mating types to form diploid cells . Using our model , this hypothesis was tested by comparing colonies with axial budding haploid cells ( new buds emerging adjacent to the previous bud site ) and colonies with random budding haploid cells ( new buds emerging at random positions ) . The time of first mating and the percentage of diploid cells in a colony were used as a quantitative measure to assess the mating efficiency . In 1000 simulations performed for each budding pattern , we found that although the total populations for both budding patterns show exponential growth , these two budding patterns lead to significantly different mating features . On average , the first mating happens earlier in the axial budding colonies as shown in Table 2 . Moreover , the axial budding colonies show significantly higher percentage of diploid cells than the random budding ones ( Fig 4A ) . At 12 hours , over 70% of the population are diploid cells in the axial budding colonies , while the percentage for the random budding colonies is only 50% . These results support that axial budding pattern facilitates mating , especially at an early stage of colony growth . Another interesting and unexpected result from the simulations is that axial budding colonies show significantly lower percentage of inbreeding during the early stage of colony development , compared to random budding colonies with the same mating type switch frequency ( Fig 4B ) . At 4 . 5 hours , among all pairs of mated cells , about 65% are mother-daughter or siblings in random budding colonies , and this percentage is only around 45 for axial budding colonies . However , this difference becomes smaller as colonies grow . It was hypothesized that bipolar budding is important for maximizing the spread of a colony to reach out for nutrient in new territory [3 , 4 , 13] . Diploid cells require the BUD8 protein for bipolar budding: bud8Δ mutants do not bud in a bipolar manner but instead bud adjacent to their birth scars in a pattern similar to haploid axial budding [6] . By comparing wild type cells to bud8Δ mutants , it was shown that bipolar budding is necessary for colony spread and agar invasion [41] . To understand the relationship between budding patterns in diploid cells and the spread of colonies , colonies with bipolar and random budding diploid cells were studied via our model . In order to assess the differences of these colonies , two indicators that measure the overall spread were introduced . The first indicator , called the colony radius and denoted by R , is the radius of the minimal covering circle of the colony ( see S3 Fig for illustration ) . Larger colony radius implies a wider spread and higher efficiency in nutrient search . The second indicator , called the colony sparseness and denoted by σD , is defined as the ratio between the area of the minimal covering circle and the total actual area of the colony: σ D = π R 2 area of the colony , where the actual area of the colony is the sum of areas of all cells . Since most of the cells do not overlap with each other in the early stage of colony formation , the sum of cell areas can be calculated according to the cell radii . Larger colony sparseness implies sparser distribution of cells inside the minimal covering circle and less competition for nutrient from neighboring cells . In the simulations , these two types of colonies start with four diploid cells , and will contain only diploid cells because sporulation is not considered in this paper . For each of the situations under different budding patterns and initial nutrient settings , 1000 samples were simulated and data are recorded when colonies grow to 25 , 50 , 75 , 100 , 125 and 150 cells ( shown in Fig 5A ) . When the initial nutrient is abundant ( by setting U0 = 2 ) , for both bipolar and random budding colonies , the colony radius increases on average as colonies grow . The bipolar budding colonies have , on average , slightly larger colony radius and sparseness than the random budding ones ( Fig 5A ) . However , these advantages for the search of nutrient is gradually lost as colonies grow . Our simulations also agree with the modeling results in [23] , which suggested that simply switching budding pattern from non-polar to polar does not necessarily lead to significant increase in colony size . Since only little advantage of the bipolar budding pattern was observed under rich nutrient condition , we tested a decreased initial nutrient level U0 = 1 , which represents a poor nutrient condition . Fig 5B shows that for bipolar budding colonies , when the initial nutrient level U0 decreases , both the colony radius and sparseness increase on average . With rich initial nutrient U0 = 2 , the colony radius on average increases about 150% as the population grows from 25 cells to 150 cells , while the colony sparseness remains almost constant; on the other hand , when the initial nutrient level is lowered to U0 = 1 , the increase in colony radius is over 200% , and the colony sparseness also increases more than 40% . These observations support that the bipolar budding pattern enhances colony development through better nutrient search . Another noticeable observation is that the curves of colony radius and sparseness have shifted significantly as U0 decreased from 1 . 1 to 1 , suggesting the possible existence of certain threshold of nutrient , below which the bipolar budding is far more advantageous . However , the situation is different for the random budding colonies . As shown in Fig 5C , nutrient limitation does not cause an active spread of the colony and there is little increase in colony radius and sparseness with various U0 . Previously , Jönsson and Levchenko Jonsson2005 found that the colony area increased by 20% when neighbor inhibition varied from weak to strong , even when all divisions were non-polar . This difference between our results and theirs in [23] may be explained by the different approaches used to model the growth inhibition: in [23] the growth inhibition was modeled to be proportional to the number of neighboring cells , while in our model the growth of the cells is inhibited as their surrounding nutrient is consumed by themselves and neighboring cells . This growth inhibition due to nutrient consumption is negligible under rich nutrient condition but will be more pronounced when the overall nutrient is limited . Besides the measures of colonies , we are also interested in the actual colony morphology . Under rich nutrient condition U0 = 2 , colonies of both budding patterns exhibit approximately round shape except that the peripheries of the bipolar budding colonies are not as smooth and have lightly irregular extensions ( Fig 6A and 6C ) . Under poor nutrient condition U0 = 1 , bipolar budding colonies have a tight core and finger-like branches to reach out for nutrient ( Fig 6B ) , while random budding colonies are still relatively compact with emerging small branches at the periphery ( Fig 6D ) . More samples of colony morphology are shown in S4 Fig . Our simulations suggest that only bipolar budding cells give rise to a colony morphology with finger-like extensions under limited nutrient . Our results are consistent with the previous experiments of the lab strain S288C , which showed smooth colony structure and was specifically selected to be non-flocculent with a minimal set of nutritional requirements [41 , 42] . It is known that cellular functions decline as a cell ages . As consequences or causes , increased cell size , cell cycle length and death probability , disruption of regular budding pattern , as well as lower sensitivity to mating pheromone are manifestations of aging . Experiments have shown that daughter cells from older mothers have shorter lifespan [18] . However , granddaughters of an old mother cell show a gradual restoration to a normal lifespan , suggesting that some aging factors might be diluted to recover rejuvenation [18] . To accurately reflect the effect of the ages of mother cells as observed in biological experiments , we included a variable a representing age in some probabilities or parameters introduced in Models Section: ( i ) Daughter cells born from older mothers have higher death probability and are more likely to bud randomly . Accordingly Eqs ( 1 ) and ( 3 ) are modified to: P d ( a , a M ) = 1 - e - k 1 ( 1 + a M / c 1 ) a and P b ( a , a M ) = 1 - e - k 2 ( 1 + a M / c 2 ) a , where aM is the age of the mother cell . ( ii ) The initial size of a daughter cell increases with the age of its mother: for diploid daughter cells , Eq ( 4 ) is changed to 3 . 5 ( 1 + aM/c3 ) μm , and for haploid daughters , Eq ( 5 ) is modified as 2 . 8 ( 1 + aM/c4 ) μm . The initial cell cycle length decreases with the age of its mother and g0 in Eq ( 6 ) is modified to be g0/ ( 1 + aM/c5 ) . ( iii ) The frequency of successful matings of haploid cells is influenced by the ages of their mother cells , so that Eq ( 7 ) is modified to P m ( a , a M ) = 0 . 75 - ( a k 3 + a M c 6 ) . Here c1 , c2 , … , c6 are scaling parameters . First , we tested how aging affects yeast colony by comparing two colonies: colonies initiated by a single haploid cell of age 0 ( referred to as young colonies ) , and colonies initiated by a single haploid cell of age 30 ( referred to as old colonies ) . Within each of these two colonies , both axial budding and random budding patterns are considered . It can be seen in Fig 7A that young colonies always have significantly larger populations than old colonies regardless of the budding patterns of the haploid cells . This result is expected , as the death probability increases with replicative age and the offsprings of old mothers need a few generations to fully rejuvenate . Fig 7B shows that mating occurs later in old colonies compared to young colonies: at 4 . 5 hours , the diploid cell percentage of old colonies is almost zero , while that of young colonies is over 20% on average . Fig 7B also shows that the old colonies tend to have a lower percentage of diploid cells , indicating lower mating efficiency in old colonies . Interestingly , although young colonies generally have higher mating efficiency , axial budding in haploid cells still shows its advantage: as time approaches 10 hours , the diploid cell percentage in an old colony with axial budding haploid cells ( cyan bars in Fig 7B ) exceeds that in a young colony with random budding haploid cells ( gray bars in Fig 7B ) . Young and old colonies initiated by four diploid cells of age 0 and of age 30 were also studied under limited nutrient . The first observation is that old colonies need a longer time to reach a certain population size , since offsprings of old mothers need several generations to rejuvenate . Fig 7C shows that if diploid cells bud in a bipolar manner , old colonies exhibit slightly larger colony radius and sparseness on average than young colonies; however , greater variance and more outliers are observed in old colonies . This may be explained by that most of the cells in a colony are young cells , and colony expansion is due to cell divisions at the periphery , which is also occupied by young cells ( old cells reside in the core of the colony ) . On the other hand , if diploid cells bud in a random manner , almost no difference was observed between young and old colonies ( Fig 7D ) . In summary , our results suggest that the age of the colony determines the mating efficiency and how quickly the cell population increases , but does not affect the overall spatial distribution of cells .
In this paper , a two-dimensional agent-based model was developed to study budding yeast colonies with cell-type specific biological processes . Our model considers processes such as budding , mating cell death , consumption of nutrient and mating type switch . We investigated the roles of budding patterns , mating type switch frequency and growth inhibition induced by nutrient depletion in yeast colony development . Our findings reveal that axial budding pattern enhances mating efficiency at an early stage of colony development , and bipolar budding pattern improves colony expansion under nutrient limitation . Our results also suggest that mating type switch frequency might control the tradeoff between efficient diploidization and inbreeding . The effect of cellular aging was also studied . Based on the simulations , colonies initiated by an aged haploid cell show declined mating probability in an early stage of colony development but later recover as the rejuvenated offsprings become the majority . It was also shown that colonies initiated by aged diploid cells do not show disadvantage in colony expansion due to the fact that young cells contribute the most to colony expansion . Our model can be extended to take into account intracellular signaling pathways and cellular responses . For simplicity and due to the lack of sufficient information , our model focused on a set of conceptual agent-based rules based on statistical results of experimental observations . However , one may also include the Cdc42 pathway or the cell cycle pathway for each individual cell to achieve a more realistic model . Another possible direction to extend our model is to include more detailed morphological changes induced by mating pheromone or nutrient depletion . In the current model , mating is only allowed when two cells of opposite types have direct contact , while in reality , cells may be able to sense mating pheromone over a longer distance and make projection toward the mating partners [33] . A more realistic field of mating pheromone described by reaction-diffusion equations as in [43] could be included . Elongated cellular morphology due to nutrient depletion may also be considered . The incorporation of cell morphological change may need to shift the current simple computational approach to a more involved numerical method , such as the level set or phase field methods [44] , and the computational cost will increase drastically . Therefore , a more feasible first step would be using the current framework but making the directional growth possible as in [45] , without considering the detailed cell shape change . One major limitation of the agent-based approach is that the cell population size is restricted to a relatively small scale due to a high computational cost . For a relatively large population , it is often beneficial to consider on-lattice agent modeling or continuous model governed by continuum equations [46 , 47] . However , these modeling approaches cannot capture some important biological phenomena at the small population scale , as studied in this paper . In conclusion , our model is simple , but captures many essential characteristics of yeast colony development and our statistical results show good agreement with previous experiments and have verified some existing hypotheses . It can be extended to further understand the development of yeast colonies . It is worth noting that the model proposed here can serve as a framework to study multicellular organisms , especially systems such as tissues with stem cell lineage [48–50] . | Budding yeast is a model organism in understanding fundamental aspects of eukaryotic cells , such as cell polarization and cell aging . Previously , extensive research has focused on the molecular mechanisms of biological processes in yeast , but many questions regarding yeast budding remain unsolved . For example , how do different budding patterns affect yeast colony growth ? How does declined spatial order due to aging impact the colony at the population level ? To address these questions , we developed a computational agent-based model , which incorporates key biological processes , the effect of aging , as well as cell-environment interaction . We performed and analyzed a large number of simulations for a variety of situations , and obtained insightful results . We found that axial budding pattern enhances the percentage of diploid cells at early stage and bipolar budding pattern improves colony development under nutrient limitation; the frequency of mating type switch might control the trade-off between diploidization and inbreeding; aging affects the percentage of diploid cells in colonies initiated by a single haploid cell , but does not have much influence in the expansion of colonies initiated by diploid cells . The framework of the model can be extended to study other important systems , such as tissue with stem cell lineage . | [
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] | 2017 | A modeling study of budding yeast colony formation and its relationship to budding pattern and aging |
The presence of voltage fluctuations arising from synaptic activity is a critical component in models of gain control , neuronal output gating , and spike rate coding . The degree to which individual neuronal input-output functions are modulated by voltage fluctuations , however , is not well established across different cortical areas . Additionally , the extent and mechanisms of input-output modulation through fluctuations have been explored largely in simplified models of spike generation , and with limited consideration for the role of non-linear and voltage-dependent membrane properties . To address these issues , we studied fluctuation-based modulation of input-output responses in medial entorhinal cortical ( MEC ) stellate cells of rats , which express strong sub-threshold non-linear membrane properties . Using in vitro recordings , dynamic clamp and modeling , we show that the modulation of input-output responses by random voltage fluctuations in stellate cells is significantly limited . In stellate cells , a voltage-dependent increase in membrane resistance at sub-threshold voltages mediated by Na+ conductance activation limits the ability of fluctuations to elicit spikes . Similarly , in exponential leaky integrate-and-fire models using a shallow voltage-dependence for the exponential term that matches stellate cell membrane properties , a low degree of fluctuation-based modulation of input-output responses can be attained . These results demonstrate that fluctuation-based modulation of input-output responses is not a universal feature of neurons and can be significantly limited by subthreshold voltage-gated conductances .
Membrane voltage in cortical neurons is dominated by fluctuations mediated by random synaptic activity [1–4] . Because probabilistic threshold crossings associated with fluctuations lower spike threshold , enabling spike response to otherwise sub-threshold inputs [5 , 6] , it has been hypothesized that background activity amplifies neuronal sensitivity , and in doing so permits fluctuations to modify the input-output functions of neurons [7–12] . Consistent with this hypothesis , recordings in vivo often show a large variance in interspike intervals [13 , 14] . Spectral properties of voltage fluctuations are also correlated with different cognitive states , lending support to the idea that fluctuations play an important role in modulating spike output [4 , 15–17] . Finally , computational models suggest that neurons are sensitive to transient inputs and modulate their input-output function in response to changes in the size of membrane voltage fluctuations [10 , 18–20] . For two reasons , however , it is not clear that results of strong effects of membrane-potential fluctuations on input-output relationships hold in general . First , data supporting a strong relationship come from only a few types of neurons [8 , 11 , 21–23] . Second , even these restricted studies have shown significant variability in the magnitude of the effect [21 , 23–25] . These observations indicate a possible complex relationship between membrane voltage fluctuations and neuronal input-output modulation . Modulation of input-output responses is likely influenced by numerous factors , including sub-threshold voltage-dependent properties present in neurons . For example , the negative slope conductance associated with Na+ current , which increases membrane resistance in close proximity to spike threshold [26] , has been shown to reduce neuronal responsiveness to high frequency voltage fluctuations in model neurons [27] . To examine how non-linear membrane properties determine the degree of fluctuation-based modulation of input-output responses in neurons , we recorded from MEC stellate cells . These neurons express strong non-linear membrane properties at sub-threshold voltages and are characterized by a voltage-dependent change in membrane resistance [28–30] . Like other cortical neurons , in vivo recordings of stellate cells have established the presence of large membrane voltage fluctuations that have the potential to influence input-output responses [31 , 32] . Using standard measures of spike output in the form of spike frequency-current and spike-probability curves , as well as analysis of spike generation in an exponential leaky integrate-and-fire model , we investigated the biophysical factors regulating the ability of voltage fluctuations to modify stellate cell input-output measures . We find that non-linear membrane properties associated with increased membrane resistivity at sub- and peri-threshold voltages reduce fluctuation-based modulation of input-output responses . Overall , our results indicate that fluctuation-based modulation of neuronal input-output responses can be very low , with limited scaling of spike output via changes in noise and conductance levels .
To investigate the modulation of input-output responses by membrane voltage fluctuations in MEC stellate cells , we started by quantifying fluctuation-induced changes in commonly used measures of neuronal input-output responses . These include the slope ( gain ) and rheobase of frequency-current ( f-I ) and spike-probability curves . Voltage fluctuations were generated using current-based fluctuations that were constructed using low-pass filtered white noise ( see Methods ) . For each cell , we recorded a short trial period in which the current input fluctuation amplitude was adjusted to maintain a standard deviation ( SD ) in output voltage at rest ( -75 mV , corrected for the electrode’s junction potential ) of approximately 2 . 5 mV ( 2 . 41 ± 0 . 1 mV ) , a value commonly observed in vivo [33] . Controlling for the SD of voltage fluctuations was essential since the intrinsic properties of neurons are voltage-dependent and a fair comparison across different cells , conditions and models require that the SD of membrane voltage be constant . Furthermore , previous work addressing similar issues has controlled fluctuation sizes in terms of the SD of membrane voltage and used similar values [8 , 9 , 19 , 23 , 24 , 34 , 35] , For f-I curves , spike frequency was determined using only the first three inter-spike intervals in order to avoid complications arising from the interaction between the time scale of voltage fluctuations and spike frequency adaptation [21 , 23] . Gain was calculated individually for each cell using the slope of a linear fit ( r2: 0 . 64 to 0 . 96 , mean: 0 . 89 ± 0 . 02 ) to the f-I relationship , while rheobase was measured as the current required to elicit a minimum of 3 inter-spike intervals from a holding voltage of -75 mV . As shown in Fig 1B , stellate cell f-I curves were only modestly influenced by the introduction of membrane voltage fluctuations . The addition of fluctuations generated a small , but non-significant , leftward shift in rheobase ( Fig 1Ci; 199 ± 20 pA vs . 158 ± 20 pA , p = 0 . 11 , n = 20 , 18 ) . As with rheobase , the addition of voltage fluctuations did not generate a significant reduction in the f-I curve gain ( Fig 1Cii; 0 . 161 ± 0 . 10 spikes/ pA s . vs . 0 . 148 ± 0 . 08 spikes/pA s , P = 0 . 31 , n = 20 , 18 ) . To quantify potential changes in the f-I curve more carefully , we also measured the effects of voltage fluctuations on firing rate within discrete regions of the f-I curve ( Fig 1D; low , mid and high ) . Previous modeling and experimental work has shown that random voltage fluctuations induce the largest increase in firing rate in the low spike rate region of the f-I curve , near the transition between rest and firing [8–10 , 19 , 20 , 24] . For our data , the low region was defined individually for each cell as the frequency of spike discharge at rheobase , while the mid and high regions corresponded to current values eliciting 15 spikes/s and 25 spikes/s more than the initial frequency , respectively . For each cell , we measured the change in firing rate brought about by voltage fluctuations for low , mid and high current input regions relative to the same cell’s f-I curve acquired without fluctuations . Differences in spike rate were small but changed significantly in the low and mid regions of the f-I curve . For the low region of the f-I curve , fluctuations induced an increase of 3 . 3 ± 0 . 34 spikes/s ( Fig 1D; P <0 . 001 , n = 18 ) , while in the mid region , these values were 3 . 2 ± 1 . 4 spikes/s ( P = 0 . 03 , n = 18 ) . Next , we measured the effects of voltage fluctuations on spike-probability curves . Unlike the f-I curve , which requires repetitive spike generation at each current step size , a spike-probability curve quantifies the probability of generating just a single spike within a given time window ( 100 ms in our case , Fig 1E and 1F ) . In the absence of membrane voltage fluctuations , the transition from a probability of zero to one in spike discharge occurred almost always within a single current step ( Fig 1F; vertical line ) . For experiments without artificial voltage fluctuations , the current amplitude associated with this transition point was defined as rheobase . The addition of voltage fluctuations smoothed the relationship between spike probabilities and current steps such that data points could be fit with a sigmoid function ( Fig 1F; Boltzmann fit , r2 >0 . 96 ) . In the presence of voltage fluctuations , rheobase was defined as the current needed to establish a 0 . 2 probability ( P0 . 2 ) in generating a single spike , while the slope was quantified using the “k” term in the Boltzmann function ( see Methods ) . As shown , voltage fluctuations induced a leftward shift in rheobase relative to conditions without fluctuations ( Fig 1F; 56 ±5 . 6 pA , P <0 . 001 , Student t-test , n = 20 ) and resulted in spike-probability curves with an average slope of 26 ± 8 . 4 pA ( mean ± s . e . m ) . Overall , modifications of stellate cell f-I curves by voltage fluctuations were small compared to previous work in other neurons [8 , 9 , 23 , 24 , 35] , with no significant modulation of f-I curve gain across the population and relatively small changes in initial firing rate . In comparison , past work using similarly sized voltage fluctuations has reported reductions in gain of up to 50% [8 , 9] . Nevertheless , stellate cells do show some degree of fluctuation-mediated modulation of input-output responses as indicated by a shiftand smoothing of the spike-probability curve . Given both theoretical and experimental work supporting a strong modulatory role for membrane voltage fluctuations , we were interested in what factors control and limit fluctuation-based changes of input-output responses in stellate cells . As a potential cause for the limited fluctuation-based modulation of input-output responses , we considered the role of non-linear membrane properties leading up to spike threshold . In simple models , realistic spike generation dynamics have been shown to reduce the likelihood of spike response to rapid voltage fluctuations [27 , 36] . We hypothesized that an extension of this effect over a much larger sub-threshold voltage region than has been previously considered could significantly reduce fluctuation-based modulation of input-output responses . To first establish the presence of sub-threshold non-linear membrane properties in stellate cells , we quantified membrane input resistance between -85 mV and -65 mV . At each holding voltage , membrane resistance was measured in voltage-clamp using a 5 mV voltage step of 100 ms duration . Depolarizing stellate cells led to a progressive increase in steady-state membrane input resistance ( Fig 2A; one-way ANOVA , P <0 . 001 , n = 12 ) . Membrane resistance nearly tripled over a 20 mV range , increasing from 51 . 8 ± 3 . 9 MΩ at -85 mV to 151 . 4 ± 15 . 5 MΩ at -65 mV ( Fig 2A ) . We should note that resistance also kept increasing with additional depolarization to the extent that very small voltage steps ( ~ 1 mV ) often elicited spikes at levels more depolarized than -65 mV and prevented an accurate measure of membrane resistance at these voltage values . Next , we analyzed average membrane voltage trajectories leading up to spike threshold . For current inputs eliciting small changes in voltage ( 5 mV ) , the resulting voltage trajectory was fit accurately with an exponential function and used to extract the membrane time constant near -75 mV ( 12 . 0 ± 0 . 9 ms , n = 19 ) . In contrast , the membrane voltage trajectory to the first spike ( for a 50 ms first spike latency ) from a holding voltage of -75 mV ( ~ 15 mV range ) could not be fit with an exponential function due to the more linear profile of the trajectory ( Fig 2B ) . Similarly , the average voltage trajectory between the trough of the afterhyperpolarization ( AHP ) and spike threshold during continuous firing ( ~ 4 Hz ) was not exponential ( Fig 2C ) . Thus , the voltage trajectories to spike threshold starting either from resting voltages or the trough of the AHP were relatively linear compared to that expected from our measures of the membrane time constant at—75 mV . To assess how changes in spike rate relate to changes in the average voltage associated with spike trajectories at different spike frequencies , we quantified the relationship between spike frequency and mean voltage ( f-V ) . As with the f-I curves , spike rate for the f-V curve was taken from the first three inter-spike intervals , with membrane voltage values calculated as the mean during the same period of time . Surprisingly , we found that f-V curves were non-linear and could be fit with a power-law function ( Fig 2D; mean r2 = 0 . 95 ± 0 . 02 , range: 0 . 7–0 . 99 ) using an exponent ( p ) of 1 . 45 ± 0 . 08 ( Fig 2D; n = 19 , range: 0 . 45–2 . 0 , 17/19 had p values >1 ) . Cell f-V curves were also shallow , with an average slope across the firing range of 4 . 5 ± 0 . 4 spikes/mV s ( n = 19 ) . Contrary to previous assumptions [12 , 37] , therefore , neuronal f-V curves can express significant power-law scaling in the absence of any fluctuation-based smoothing . In summary , our measures of membrane resistance , voltage trajectories , and f-V curves indicate that stellate cells express significant sub- and peri-threshold nonlinearities . To understand the biophysical mechanisms and consequences of a voltage-dependent membrane resistance over a large region of sub-threshold voltage , we started by studying the effects of fluctuation-based modulation of input-output behavior in a simplified model of spike generation in the form of an exponential leaky integrate-and-fire ( eLIF ) model [27] . This model has the advantage of incorporating important non-linear membrane properties associated with spike threshold using a small set of parameters that are related to physiological measures ( e . g . voltage-dependent sub-threshold membrane resistance ) . The spike slope factor ( ΔT ) in the eLIF determines the change in slope of the membrane voltage-current ( I-V ) curve as membrane voltage approaches spike threshold ( VT—Fig 3A ) . The exponential term models the increase in membrane resistance associated with an increase in Na+ conductance activation and spike generation . With small ΔT values , the I-V curve slope ( membrane resistance ) changes abruptly as the system approaches VT ( Fig 3A ) . Conversely , with large ΔT values , the change in slope is more gradual . Thus , for a small ΔT value ( e . g . 2 mV ) , the I-V curve is largely linear with the exception of a small voltage range in the immediate vicinity of VT . As ΔT becomes larger , however , membrane resistance increases gradually over a relatively large span of sub-threshold membrane voltage values . We found that a ΔT value of 15 mV best matched experimental values of steady-state membrane input resistance observed in stellate cells ( Fig 3B ) . We should note that a value 15 mV for ΔT is large compared to that implemented in previous work ( 0 . 8 m V to 6 mV ) using an eLIF model to study cortical neurons [27 , 38 , 39] . We started by comparing stellate cell membrane voltage trajectories to those generated in the eLIF model using ΔT values of 2 , 5 and 15 mV . For comparison , we also included a completely passive model ( i . e . standard leaky integrate-and fire model-LIF ) consisting of a linear conductance term ( 15 nS , -75 mV reversal ) and an artificial threshold ( -55 mV ) . For all models , membrane voltage was reset ( Vr ) to -65 mV after crossing threshold . The eLIF model with a ΔT of 15 mV does a substantially better job in reproducing the more linear approach associated with the initial approach to spiking from -75 mV ( Fig 3Ci ) . Likewise , for the interspike interval voltage trajectory between the AHP trough and spike threshold during repetitive spike discharge ( ~4 Hz ) , a ΔT value of 15 mV best captures the linear trajectory leading up to spike threshold observed in stellate cells ( Fig 3Cii ) . As ΔT is reduced , the voltage trajectories approaching spike threshold become more exponential and qualitatively more like the passive model ( Fig 3C ) . Given the differences in voltage trajectories both in the initial approach to spiking and the interspike voltage trajectories , we were interested in how mean membrane voltage scaled with spike rate ( f-V ) in each of the models . For the passive model , the f-V curve is steep and has a negative slope . In the active models , both the slope and shape changes considerably with different ΔT values . As the value of ΔT is increased from 2 mV to 15 mV , the f-V curve slope goes from negative and steep to shallow and positive ( Fig 3D ) . Furthermore , over the range of 1–60 spikes/s , the f-V curve of the eLIF using a value of ΔT of 15 mV can be accurately fit with a power-law function with an exponent of 1 . 78 , which is within the range of values observed experimentally for stellate cells ( Fig 3F ) . To quantify the modulation of input-output responses by voltage fluctuations in the models , we compared results in the eLIF model using ΔT values of 2 mV and 15 mV . We delivered the same current input fluctuations as in stellate cells , and maintained voltage fluctuations with a SD of 2 . 5 mV at -75 mV . Previous studies have also established that increasing membrane conductance via a variety of mechanisms , which include shunting inhibition , balanced synaptic conductances or simply increasing membrane leak , facilitates modulation of the f-I curve using similarly sized voltage fluctuations [8–10 , 22] . In addition , as a result of the different slopes of the exponential terms , the eLIF models using ΔT values of 2 mV and 15 mV have different input resistance values at -75 mV ( Fig 3B ) , a property that may account for potential differences in fluctuation-based modulation of input-output responses . For these reasons , a separate shunt or leak conductance ( gL ) of 15 nS was introduced in each of the models to test the effect of increasing membrane conductance on input-output modulation . Fluctuation-based modulation of both the f-I and spike-probability curves are substantially larger using a value for ΔT of 2 mV than with 15 mV ( Fig 4 ) . Consistent with previous computational and experimental results [7–10] , increasing membrane conductance using shunting inhibition increases fluctuation-induced modulation of input-output responses in both models , albeit the effect is much larger when ΔT = 2 mV ( Fig 4 ) . With ΔT = 2 mV , fluctuation-induced increases in initial spike firing rates are 23 spikes/s and 30 spikes/s under baseline and with increased membrane conductance , respectively ( Fig 4Bii–4D ) . In comparison , with ΔT = 15 mV , these value are only 4 . 2 spikes/s and 5 . 8 spikes/s ( Fig 4Bi–4D ) . Similarly , for spike-probability curves , voltage fluctuations result in larger leftward shifts and smoothing under both conductance conditions with ΔT = 2 mV ( Fig 4C–4E ) . As a result , the changes in firing rate , rheobase and gain induced by membrane voltage fluctuations correspond more closely to those observed in stellate cells when ΔT is set to 15 mV ( Fig 4D and 4E ) . Decreasing the ΔT also leads to an increase in the gain of the f-I curve ( Fig 4B ) . Consequently , the increase in fluctuation-induced modulation of the f-I curves with lower ΔT values could result from an intrinsic higher gain value that provides greater sensitivity to changes in current input fluctuations . Prior work has established that decreasing the Vr value has a divisive effect on gain [40] . To eliminate the influence of higher gain values , therefore , we incrementally decreased the Vr value to decrease gain when ΔT was small ( Fig 5A ) . Using this approach , we could maintain gain approximately equal across different ΔT values and test if differences in fluctuation-induced smoothing of the f-I curve are entirely due to changes in intrinsic gain ( Fig 5A and 5B ) . Although compensating for gain through changes in Vr decreases fluctuation-based modulation of the f-I curve , lower ΔT values still result in greater smoothing and increases in the initial firing rate of the f-I curve ( Fig 5C ) . For the sake of completeness we also quantified changes in rheobase and slope of spike probabilities curves induced by the introduction of voltage fluctuations ( Fig 5D ) . For these measures the choice of Vr has no impact . As shown , a gradual decrease in ΔT from 15 mV to 1 mV results in a gradual increase in both the ability for fluctuations to shift rheobase and smooth the spike-probability curves ( Fig 5E ) . Although the eLIF model using a large ΔT generates a very good match to the experimental results attained in stellate cells , we were interested if a more biologically plausible model using standard Hodgkin and Huxley ( H-H ) formulism could also reproduce our experimental results . For our H-H-based model , we started with a non-inactivating Na+ conductance ( INap ) that generates the gradual increase in membrane input resistance at sub-threshold membrane voltages observed in stellate cells ( Fig 6A ) . For spiking currents , we used a standard transient Na+ current coupled with a slower K+ current ( see Methods ) . Note that the rheobase values in the conductance-based model differed from the eLIF model because the voltage threshold for spiking was more depolarized than for the eLIF . As expected , reducing the magnitude of INap decreases the gradual increase in subthreshold membrane input resistance ( Fig 6A ) . Further , the reduction of INap increases the slope of the f-V curve ( Fig 6B ) . Consequently , the presence of INap drives the model neuron to generate an f-V more similar to the eLIF model using a ΔT = 15 mV , while without INap , the f-V curve is more similar to when ΔT = 2 mV . To evaluate the role of sub-threshold resistance on fluctuation-based modulation , we varied the conductance magnitude of INap . As before , we added current input fluctuations so as to generate voltage fluctuations with a 2 . 5 mV STD at -75 mV . For both f-I ( Fig 6C and 6D ) and spike probability ( Fig 6E and 6F ) measures , the reduction of INap leads to a progressive increase in the ability for membrane voltage fluctuations to modulate the input-output responses . Thus , a standard H-H conductance-based model can reproduce experimental results and observations from the eLIF model . To better understand how ΔT values determine the membrane voltage trajectory associated with both the initial and interspike interval spike approach , we used phase-plane plots to analyze the eLIF model using ΔT values of 2 mV and 15 mV . In the phase-plane plot representation , the dashed lines indicate the membrane derivative function ( dV/dt ) . The farther the value of the dashed line is from the x-axis at zero , the faster the membrane voltage changes . Through its effect on the shape of the dV/dt line , the ΔT parameter determines the rate at which membrane voltage reaches spike threshold ( Fig 7A and 7B ) . With ΔT = 15 mV , the dV/dt function is shallow and generates small values , as indicated by the close proximity to the zero x-axis , for a large portion of the trajectory leading up to spike threshold; this results in a trajectory that changes more slowly and spends a small fraction of time in close proximity to spike threshold ( Fig 7A ) . Reducing ΔT increases dV/dt values for all voltages leading up to threshold . Under these conditions , the voltage trajectory only begins to slow in the immediate vicinity of spike threshold and , consequently , spends a large fraction of the interspike interval in close proximity to threshold ( Fig 7A and 7B; insets ) . The above analysis suggests that ΔT influences fluctuation-based modulation of input-output functions at a given inter-spike interval value by setting the fraction of time that voltage spends in close proximity to spike threshold . As ΔT becomes smaller , voltage trajectories spend an increasing fraction of time near the voltage for spike threshold , whereby small fluctuations can lead to spike events very early in the evolution of the trajectory . Conversely , by linearizing the trajectory , large ΔT values limit fluctuation-induced spikes to time points later in the evolution of the voltage trajectory . To illustrate this , we quantified the probability that voltage fluctuations cause a spike at different time and voltage points during a single inter-spike interval trajectory ( Fig 7C ) . Thus , each point along the trajectory provided initial conditions from which to calculate the likelihood of generating a spike at that given voltage and time point . From each of these points , we ran the models for a 50 ms period of time and calculated the probability of spike discharge in response to voltage fluctuations ( SD = 2 . 5 mV ) within this time period using 1000 trials ( see Methods ) . As indicated in Fig 7C , for the majority of the interval , the likelihood of generating a spike in response to voltage fluctuations is substantially higher for ΔT = 2 mV than that for ΔT = 15 mV . Although a value of ΔT = 15 mV leads to higher spike discharge probabilities towards the end of the interspike interval trajectory , the effect is limited in time and occurs late in the cycle , whereby the impact on spike rate relative to the deterministic case is small . The influence of ΔT on the dV/dt line also changes the scaling between mean membrane voltage and spike frequency . As indicated above , changes in ΔT alter the dV/dt line , with a larger value of ΔT generating a shallower dV/dt line . With the addition of positive ( depolarizing ) current , the dV/dt line moves upwards and away from x-axis , which results in a faster voltage trajectory ( i . e . shorter interspike intervals ) . When ΔT = 2 mV , the voltage trajectory approaches values near threshold ( VT ) more quickly than with ΔT = 15 mV . With ΔT = 2 mV , the membrane derivative only slows down near the inflection point of the dV/dt line . Consequently , as the dV/dt line shifts upwards , the mean of the voltage trajectory remains largely unchanged because the majority of the trajectory is represented by the value near the inflection point of the dV/dt line ( Fig 7E and 7F ) . In essence , a ΔT = 2 mV compresses the voltage trajectory to a value approximately equal to VT . With ΔT = 15 mV , however , a shift upwards in the dV/dt line significantly accelerates the voltage variable for a much larger fraction of the trajectory profile , especially for time points early in the trajectory that are far from the inflection point of the dV/dt line . As a result , the mean of value of the voltage variable depolarizes as the applied current magnitude increases and the dV/dt line shifts upwards ( Fig 7E and 7F ) . By generating a shallow f-V , a large ΔT limits the ability for a change in voltage brought about through random fluctuations to increase spike firing rate . Conversely , when ΔT is small and the f-V relationship is steep , voltage fluctuations can give rise to a large change in spike firing rate ( Fig 7D ) . With large ΔT values , the increase in membrane resistance , and the resulting increase in voltage fluctuations , could potentially overcome the reduction in fluctuation-based modulation established by a shallow f-V relationship . Although the SD of membrane voltage fluctuations is larger in the peri-threshold region with ΔT = 15 mV compared to ΔT = 2 mV , the difference is small compared to the changes in the f-V relationship established by increasing the value of ΔT ( Fig 8A ) . Thus , at the mean voltage where fluctuations first induce spiking in both models ( ~-68 mV ) , the SD of voltage fluctuations only increases from 2 . 64 mV to 3 . 06 mV when ΔT is changed from 2 mV to 15 mV . In comparison , changes in the f-V relationship are much greater . With ΔT = 2 mV , a 60 spikes/s range occurs entirely within a 0 . 5 mV range , but increases to a 10 mV range when ΔT = 15 mV ( Fig 7D ) . As a result , increasing ΔT from 2 mV to 15 mV has a much larger effect on the scaling of the f-V relationship than the size of membrane voltage fluctuations . Even under conditions where the SD of membrane voltage fluctuations is increased to 3 . 6 mV or 7 . 2 mV ( by increasing the current-input fluctuations ) , values much larger than those used with ΔT = 2 mV , the modulation of the f-V and f-I relationships are still significantly less with ΔT = 15 mV than with ΔT = 2 mV ( Fig 8B and 8C ) . In summary , a large ΔT generates voltage trajectories that spend a smaller fraction of time in close proximity to spike threshold and whose mean changes significantly with spike frequency . Both characteristics are the result of a gradual increase in membrane input resistance and help reduce modulation of input-output responses by voltage fluctuations . Our measures of input-resistance , voltage trajectories and analyses of the eLIF model generated two testable hypotheses regarding the sensitivity of stellate cell spike output to voltage fluctuations . Reducing the increase in input resistance associated with depolarization over the subthreshold region should lead to an increase in fluctuation-based modulation of input-output responses . This manipulation is akin to reducing the value of ΔT . Second , manipulations of membrane resistance using negative and positive sloped conductances should result in a reduction and increase , respectively , in fluctuation-based modulation of input-output responses , by manipulating the influence of the endogenous negative slope conductance and altering the voltage trajectories associated with the approach to spike threshold . Previous work in other neurons has established that steady-state Na+ conductance , mediated either by a window current or persistent Na+ conductance , can substantially increase membrane resistance with depolarization [26 , 41] . To establish the role of Na+ conductance in determining sub-threshold input resistance in stellate cells , we used a small concentration of TTX ( 10 nM ) that was able to significantly alter sub-threshold membrane resistance but also maintain the ability to generate at least one spike . Application of TTX significantly reduced the gradual increase in input resistance across different voltages ( 2-way ANOVA , P <0 . 001 , n = 12 ) . At -65 mV , TTX reduced the input resistance from 151 . 4 ± 15 . 5 MΩ to 65 . 9 ± 4 . 8 MΩ ( Fig 9A; P <0 . 001 ) without affecting the input resistance below -70 mV ( Fig 9A; P >0 . 32 ) . Next , we assessed the effect of Na+ conductance activation and a voltage-dependent increase in input resistance on the membrane voltage trajectory leading up to a spike . Because the membrane voltage trajectory to the first spike from a holding voltage of -75 mV could not be fit with an exponential function ( Fig 2B and Fig 9B ) , we quantified changes in the first spike voltage trajectory by measuring the fraction of time spent above the mid-point of the trajectory . A trajectory with a large fraction above the mid-point is more similar to an exponential approach . The mid-point to threshold was calculated for each individual cell’s response to a square current step eliciting a ~50 ms latency to first spike . In the presence of TTX , the voltage trajectory spent a significantly larger fraction of time above the mid-point compared to control ( Fig 9B; TTX: 0 . 84 ± 0 . 03 vs . control: 0 . 68 ± 0 . 01 , P <0 . 001 , n = 6–18 ) . Unfortunately , 10 nM TTX eliminated the ability to generate continuous spike discharge that is required for f-I curve measures . As a consequence , we limited our analysis of cell output in the presence of TTX to spike-probability curves . As indicated , membrane voltage fluctuations were more effective at shifting the rheobase and smoothing the spike-probability curve in the presence of TTX ( Fig 9D and 9E ) . The leftward shift in rheobase increased from 64 . 4 ± 7 . 7 pA to 134 ± 25 pA ( Fig 9Ei; paired Student t-test , P = 0 . 001 , n = 10 ) , while the slope factor increased from 18 . 0 ± 1 . 3 pA to 29 . 6 ± 4 . 7 pA ( Fig 9Eii; paired Student t-test , P = 0 . 02 , n = 10 ) . These results indicate that reducing the amount of sub-threshold Na+ conductance generates more exponential-like trajectories that result in increased fluctuation-based modulation of the spike-probability curves . To further investigate the role of membrane resistance and negative slope conductance in shaping modulation of input-output response by voltage fluctuations , we manipulated membrane resistance in stellate cells using dynamic clamp by introducing artificial negative or positive slope conductances . In particular , we were interested in the effects of negative slope conductance since the ability for Na+ conductance to increase input resistance and slow the rate of change of membrane voltage is related to the negative slope associated with the Na+ I-V relationship [26] . Hence , the introduction of an artificial negative slope conductance should further decrease fluctuation-based modulation of input-output responses . Conversely , the addition of a positive slope conductance should increase modulation via voltage fluctuations by decreasing the influence of the endogenously expressed negative slope conductance . For the negative slope conductance , we used a value of -5 nS , which was the maximum amount that could be added without introducing instabilities and that increased membrane resistance measured at -75 mV from 68 . 9 ±8 . 9 MΩ to 102 ± 13 MΩ . The positive conductance was set to 15 nS and decreased membrane resistance to 34 . 1 ± 2 . 7 MΩ . For the positive conductance , values greater than 15 nS led to a loss of continuous spiking in fashion similar to what has been reported in CA1 pyramidal cells using this manipulation [42 , 43] . Both the negative and positive conductances were linear with a reversal potential of -75 mV . As with TTX experiments , we quantified changes in the first spike voltage trajectories using the fraction of time above the mid-point . Changes in membrane conductance had a significant impact on the trajectory leading up to spike threshold ( Fig 10A; one-way ANOVA , P <0 . 001 , n = 8–18 ) . Negative conductance decreased the fraction of time above the midpoint to 0 . 59 ± 0 . 02 , while adding positive conductance increased this this value to 0 . 82 ± 0 . 02 ( Fig 10B; P <0 . 001 , Tukey’s test ) . Changing membrane conductance also had a significant impact on the duration of the AHP associated with continuous firing at ~4 Hz ( Fig 10C; one-way ANOVA , P <0 . 001 , n = 9–12 ) . Negative conductance led to a significant increase in the AHP half-duration ( time from trough to midpoint voltage between trough and spike threshold , Fig 10D; 98 . 1 ± 5 . 3 ms , P <0 . 001 , Tukey’s test ) . Although positive conductance did not significantly alter the AHP half duration relative to control when taking repeated measures into account , there was a decrease in mean values ( 43 . 4 ± 4 . 5 ms , P = 0 . 07 , Tukey’s test ) . Hence , overall , changes in membrane conductance altered the duration of membrane voltage trajectories leading to spike threshold in a form consistent with our analysis of the eLIF model . Next , we quantified the effects of negative and positive changes in membrane conductance on fluctuation-based modulation of stellate cell input-output curves . For each conductance level , we compared changes in the slope and rheobase of f-I and spike probabilities curves induced by the introduction of membrane voltage fluctuations . As before , membrane fluctuations were kept at a SD of ~ 2 . 5 mV ( -5 nS , 2 . 3 ± 0 . 1 mV , control: 2 . 41 ± 0 . 1 mV; 15 nS: 2 . 35 ± 0 . 01 mV , n = 5 , 19 , 18 ) . Analysis of f-I curves indicated that gain was significantly modulated by changes in membrane conductance , but not by the introduction of membrane voltage fluctuations ( Fig 11A and 11B; 2-way ANOVA , P <0 . 001 for conductance , P = 0 . 36 for voltage fluctuations ) . Contrary to expectations [8 , 9 , 44 , 45] , f-I curve gain can be modulated , albeit modestly , by changes in membrane conductance only . More importantly , stellate cells f-I curves maintained a low degree of fluctuation-based modulation at all three conductance levels . We should note that eLIF model using a ΔT = 15 mV , but not ΔT = 2 mV , also generates a small decrease in gain when membrane conductance is increased . This result is related to changes in mean interspike interval membrane voltage induced by changes in conductance when ΔT = 15 mV , but not ΔT = 2 mV . We proceeded to measure changes in rheobase and initial firing rate associated with f-I curves resulting from voltage fluctuations under each of the conductance conditions . Changes in membrane conductance significantly impacted the ability of artificial fluctuations to change rheobase ( Fig 11Ci; P <0 . 02 , one-way ANOVA , P = 0 . 01 , Tukey’s test ) . Further , voltage fluctuations were not able to significantly reduce rheobase values in the presence of -5 nS conductance ( Fig 11Di; one sample Student t-test , P = 0 . 58 ) , while with 15 nS rheobase significantly increased ( Fig 11Di; one sample Student t-test , P <0 . 001 ) . As with rheobase , changes in the initial spike firing rate were also significantly changed by membrane conductance ( Fig 11Dii; P = 0 . 001 , one-way ANOVA ) , with each conductance level generating significant differences in initial spike firing rates ( Fig 11Cii; P <0 . 01 , Tukey’s test ) . With -5 nS conductance voltage fluctuations were not able to significantly increase firing rate from zero ( Fig 11Cii; one sample Student t-test , P = 0 . 79 ) , while these changes were significant under control and with 15 nS ( Fig 11Cii; one sample Student t-test , P <0 . 001 ) . Fluctuation-induced changes in spike-probability curves mirrored those observed in the f-I curves ( Fig 11D and 11E ) . For both rheobase and slope factors , conductance had a significant impact ( Fig 11Ei and 11Eii; P <0 . 001 , one-way ANOVA ) . Increasing membrane conductance by 15 nS led to both an increase in the ability for voltage fluctuations to shift the rheobase and smooth the spike-probability curves ( Fig 11E ) . With a decrease in membrane conductance ( -5 nS ) , rheobase changes and smoothing were less pronounced ( Fig 11D and 11E ) . Overall , these results are consistent with our hypothesis and indicate that a slower and more linear voltage trajectory , established by an increase in membrane resistance through a negative slope conductance , reduces modulation of input-output response by voltage fluctuations . Additionally , the linearization of voltage trajectories is fundamentally related to the negative slope conductance associated with Na+ current activation . Our data show that non-linear membrane properties shape the potential for voltage fluctuations to modulate the input-output responses of neurons .
Membrane voltage fluctuations and changes in membrane conductance ( e . g . shunting inhibition ) are believed to be key factors that control neuronal input-output scaling [7–9 , 11 , 45] . A generally accepted role of voltage fluctuations is to lower spike threshold and amplify weak inputs [5 , 6] . Thus , the size and spectral content of voltage fluctuations can be used to potentially gate inputs , as suggested by some network models [46] . In addition , fluctuating current input can divisively scale the f-I curve by disproportionately increasing spike firing rate near threshold [5 , 7–10] . Moreover , under fluctuation-driven spiking , it has been shown that spike firing rate can scale with sub-threshold membrane voltage for a meaningful portion of a cell’s dynamic range [9 , 10] . For this reason , in the presence of significant voltage fluctuations , changes in the sub-threshold I-V relationship brought about through shunting inhibition , balancing excitatory and inhibitory conductances , or simply introducing a leak conductance are expected to translate into a further reduction in the slope of the f-I curve . Changes in the slope of the f-I curve are believed to be critical for setting the tuning curve of individual cells and have been proposed to play a critical role in sensory processing , particularly in the visual system with regards to setting the neuronal spike response to contrast [3 , 12 , 37 , 45] . Synaptic-mediated voltage fluctuations have therefore been implicated in setting both neuronal spike threshold and the overall scaling of the input-output relationship . Data from cerebellar granule cells [9] and simulations from compartmental models [10] has convincingly demonstrated that spike rate can indeed scale with sub-threshold membrane voltage once voltage fluctuations are added . In granule cells , for example , fluctuations permit spike generation over a range of more than 100 spikes/s across what would otherwise be sub-threshold voltage values in the absence of fluctuations . Our data , however , suggest that this scenario is not generalizable , at least within the limits of physiological voltage fluctuations . In the case of stellate cells and our eLIF model ( ΔT = 15 mV ) , spike frequency scaling with sub-threshold membrane voltage is severely constrained due to a very shallow f-V relationship , which limits the spike frequency range in which voltage fluctuations can generate spikes . Consistent with our interpretation , granule cells have very steep f-V curves in the absence of significant membrane voltage fluctuations , with a 250 spikes/s range occurring over less than 2 . 5 mV [9] . Similarly , simulations supporting this mechanism [10] have been carried out in a compartmental model also expressing a steep f-V relationship ( 80 spikes/s over ~ 2 . 5 mV ) . In contrast , stellate cells and the eLIF model ( ΔT = 15 mV ) generate f-V curves with slopes in the range of 4–5 spikes/mVs . Results presented here , therefore , indicate that the characteristics of the f-V curve and its relation to voltage trajectories leading up to spikes are crucial to understanding the degree that neuronal input-output functions are modulated by voltage fluctuations . In the visual system , a power-law scaling between spike firing rate and membrane voltage of layer II pyramidal neurons is critical for gain control and contrast invariance [12 , 37] . Modeling has shown that a power-law scaling with an exponent near 2 between spike firing rate and voltage can arise from the combination of an intrinsic , steep and linear f-V relationship , and smoothing through Gaussian-distributed voltage fluctuations [3 , 19 , 34 , 47] . In contrast with past assumptions , our data indicate that the f-V curve is not well approximated by a steep linear function , and that part of the non-linear scaling between spike rate and voltage can result from intrinsic voltage-dependent membrane properties . For the eLIF model using a ΔT = 15 , the gradual activation of the negative slope conductance plays a critical role in setting the shallow , non-linear f-V curve . By activating gradually , depolarization results in a change of mean voltage at different spike discharge rates . This is because the rate of change in voltage is heavily influenced by the activation of the negative slope conductance . As greater amounts of the negative slope conductance are activated incrementally , the shape and mean of the interspike-interval voltage trajectory change considerably . This is not the case with ΔT = 2 mV because the voltage trajectory at different frequencies is largely set by the passive properties of the membrane , which result in an exponential approach that does not experience a change in mean with increasing levels of depolarization and firing rate . Stellate cells have been implicated in spatial navigation via their grid-like spatial firing fields [48] . It is possible that the low degree of modulation of input-output responses by membrane voltage fluctuations in stellate cells helps maintain stable firing patterns with respect to spatial position . By reducing the influence of rapid voltage fluctuations on firing rate and input-output responses , stellate cell behavior likely promotes increased reliability to inputs associated directly with relevant network activity [31 , 32] . Consistent with this interpretation , our past work on spike-phase locking in stellate cells demonstrated a very high degree of spike-phase locking to slow ( 1–10 Hz ) oscillatory inputs in the presence of random voltage fluctuations and increased membrane conductance [49] . A key factor in decreasing input-output modulation by membrane voltage fluctuations in stellate cells is the gradual activation of Na+ conductance . By slowing the approach to spike threshold , the gradual activation of Na+ conductance results in membrane voltage being farther from threshold for a large fraction of the trajectory . Current injections associated with f-I curve measures lead to a graded change in mean voltage and small , incremental increases in firing rate , resulting in a shallow f-V relationship . Both an LIF model and an eLIF model implemented with small ΔT values express linear sub-threshold membrane properties . This leads to an exponential approach to threshold in which membrane voltage plateaus early and a large fraction of time is spent near spike threshold . As a result , small changes in current input and membrane voltage result in large changes in firing rate . In addition to stellate cells , cortical neurons in the visual cortex [39] , cerebellar Purkinje cells [50] and striatal interneurons [51] show a gradual increase in membrane resistance with depolarization that is mediated by Na+ current . Hence , the behavior observed in stellate cells is likely applicable to a wide range of different neurons . From a non-linear dynamics perspective , an increase in membrane resistance that results in a region of negative slope conductance in the vicinity of threshold is consistent with a saddle-node bifurcation . This bifurcation is often associated with type I characteristics present in cortical pyramidal cells [52] . On the other hand , fast-firing interneurons in cortex have been classified as type II , with threshold behavior often modeled using a Hopf bifurcation and hence not requiring an increase in membrane resistance [52 , 53] . Previous modeling and experimental studies have suggested that type I behavior promotes a high sensitivity to membrane voltage fluctuations [18 , 52 , 54] . Unfortunately , drawing a clear relationship between the degree of modulation of input-output responses by voltage fluctuations and the type of threshold bifurcation is difficult . Our mechanism requires a graded increase in membrane resistivity over a 20 mV range , while a determination between a saddle-node and a Hopf bifurcation is established in the immediate vicinity of spike threshold , which is often less than 1 mV . In vivo voltage fluctuations , however , can span more than a 10 mV range such that the integration behavior of a cell over large regions of sub-threshold voltage , which are well outside the immediate vicinity of spike threshold , become crucial to understanding how a cell reacts to voltage fluctuations . For these reasons , we believe that either form of bifurcation and type can give rise to low or high sensitivity in input-output responses to random voltage fluctuations .
All experimental protocols were approved by the University of Utah Institutional Animal Care and Use Committee . Horizontal sections of hippocampus and entorhinal cortex were prepared from 25 to 50 day-old Long-Evans rats of either sex . All chemicals were obtained from Sigma-Aldrich ( St . Louis , MO ) unless otherwise noted . After anesthetization with isoflurane and decapitation , brains were removed and immersed in 0°C artificial cerebrospinal fluid ( ACSF ) solution consisting of the following: ( in mM ) : NaCl ( 125 ) , NaHCO3 ( 25 ) , D-glucose ( 25 ) , KCl ( 2 ) , CaCl2 ( 2 ) , NaH2PO4 ( 1 . 25 ) , MgCl2 ( 1 ) , and buffered to pH 7 . 4 with 95/5% O2/CO2 gas . Horizontal slices were cut to a thickness of 400 μm ( Leica VT 1200 , Leica Microsystems; Wetzlar , Germany ) . After the cutting procedure , slices were incubated in ACSF at 30°C for 20 minutes before being cooled to room temperature ( 20°C ) . After the incubation period , slices were moved to the stage of an infrared differential interference contrast-equipped microscope ( Axioscope 2+; Zeiss , Oberkochen , Germany ) . All recordings were conducted between 32 and 34°C . Electrodes were drawn on a horizontal puller ( P97; Sutter Instruments , Novato , CA ) and filled with an intracellular solution consisting of the following ( in mM ) : K-gluconate ( 120 ) , KCl ( 20 ) , HEPES ( 10 ) , diTrisPhCr ( 7 ) , Na2ATP ( 4 ) , MgCl2 ( 2 ) , Tris-GTP ( 0 . 3 ) , EGTA ( 0 . 2 ) and buffered to pH 7 . 3 with KOH . Final electrode resistances were between 2 and 5 MΩ , with access resistance values between 5 and 16 MΩ . Bridge balance compensation was used for all recordings . Seal resistance values were always greater than 1 GΩ . Electrophysiological recordings were performed with a current-clamp amplifier ( Axoclamp 2B; Molecular Devices , Union City , CA ) , and data were acquired using custom software developed in Matlab ( v . 2011 , Mathworks , Natick , MA ) utilizing the data acquisition toolbox . An estimated junction potential of 10 mV was subtracted for data analysis . Thus , the average resting potential reported here ( -75 mV ) is 10 mV more hyperpolarized than those reported elsewhere for stellate cells [55 , 56] . Stellate cell identity was established using the following criteria: 1 ) presence of a hyperpolarization-mediated membrane voltage sag , 2 ) impedance and resonance measures indicating a steady-state input resistance at rest between 35 MΩ and 80 MΩ and the presence of a ~5 Hz resonance peak determined using methods described previously [49] and 3 ) the location and cell morphology under DIC-IR optics ( i . e . in layer II of MEC and with a non-pyramidal cell body shape ) . For voltage clamp experiments , we held cells at each corresponding voltage ( -85 to -65 mV ) and used a small step ( 5 mV ) and measured the change in current . The ratio of the change in voltage and current was used to measure input resistance at each corresponding holding voltage . For dynamic clamp experiments , the current-clamp amplifier was driven by an analog signal from an x86 personal computer running Real-Time Application Interface Linux and Real-Time eXperimental Interface ( RTXI ) [57 , 58] . Shunting inhibition ( Iinh ) was implemented using RTXI with the following equation: Iinh = ginh ( V-Einh ) For these experiments , Einh and ginh were set to -75 mV and 15 nS , respectively . In the case of negative conductance ( Fig 10 and 11 ) , the ginh term was set to -5 nS . For all experiments , the sample rate of the dynamic clamp was set to 10 kHz . A measured junction potential of approximately 10 mV was subtracted from all recordings . Data were collected at 10 kHz and filtered at 3 kHz . Current input fluctuations were implemented with filtered white noise using a low pass ( fcut= 100 Hz ) filter . The current signal was constructed in the frequency domain using a frequency amplitude ( A ( f ) ) scaling of A ( f ) = 1/ ( 1+ ( f/fcut ) ) . Matlab’s ifft function was used to implement an inverse Fourier transform and generate the time series from signals constructed in the frequency domain . For each cell , we recorded a short trial period in which the current input fluctuations were adjusted to maintain a standard deviation ( SD ) in membrane voltage fluctuations at rest ( -75 mV ) of ~2 . 5 mV . For Na+ channel block , tetrodotoxin ( TTX , Tocris , Bristol , UK ) was bath applied at a concentration of 10 nM . Recordings with TTX were carried out approximately 15 minutes after bath application of the drug . For the exponential leaky integrate-and-fire ( eLIF ) model [27] , membrane voltage dynamics were governed by the following differential equation: CdVdt = Ie+gLΔTeV-VTΔT-gL ( V-EL ) where C = 170 pF , VT = -60 mV , gL = 25 nS , EL = -75 mV and ΔT = 15 mV ( default ) . Note , for the passive version of the model ( i . e . standard leaky integrate-and-fire ) , the first gL term was set to zero and a separate leak term was used with the same reversal potential and using a conductance value of 15 nS . This value of conductance generates a passive model with the same input resistance as the eLIF model at -75 mV . Because of the exponential term in the eLIF , membrane voltage diverges to infinity upon crossing VT . For the eLIF simulations , membrane voltage was reset to VR ( -65 mV ) upon reaching a value of 0 mV . The passive model lacks a true threshold phenomena , therefore an artificial threshold was set at -55 mV and membrane voltage was reset to -65 mV after crossing this threshold value . All models were simulated in Matlab and solved with a forward Euler method using a time step of 0 . 01 ms . We also tested model solutions with a time step of 0 . 001 ms and found the same results . For the H-H formulism-based model ( Fig 6 ) , membrane voltage was governed by the following equations: C dV dt = I e − g Na m ( 1−n ) ( V− E Na ) − g Nap p ( V− E Na ) − g K n ( V− E Na ) − g L ( V− E L ) dn dt = ( n ∞ −n ) τ n , ( K + conductance ) m= 1 ( 1+ e V+30 −3 ) , ( transient Na + conductance ) n ∞ = 1 ( 1+ e V+30 −3 ) , ( K + conductancesteady-stateactivation ) p= 1 ( 1+ e V −15 ) , ( persistent Na + conductance ) where C = 170 pF , τn = 3 ms , ENa = 50 mV , EK = -90 mV , EL = -80 mV , gNa = 170 nS , gK = 90 nS , gNap = 150 nS and gleak = 20 nS . To reduce the dimensionality of the model we used the approximation that h≈1-n and that both m and p equilibriate with membrane voltage instantaneously due to their time constants being smaller than the membrane time constant . As with experiments , current fluctuations were generated using filtered white noise generated using the same equation and cut-off frequency ( 100 Hz ) as in experiments . To ensure a SD of 2 . 5 mV at a voltage of -75 mV , each model was tested with incrementally larger noise coefficients until a SD of 2 . 5 mV was reliably measured over a small region of coefficient values at -75 mV over a 15 s duration . Current fluctuations were added to the DC current term ( Ie ) in the above equation ( i . e . additive noise ) . To increase model membrane conductance , a separate gLterm was added using a reversal potential of -75 mV . All analyses were carried out in Matlab using custom software and/or built in functions . For power-law and Boltzmann fits , we also confirmed fits in Origin 8 . 5 ( OriginLab , Northampton , MA ) . Spike times were determined using a threshold crossing for membrane voltage . Spike frequency was determined using the mean inverse of the first three inter-spike intervals elicited from one or two second current steps . Average gain values were determined by averaging the individual slope values attained using a linear regression analysis of the f-I relationship . Rheobase values were calculated as the minimal current required to elicit 4 spikes from a holding voltage of -75 mV . For illustrative purposes concerning the average f-I curves shown in Fig 1 and 8 , we calibrated the starting point of the f-I curve such that each cell’s initial value was near the average rheobase calculated for a given condition . For spike-probability curves , the current step duration was 100 ms and each current step size was repeated 15 to 25 times , with spike-probability defined as the number of steps that evoked spikes divided by the total number of steps . Individual spike-probability ( P ( I ) ) curves were fit with a Boltzmann function as follows: P ( I ) = 11+eI-Ihalf-k where P is the probability of spike discharge , Ihalfis the current step size value required to elicit 0 . 5 probability ( P0 . 5 ) in spike discharge and k is the slope ( larger values denote a shallower slope ) of the curve . Fits were used to calculate the slope factor ( k ) , while the rheobase for probability curves was defined as the current step size required to elicit a P0 . 2 in spike discharge . For the f-V curve , experimental and modeling results were fit using a power-law function: fV = aV-Vcp+b where f is the firing rate , p is the exponent of the fit reported in the results section , a and b are positive constants and VC is the minimal voltage required to elicit spike generation . Note that the term a was bounded such that only values of 1 or greater were possible . All fits used a least-squares method . To quantify the effect of membrane voltage fluctuations on spike discharge in models during continuous spiking ( Fig 7C ) , we first considered the model cell spiking periodically in the absence of random fluctuations ( with period of ~270 ms ) , and defined the interval fraction as the inter-spike interval time divided by the total period . At every given fraction of the interval , we took the deterministic state of the model ( value for voltage and all other variables ) and used it as the initial condition for a set of 1000 test simulations , each 50 ms long in which fluctuations were then added . We defined probability as the number of simulations generating a spike divided by the total number of simulations at each given interval fraction . Thus , our probability measures how likely it is that voltage fluctuations will introduce one spike at a given phase and provides a measure for the ability of voltage fluctuations to change the spike rate associated with the low current region of the f-I curves . For multiple comparisons , statistical significance was determined using either a one-way or two-way ANOVA . For repeated measures of means , statistical difference was determined using Tukey’s honestly significant criteria , while a Student t-test ( one- or two-sample ) was used for comparison of one or two values . Means are presented along with the standard error of the mean ( s . e . m . ) . | The membrane voltage of neurons in vivo is dominated by noisy “background” fluctuations generated by network-based synaptic activity from nearby cells . It has been speculated that membrane voltage fluctuations in neurons play an important role in scaling the relationship between input amplitude and spike rate response . For this to be true , neuronal spike input-output behavior must be sensitive to physiological membrane voltage fluctuations . Using a combination of single cell recordings and modeling , we investigated the mechanisms through which voltage fluctuations modulate neuronal input-output responses . We find that neurons that express an increase in membrane input resistance with depolarization show low levels of noise-mediated modulation of input-output responses due , in part , to voltage trajectories that suppress the likelihood of generating a spike in response to random current input fluctuations . Hence , non-linear membrane properties arising from certain types of voltage-gated conductances limit noise-based modulation of neuronal input-output responses . | [
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] | [] | 2015 | Non-linear Membrane Properties in Entorhinal Cortical Stellate Cells Reduce Modulation of Input-Output Responses by Voltage Fluctuations |
Whereas optogenetic techniques have proven successful in their ability to manipulate neuronal populations—with high spatial and temporal fidelity—in species ranging from insects to rodents , significant obstacles remain in their application to nonhuman primates ( NHPs ) . Robust optogenetics-activated behavior and long-term monitoring of target neurons have been challenging in NHPs . Here , we present a method for all-optical interrogation ( AOI ) , integrating optical stimulation and simultaneous two-photon ( 2P ) imaging of neuronal populations in the primary visual cortex ( V1 ) of awake rhesus macaques . A red-shifted channel-rhodopsin transgene ( ChR1/VChR1 [C1V1] ) and genetically encoded calcium indicators ( genetically encoded calmodulin protein [GCaMP]5 or GCaMP6s ) were delivered by adeno-associated viruses ( AAVs ) and subsequently expressed in V1 neuronal populations for months . We achieved optogenetic stimulation using both single-photon ( 1P ) activation of neuronal populations and 2P activation of single cells , while simultaneously recording 2P calcium imaging in awake NHPs . Optogenetic manipulations of V1 neuronal populations produced reliable artificial visual percepts . Together , our advances show the feasibility of precise and stable AOI of cortical neurons in awake NHPs , which may lead to broad applications in high-level cognition and preclinical testing studies .
Optogenetic techniques enable the functional characterization of neuronal populations and circuits with high spatial and temporal precision [1–7] . Though relatively understudied as compared to rodents , optogenetics techniques have been applied to the study of high-level cognition circuits in NHPs [8–13] , including those underlying human neurological and psychiatric disorders [14–16] , and they hold the potential to unveil the mechanistic pathways for visual processing circuits that are found only in humans and NHPs ( as the only mammals with retinal foveas ) [17 , 18] . NHP studies are moreover essential for preclinical testing of optogenetic therapies before they can be translated to human applications [11 , 19 , 20] . Previous research has recorded optogenetic activation using traditional electrophysiological techniques . This approach is limited , however , because repeated electrode recordings in the same neurons are difficult to achieve across recording sessions in NHPs . In addition , examining opsin expression patterns in vivo within the area targeted by viral vector infusions , while maintaining the health of the neurons , is not currently possible without 2P laser-scanning microscopy [14 , 21–24] . These combined hurdles call for an all-optical interrogation ( AOI ) approach to the application of optogenetic methods in NHPs . AOI is achieved by the combination of optogenetics to perturb neuronal activity , while using calcium or voltage indicators—rather than electrode-based stimulation and recording—to minimize the invasiveness of the readout [25–30] . AOI’s implementation thus allows the monitoring of large neuronal populations—repeatedly and less invasively—with single-cell resolution [31–33] , while enabling detailed mapping of neural circuits during behavior [34 , 35] . Pioneering efforts to apply AOI in NHPs combined optogenetics with both in vivo epifluorescence imaging and intrinsic signal optical imaging [19] . Whereas these techniques allowed for large-field viewing , the spatial resolution of the readout was limited , and specific neurons of interest could not be interrogated repeatedly across recording sessions . Here , we combined wide-field single-photon ( 1P ) and single-cell 2P optogenetic stimulation techniques with recently developed 2P imaging technique in awake macaques [4 , 36] to achieve AOI in NHPs . A red-shifted opsin ChR1/VChR1 ( C1V1 ) and calcium indicators GCaMP5G/GCaMP6s were delivered into V1 with adeno-associated viruses ( AAVs ) and expressed in V1 neuronal populations . The labeled V1 neurons exhibited consistently robust responses , over several months , to either optogenetic or visual stimulation . The behavioral experiments confirmed that robust artificial visual perception could be induced by optogenetic stimulation of V1 neuronal populations .
We infected area V1 neurons in three monkeys with C1V1 ( AAV9–CamKIIα–C1V1 ( T/T ) –ts–EYFP ) —a red-shifted channel-rhodopsin transgene—and GCaMP5G/GCaMP6s ( AAV1–hSyn-GCamP5G/AAV1–Syn–GCamP6s ) —calcium indicators of activity . Six weeks after virus injection , a 1-cm–diameter round optical window ( glass coverslip attached to a titanium ring ) was implanted onto the cortical surface using dental acrylic cement attached to the bone surrounding the craniotomy . To enhance the stability of 2P imaging , we used a three-point head-fixation design , with two head posts implanted on the forehead of the skull and one on the back . A T-shaped steel frame was connected to these head posts for head stabilization during subsequent imaging and stimulating sessions [36] . We imaged layer II/III neurons in the infected cortical area using 2P ( Fig 1A ) . Dark cell bodies indicate that C1V1–ts–EYFP expression was localized to the membrane [37] ( see also S1 Fig ) . Fluorescence of GCaMP6s was relatively weak in the absence of cellular responses to either visual or optogenetic stimulation . NHPs maintained fixation while visual stimuli consisting of drifting gratings and color patches were presented sequentially on the neuronal receptive field for 1 second , with >2-second interstimuli intervals . We recorded robust neuronal calcium responses that showed normal orientation and color selectivity , as well as well-organized receptive-field spatial organization ( Fig 1B , S3 and S4 Figs ) . We then stimulated the neurons optogenetically . Using 1P stimulation ( 532-nm laser ) , we illuminated the entire imaging field ( a 1-mm2 laser spot ) while measuring neuronal activity simultaneously with 2P imaging . Simultaneous stimulation/imaging presented a significant challenge , because—although the stimulation and recording wavelengths were sufficiently separated and filtered optically—the optogenetic stimulation power was orders of magnitude higher than the fluorescence power emitted by the activated cells . Thus , stimulation light leaked through the filters and into the highly amplified photomultipliers ( PMTs ) , with higher power than the relatively small GCaMP fluorescence signal . Therefore , the full-field optogenetic stimulation laser was powered down whenever each 2P imaging scan targeted the central 75% of the FOV ( 24 ms out of each 32-ms imaging scan frame ) . Thus , the entire field was stimulated for 8 ms out of every 32-ms scan ( 25% duty-cycle stimulation at 31 . 25 Hz ) . This allowed us to view the optogenetic activation responses artifact-free ( S6 Fig ) . The cells that were successfully stimulated optogenetically constituted a considerable fraction of the targeted population and responded vigorously ( Fig 1C ) . By repeatedly stimulating—both optogenetically and visually—we made three observations: 1 ) responses from the two modes of stimulation were comparable to each other in both amplitude and dynamics ( Fig 1D and 1E ) ; 2 ) repeated stimulation resulted in similarly sized responses ( Fig 1F ) ; 3 ) optogenetic activation did not alter the receptive field properties of neurons that were subsequently stimulated with visual stimuli ( S3A Fig ) . Notably , the dose-response curve revealed that the average laser-evoked responses were saturated at approximately 0 . 8 mW/mm2 , indicating high sensitivity of the optical manipulation system ( Fig 1G and S2 Fig ) . Using AOI , we assessed the long-term stability of both transgene expression and the physiological response strength to visual and optogenetic stimulation in the behaving NHPs . Transgene expression level and pattern were maintained ( Fig 2A ) , and neurons exhibited consistently robust responses and tuning to visual stimuli ( Fig 2B , 2D and 2I , S3 Fig from monkey M1 and S4 Fig from monkey M3 ) over several months . The same neuronal population was also repeatedly and stably activated by optogenetic stimulation over a 4-month period ( Fig 2C , 2G and 2H ) . We also evaluated the transgene expression at different cortical depths , from the surface to 500 μm . At 10 months post-infection , there was abundant expression between 150 to 300 μm ( Fig 2E and S5 Fig ) , and neurons in this depth range responded robustly to optogenetic stimulation ( Fig 2F ) . Thus , both expression level and optogenetic responses remained stable over long time periods ( in our experience , 6 months or more ) in NHP cortex . A powerful way to assess neural circuit function is to photostimulate an individual neuron ( minimizing stimulation of unwanted targets ) while simultaneously monitoring the activity of the connected neurons in the network [5 , 16 , 35] . To perform simultaneous single-cell–resolution 2P optogenetic activation with 2P calcium imaging of the neuronal population , we added a second optical path to our microscope—driven by a mode-locked femtosecond laser ( λ = 1070 nm , 50 fs ) —and applied 2P stimulation with spiral galvanometer scanning targeted to the somas of the target cells [34] . To examine the spatial specificity of 2P activation , we measured the calcium response of the targeted neuron as a function of multiple stimulation sites ( 5 × 5 grid ) ( Fig 3A ) [34 , 35] . We sequentially stimulated each of the sites using 2P spiral activation . Robust responses in the central neuron were evoked only when the target neuron was directly targeted ( Fig 3B–3D ) , suggesting that spiral 2P stimulation has high spatial precision and must be focused on the neuron for strong optogenetic activation to occur . We then simultaneously monitored and sequentially manipulated several neurons in one imaging field ( Fig 3E ) . Each of these neurons generated strong responses only when targeted by the 2P activation laser ( Fig 3F and 3G ) . To assess the monkeys’ perception from optogenetic stimulation of V1 neuronal populations , we designed a “GO”/“NO GO” visual object detection task , in which two monkeys were required to report the appearance of a visual cue using eye movements ( Fig 4A ) . Each trial began when the NHP fixated the central fixation point . Subsequently , a 0 . 5-degree Gaussian white dot was presented for 22 ms at an eccentricity of approximately 3 degrees as a visual cue for GO ( an eye fixation break ) , and the NHP was rewarded for producing a saccade within 500 ms . On the NO GO trials ( 50% , no visual cue ) , the animal was rewarded for holding fixation for 2 , 000 ms for the entire trial . Training proceeded until the NHPs conducted this task with high accuracy ( >80% correct rate; Visual Stim; Fig 4C ) . Notably , both monkeys tended to make eye movements towards the location of the visual cues ( Fig 4D , green; SD of saccade endpoints from the target: 0 . 22 and 0 . 69 degrees for Monkey M1 and M2 , respectively ) , though any saccade exceeding 1 degree in magnitude was sufficient to receive a reward . We then examined the artificial visual perception generated by optogenetic stimulation—Opto Stim . The GO condition here had no visual cue . Instead , we conducted optogenetic stimulation ( a 532-nm , 66-ms laser pulse , subtending 1 mm2 for Monkey M1 , and a 15-Hz , 33% duty-cycle [22 ms on , 44 ms off] , 0 . 8-mW laser pulse train for Monkey M2 ) at the position of the C1V1-expressing cortex ( about 3 degrees eccentric from the fovea , in a different position from the stimulus in the Visual Stim block , so that saccadic targeting would indicate the monkey’s differential perceived stimulation within visual space ) . Similar to the Visual Stim condition , monkeys in Opto Stim received a juice reward if they produced a saccade ( >2 degrees ) after the optogenetic stimulation . Both monkeys performed this task well after 3–5 sessions as a result of Opto Stim , with 99% versus 96% accuracy for Monkeys M1 versus M2 , respectively ( Opto Stim; Fig 4C ) . The eye movements correctly targeted the stimulation locations within visual space , corresponding to the retinotopic C1V1-expressing loci ( which were never otherwise targeted with Visual Stim cues; SD of saccade endpoints from the target: 0 . 33 and 0 . 51 degrees for Monkey M1 and M2 , respectively ) . This further confirmed that optogenetic stimulation successfully induced artificial visual perception in the NHPs ( Opto Stim; Fig 4E ) . To rule out the possibility that any of the observed effects were due to artifacts resulting from the physical side effects of Opto Stim , we interleaved Mistargeted Stim trials ( 8 . 3% ) in the GO condition: Here , we redirected the laser to a region of V1 cortex that did not express C1V1 ( Fig 4B ) . This mistargeted laser should not have been capable of evoking either optogenetic activation of neurons or artificial visual perception . This control condition was treated as a GO task , and monkeys were again rewarded for saccades in any direction , launched immediately after laser onset ( <500 ms ) . Despite this incentive , we observed significantly fewer saccades in the control condition ( p < 10−20 for Monkey M1 and p < 10−10 for Monkey M2 ) ( Opto Stim versus Mistargeted Stim; Fig 4C ) , indicating that the monkeys were truly not aware of the mistargeted laser stimulation . Note that we sometimes observed saccades in the “No Stim” period before the “Opto Stim” block that were biased slightly toward the optogenetic target area , perhaps because perception induced by our Opto Stim condition was weaker than from Visual Stim and thus the monkeys were more likely to guess . But in general , the percentage saccades launched was much lower in the “No Stim” condition . We also studied saccadic latencies as a function of stimulus type and duration . For Visual Stim , saccadic responses were swift and robust ( Fig 5A ) , and exhibited consistent latencies of approximately 119 ms , measured as the time between cue onset and the saccade crossing the 1-degree magnitude threshold ( Fig 5D ) . During Opto Stim ( 2 . 4 mW/mm2 ) , we found that laser pulses of 44-ms duration ( or more ) elicited robust responses ( Fig 5B ) . For Monkey M2 , similar results were found ( Fig 5F–5K ) . Two pulse stimulation ( 44-ms total duration ) induced responses in 92% trials ( Fig 5K ) in Monkey M2 , similar to Monkey M1 under 44-ms duration single-pulse photostimulation . Saccadic latencies from optogenetic stimulation in both monkeys was 30–40 ms shorter than from visual stimulation , averaging about 90 ms after laser onset ( Fig 5D and 5J ) . This 30–40 ms difference arose presumably because optogenetic stimulation bypassed the subcortical visual pathway . This observation is consistent with previous studies of visual signal propagation from the retina to V1 [38 , 39] .
Channelrhodopsin-2 ( ChR2 ) is a commonly used optogenetic actuator for NHPs , though it often requires high laser power to evoke neuronal and behavioral responses [12–15 , 21] . The high conductance and red-shifted absorption spectrum of C1V1 makes it a preferable choice [10 , 37 , 41 , 42] . This is especially true for AOI experiments , since C1V1’s excitation spectrum is well separated from that of GCaMPs [34 , 35] . Expression of C1V1–ts–EYFP was robust , and we observed membrane-localized EYFP fluorescence , which has previously indicated membrane localization of C1V1 [41] ( Fig 1 and S1 Fig ) . We visualized GCaMP6s fluorescence and filtered the widefield 1P stimulation pulses with a 500 ± 12 . 5-nm filter to block most of the EYFP fluorescence . Although the imaging quality was somewhat reduced due to the filter , we nevertheless identified robust responses derived from both visual and optogenetic stimulation ( Figs 1 and 2 ) . Note that we did not achieve high efficiency of co-expression of C1V1–ts–EYFP and GCaMP6s in single neurons , and we found that many neurons could be activated by our wide-field illumination but not by our single-cell photostimulation . Precise quantification of the single-cell expression levels was not possible with our methods because the bright background fluorescence in our approach was likely contributed to by fluorescence of other neurons due to the membrane-bound targeting of the specific indicator we chose . Dendrites from other neurons—and even the soma membranes of directly abutting neurons—could not be perfectly isolated from any given target neuron . We expect that this issue of precise single-cell quantification would be ameliorated by using an indicator that expresses within the cytosol ( labeling the cell body only ) rather that the membrane . This is why we also tested C1V1–porcine teschovirus-1 2A ( P2A ) –mCherry , with the hope that the mCherry would express inside the cytosol of cell bodies , thus allowing direct quantification of single-cell fluorescence . Alas , the efficiency of photoactivation of this construct was much lower than the C1V1–ts–EYFP , for unknown reasons . We are thus currently working to develop soma-targeted C1V1-EYFP for both high efficiency photoactivation and quantification of expression [5] . We conclude that more powerful molecular tools and gene delivery techniques further advance their utility in NHPs . AOI using C1V1 results in much lower tissue damage than what would be caused by repeated probe penetration , or from the photodamage expected with ChR2 constructs [16 , 20] . A primary limitation of our method arose from the 2P imaging-depth limit . This confined our AOI to superficial cortical circuits , lying within 500 μm of the surface [43] . New multiphoton microscopy methods will improve and extend the depth limit of AOI to as deep as 1 mm [44] . Cellular-resolution imaging of subcortical structures is currently achievable with fiber-optic confocal laser endomicroscopy ( CLE ) techniques [45 , 46] . Because the co-expression level of C1V1 and GCaMP was low in our experiments , it is unlikely that we accidentally stimulated unseen dendrites of untargeted neurons while stimulating our target neurons . But this problem—unwittingly stimulating unwanted hidden dendrites that drive neurons other than the targeted neurons—will rise in significance as expression density improves . Soma-targeted opsins serve to minimize this concern [5] , which is why we are currently working to develop soma-targeted C1V1–EYFPs that could improve specificity of 2P stimulation . Electrical microstimulation of the visual cortex evokes phosphene perception in humans , as well as saccadic eye movements in NHPs [39 , 47–50] . Similarly , optical stimulation of monkey V1 has been reported to induce saccades [13] , which we also observed . One refinement of our current design over prior work was to include a control condition in which we targeted an unlabeled region of cortex to rule out potential non-optogenetic artifacts related to laser activation . Interestingly , the animals did not immediately respond to Opto Stim when switching from the Visual Stim block . Though both monkeys required fewer than 30 trials to first detect the Opto Stim , this could indicate that the percept derived by the Opto Stim was not identical to that derived from the Visual Stim . If so , the monkeys might have generalized their initial responses to novel stimuli ( triggered by the Opto Stim ) , in much the same way as they might do during operant conditioning of an unfamiliar visual stimulus . Moreover , we discovered that saccadic responses were faster when elicited by optogenetic stimulation of visual cortex than by real visual stimuli , which follows from the known latencies of transmission from the retina and subcortical visual pathway . Because of the tight homology between the human brain and the NHP brain , the functional characterization of neurons and neural circuits underlying high-level cognition—and cognitive decline—as well as neurological and psychiatric disorders remains heavily dependent on NHP research . Primates , moreover , are the only foveate mammals; thus , they are the only animal model with human-equivalent visual capabilities and oculomotor behaviors [17 , 18] , which makes NHPs a critical animal model for human visual perception , as well as the development and testing of clinical therapies and neural prosthetics . By integrating optogenetics and calcium imaging , AOI offers the ability to precisely determine and manipulate fine functional maps in real time during NHP behavior . One of AOI’s main functions is the precise manipulation of single neurons and simultaneous monitoring of connected neuronal activity to determine the strength of connectivity within neural circuits without unwanted activation of nearby targets .
All procedures involving animals were in accordance with the Guide of Institutional Animal Care and Use Committee ( IACUC ) of Peking University Animals , and approved by the Peking University Animal Care and Use Committee ( LSC-TangSM-5 ) . Rhesus monkeys ( Macaca mulatta ) were purchased from Beijing Prima Biotech , Inc . and housed at Peking University Laboratory Animal Center . The study used three healthy adult male monkeys 4–6 years of age and weighing 5–7 kg . Two sequential sterile surgeries were performed on each animal under general anesthesia . In the first surgery , a 16-mm–diameter craniotomy was created in the skull over V1 . We opened the dura and injected 200 nl of a 1:1 mixture of AAV1 . Syn . GCaMP6s . WPRE . SV40 ( CS0564 , titer 2 . 2e13 [GC/ml] , Penn Vector Core ) or AAV1 . hSyn . GCaMP5G . WPRE . SV40 ( V4102MI-R , titer 2 . 37e13 [GC/ml] , Penn Vector Core ) and AAV9 . CamKIIa . C1V1 . TS . eYFP . WPRE . hGH ( V4545MI-R , titer 1 . 6e13 [GC/ml] , Penn Vector Core ) at a depth of approximately 350 μm . Injection and surgical protocols for each NHP followed from a previous study [36] . Briefly , a small cover glass ( 6 mm in diameter ) with a single pore ( 0 . 3 mm in diameter ) was used to target the injection pipette and stabilize the cortical surface during each injection . The quartz pipette ( QF100-70-7 . 5 , Sutter Instrument , USA ) was pulled with a 15–20 μm tip using a laser-based pipette puller ( P-2000 , Sutter Instrument , USA ) and used for virus injections . After injections , we sutured the dura , replaced the skull cap with titanium screws , and closed the scalp . The animal then returned to its cage for recovery and received Ceftriaxone sodium antibiotic ( Youcare Pharmaceutical Group Co . Ltd . , China ) for one week . A second surgery was performed 45 days later to implant the head posts and imaging window . We used a three-point head-fixation design , with two head posts implanted on the forehead of the skull and one on the back . A T-shaped steel frame was connected to these head posts for head stabilization during subsequent imaging and stimulating sessions . We trained each monkey to sit in a primate chair with its head restrained while performing visual fixation and behavioral choice tasks . Eye position was monitored with an infrared eye-tracking system ( ISCAN , Inc . ) at 120 Hz . Each trial started with the eye fixated on a white 0 . 1-degree point within a window of 1 degree . Visual stimuli were generated using a ViSaGe system ( Cambridge Research Systems ) and displayed on a 17-inch LCD monitor ( Acer V173 , 80Hz refresh rate ) positioned 45 cm from the animal’s eyes . Receptive fields of C1V1- and GCaMP-expressing sites were initially localized with small patches of drifting oriented gratings . We designed a two-block “GO”/“NO GO” detection task in which NHPs made targeted saccades as a means to report perceptually detected Visual Stim or Opto Stim cues ( Fig 4A ) . In the first block ( Visual Stim ) , a real visual object was presented on the monitor as a GO cue . This visual object was flashed for 22 ms approximately 3 degrees peripheral to the fixation point . The NHPs were trained to generate a saccade within 500 ms of cue onset to obtain a juice reward . The central fixation point remained unchanged for the duration of the trial . In the Opto Stim block , a 1P laser pulse ( with a wavelength of 532 nm , a 1 . 0-mm diameter , 0 . 2–2 . 4 mW/mm2 , and a duration of 22 , 44 , or 66 ms ) was projected onto the C1V1-expressing cortical site in each monkey as a GO cue instead of a real visual object . We interleaved trials with either mistargeted laser stimulation of the cortex ( to an area without C1V1 expression; 8 . 3% trials; Mistargeted Stim in Fig 4C and 4D ) or without laser stimulation ( 66 . 7% trials; No Stim in Fig 4C and 4D ) as control trials , allowing us to rule out artifacts related to laser operation . We used a ratio of 1:1 ( No Stim:Visual Stim ) in visual stimulation sessions , whereas we used a ratio of 8:3:1 ( No Stim:Opto Stim:Mis Stim ) in optogenetic stimulation sessions . By using fewer Opto Stim trials , we sought to increase the confidence level of the response data by increasing the NHP decision criteria . After a 10-day recovery period following the second surgery , the animals were trained to fixate their gaze on a fixation point . Imaging was performed using a Prairie Ultima IV 2P microscope ( Bruker Nano , Inc . , FMBU , formerly Prairie Technologies ) and a Ti: Sapphire laser ( Mai Tai eHP , Spectra Physics ) with a 16× objective ( 0 . 8-N . A . , Nikon ) . Whereas 920 nm is a commonly used wavelength for 2P imaging in rodents , we used 1 , 000 nm for our 2P imaging because we found that it achieved higher quality images ( and at deeper depths ) in our NHP experiments [36] . Fast resonant scanning ( up to 32 frames per second ) was used to obtain images of neuronal activity ( 8 fps by averaging every 4 frames ) . To discriminate GCaMP5G from C1V1–ts–EYFP , GCaMP5G fluorescence was acquired with a 920-nm excitation laser using a 500- ± 12 . 5-nm filter , whereas EYFP fluorescence was acquired with a 1 , 040-nm excitation laser using a 525- ± 35-nm filter . To achieve 2P imaging with a 1 , 000-nm excitation source , our power density was approximately 7e-5 mW/um2 ( <50 mW scanning over an 850-μm × 850-μm area ) , which was approximately 10 , 000 times less power than the stimulation power level ( approximately 0 . 3 mW/um2 , 30 mW , 1 , 070 nm focused on a diameter of 10 μm ) . Our imaging laser power could therefore not have caused significant photostimulation of C1V1 . Even if it did , it follows that its effects must have been approximately 10 , 000 times smaller than the effects of our intended photostimulation [34] . A 532-nm laser was used for 1P optical stimulation . The laser was directly pointed at the target cortical area through the imaging window . Due to the brightness of the stimulation laser and the high sensitivity of the PMTs , a 500-nm band pass ( 25-nm width ) filter was inserted before PMT of green channel during simultaneous 2P imaging . Nevertheless , simultaneous stimulation light could have potentially leaked through the filtering system to cause recording artifacts . We addressed this potential confound by blocking the 532-nm laser light during the scanning of the central image during 2P recordings , using an electronic circuit that powered down the full-field stimulation pulse whenever the imaging scan was within the central 75% of the FOV ( S6 Fig ) . A secondary femtosecond laser with 1 , 070-nm wavelength ( maximal power , 2 . 3 watts; pulse width , 50 fs; Fidelity , Coherent , USA ) was used on a secondary galvanometer path in the 2P microscope ( Ultima IV , Prairie , Bruker , USA ) to perform 2P optogenetic activation targeting single cells , while simultaneously recording calcium activity . Spiral regions ( 5 rotations , 1 . 2 expansion rate , 0 . 01 pixel/μs , and 30 repetitions ) were defined to point target photo-activation areas ( S7 Fig ) . The laser power was adjusted to 30 mW at the end of the objective with a polarization beam splitter into 1 , 070-nm femtosecond laser light pathway . Customized Matlab software ( The MathWorks , Natick , MA ) was used to do data analyzing . To correct the image shifts caused by the movement between the objective and the cortex , we first obtained a template image by averaging 1 , 000 frames in the middle of an imaging session and then realigned images from each session to the template image using a normalized cross-correlation–based translation algorithm . The visual stimuli were randomly interleaved during experiments . No data were excluded during analysis . Customized Matlab software was used to perform statistical analysis . As demonstrated in the figure legends , data were presented as individual data points or as mean ± SEM . Number of repetitions for each experiment was also noted within the figure legends . The genetic constructs used in this work are available via Addgene . | This report details the first successful application of long-term all-optical interrogation techniques in monkeys . We have overcome obstacles that prevented the combination of single- and two-photon ( 1P and 2P ) optogenetic stimulation with 2P imaging in awake-behaving monkeys , retesting targeted individual cells and neuronal ensembles over periods that extended beyond 6 months . Our strategy results in repeatable primary visual cortex ( V1 ) neuronal stimulation of the same neurons and produces reliable visual percepts , which monkeys report behaviorally in a visual–motor task . The animals’ behavioral responses to their optogenetic-induced perceptions are comparable to their responses to real visual stimulation . These technical advances establish the feasibility of combined long-term optogenetic manipulation and 2P imaging of neocortical neurons in awake-behaving monkeys . Our approach may be applied to investigate the molecular and circuit-level mechanistic pathways that are unique to primate neural function . These methods also provide a roadmap for preclinical testing of human optogenetic therapies and may serve as the basis for optogenetic studies involving sensorimotor functions relevant to human perception , cognition , behavior , and neurological/psychiatric disorders . | [
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] | 2018 | Long-term all-optical interrogation of cortical neurons in awake-behaving nonhuman primates |
Scabies is a common dermatological condition , affecting more than 130 million people at any time . To evaluate and/or predict the effectiveness and cost-effectiveness of scabies interventions , disease transmission modelling can be used . To review published scabies models and data to inform the design of a comprehensive scabies transmission modelling framework to evaluate the cost-effectiveness of scabies interventions . Systematic literature search in PubMed , Medline , Embase , CINAHL , and the Cochrane Library identified scabies studies published since the year 2000 . Selected papers included modelling studies and studies on the life cycle of scabies mites , patient quality of life and resource use . Reference lists of reviews were used to identify any papers missed through the search strategy . Strengths and limitations of identified scabies models were evaluated and used to design a modelling framework . Potential model inputs were identified and discussed . Four scabies models were published: a Markov decision tree , two compartmental models , and an agent-based , network-dependent Monte Carlo model . None of the models specifically addressed crusted scabies , which is associated with high morbidity , mortality , and increased transmission . There is a lack of reliable , comprehensive information about scabies biology and the impact this disease has on patients and society . Clinicians and health economists working in the field of scabies are encouraged to use the current review to inform disease transmission modelling and economic evaluations on interventions against scabies .
Scabies is a common dermatological conditions [1] , affecting more than 130 million people at any time [2] . It is a neglected disease caused by the mite Sarcoptes Scabiei [3] . Scabies often results in severe itching , and in some patients , including those with compromised immunity , it may progress to “crusted scabies” ( CS ) . The fissures associated with scabies provide a portal of entry for bacteria , potentially resulting in secondary infections , sepsis , indirect effects on renal and cardiovascular function , and death due to complications [4] . Secondary bacterial superinfections are uncommon in Western countries [5] . Worldwide , scabies is responsible for 0 . 07% of the total burden of disease [6] . Compared to its disease burden , scabies research is severely underfunded [7 , 8] , even though it imposes major costs on healthcare systems [2] . Various countries and organisations have identified scabies control as a public health priority and the World Health Organisation Strategic and Technical Advisory Group for Neglected Tropical Diseases recently recommended that scabies be included in the Neglected Tropical Disease profile in category A [2 , 9–11] . Elimination of scabies is difficult , as cured patients often get re-infected . Treatment strategies range from treating individuals and their contacts , to mass drug administration ( MDA ) strategies [12–19] , which involves treating whole communities at once . Drugs include oral ivermectin as well as a range of topical treatment options . It is unknown which ( combination of ) treatment strategies results in the best health outcomes against the lowest costs , and to what extent this differs between communities . Health-economic modelling may help answer such questions . To determine the cost-effectiveness of interventions against scabies , it is crucial to take into account it’s infectious nature since the extent to which interventions impact transmission will ( to a large part ) determine their cost-effectiveness . While such disease transmission approaches have successfully been applied to guide interventions against other infectious diseases like Ebola and influenza [20] , few attempts have been made to use modelling to aid decision-making about scabies intervention strategies . Scabies interventions include efforts aimed at access and coordination of services , scabies detection , primary care , acute care , specialised care , social work , follow-up , or combinations of the above . Given the multifaceted nature of interventions required to combat scabies , health-economic evaluation requires a comprehensive modelling approach . For this article , a systematic review of existing scabies models was conducted to inform the development of a proposed modelling framework which can be adjusted to different situations/communities . The proposed modelling framework aims to determine long-term effects of alternative interventions on the incidence , prevalence , quality of life ( QoL ) , resource use and costs associated with scabies and CS . It can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed . For models to be of use for decision-makers , a range of clinical and economic inputs is required . However , up to now , no systematic overview of evidence-based information on these inputs , including evidence on the biology of scabies , patient QoL and resource use has been published . This systematic literature review fills this gap by providing an overview of published information that can be used to inform scabies modelling in human populations , and improve decision-making about scabies interventions in affected communities . After discussing the search strategy , this paper will first discuss characteristics of published scabies models and a proposed , comprehensive scabies modelling framework . Secondly , the paper will discuss potential model inputs , consecutively: the life cycle of scabies mites , patient QoL , and resource use associated with scabies and CS .
In order to inform our proposed modelling framework design , a systematic literature search was performed on 26 and 27 July 2017 , searching the databases PubMed , Medline , Embase , CINAHL , and the Cochrane Library . Search terms related to the disease ( "scabies" OR "sarcoptes" ) were combined with search terms identifying the type of information required ( "model" OR “modeling” OR “modelling” OR "transmission" OR "utility" OR “quality of life” OR “economics” OR “economic” OR “cost-effectiveness” OR “cost-utility” OR “cost” OR “cost-of-illness” OR “cost-consequence” OR “cost-consequences” OR “efficacy” OR “effectiveness” or “impact” ) . Articles were limited to humans only , had to be published in English and after the year 2000 . Studies from before the year 2000 were only included if they were cited in a more recent source ( post 2000 ) , confirming their continued relevance . Studies that considered scabies but did not present any models or model inputs on scabies biology , QoL or resource use were excluded . Articles were also excluded when they presented a case study of a single patient or an outbreak of scabies in a single institution , and if they simply provided a discussion of scabies guidelines or protocols in a particular country or institution . Those studies that mentioned scabies as one of a number of diseases/indications/causes/comorbidities were also excluded . Reference lists of reviews were used to identify any papers missed through the search strategy . Data extractions were performed by the primary author , per topic area: ( 1 ) models , ( 2 ) biology of scabies , ( 3 ) patient QoL and ( 4 ) resource use . Data items were not predefined , meaning that all data on the various topic areas ( e . g . QoL ) was included and presented , independent of reported outcome measures ( e . g . which type of QoL questionnaire was used ) . After the data was extracted per topic area , it was evaluated which data per topic addressed simple scabies , CS , and which were based on populations including both simple scabies and CS patients . Results were not meta-analysed . The validity and reliability of disease models and inputs is highly dependent on the research question they try to answer , the population of interest , and how the models are informed/how the inputs are used . Therefore , this review did not include a quantitative risk of bias assessment , but a qualitative description of the limitations of the various models and input parameters . All scabies models were evaluated on their main characteristics , and strengths and limitations were identified . Strengths of the various models were combined to design a new , proposed modelling framework . Relevant data from papers on the biology of scabies , patient QoL and resource use was extracted and described . A review protocol has not been published . A completed PRISMA checklist is provided as supplementary material .
Four scabies models were identified from the literature ( Table 1 ) . One model was a Markov decision tree , two were compartmental models , and one was an agent-based , network-dependent Monte Carlo model ( see Table 2 for a description of these model types ) . None of the models identified specifically addressed CS . Bachewar et al . [21] published the Markov decision tree as part of a randomised clinical trial comparing three alternative treatment regimens . As opposed to the other models , this model does not consider scabies epidemiology , transmission or population dynamics . A Markov decision tree is provided , which is used to calculate the cost-effectiveness of alternative treatments , using efficacy data from the trial . The model serves as a mechanism to calculate and compare costs for a range of interventions , without considering the biology or transmission of scabies . This limits its use compared to the other identified models . Gilmore [22] published an agent-based Monte Carlo model , using a variety of small-world network architectures to gain insight into scabies dynamics and the effect of alternative treatment strategies . This study focused on childhood scabies and found that in the absence of an effective vaccine , and with scabies continually imported to communities from non-local contacts , eradication is impossible and open-ended treatment regimens are required . A crucial advantage of Gilmore’s model is that it allows for non-random mixing patterns [25] , since it is likely that the contacts between individuals that result in scabies infestation , do not occur at random [26] . Mixing patterns are a characteristic of a network ( e . g . community ) referring to the extent to which nodes ( e . g . people ) connect ( e . g . are in close enough contact to result in infection ) . Bhunu et al . [23] published a deterministic , compartmental model , using Descartes’ rule of signs and numerical simulations to show endemic equilibria and determine whether the current treatment regime is sufficient to control scabies infection , or whether a vaccine is required . Assumed values for key model parameters were not substantiated , and it is not clear what the model is calibrated to . This means it is impossible to evaluate the reliability of the model and any of its results . The model focussed on predicting the potential impact scabies vaccination might have in case a vaccine would become available . It should likely be viewed as a theoretical exercise rather than one that provides actual insights into scabies epidemiology or into the effectiveness of any available intervention . Lydeamore et al . [24] recently published another compartmental model to explore the impact of alternative MDA treatment strategies . As opposed to the other models , this model aimed to capture the mite’s life cycle in relation to the host . The authors considered this critical , as the parasite’s life state ( e . g . eggs versus living mites ) can interact critically with treatment success or failure . This is a valuable model characteristic when studying the effectiveness of different treatment types ( e . g . , ovicidal versus non-ovicidal ) . In contrast to the model by Gilmore at al . , homogeneous mixing is assumed . None of the models included QoL . An advantage of including QoL is that it can be used to quantify the impact of a wide range of conditions and that ( under certain requirements ) it can be multiplied with duration of life to obtain QALYs ( quality-adjusted life years ) . By measuring the impact of interventions on QALYs , outcomes can be compared not only between different ( types of ) interventions but also across different disease areas . This is needed to inform decision-making , particularly when funds need to be distributed over interventions/programs in a range of different areas . In models , QoL can be used as outcome measure , for example by weighting health states ( e . g . “CS grade 1” ) by their associated utility value . Furthermore , disutilities can be attached to complications as well as treatment-related adverse events . The same is true for costs , which can be attached to the various health states and events in cost-effectiveness models . Only the model by Bachewar et al . included a cost-effectiveness analysis , but it did not take into account transmission dynamics . Combining the strengths of the abovementioned models , Fig 2 provides a proposed modelling framework to inform cost-effectiveness analyses . This framework can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed . Like the model by Gilmore ( 2011 ) , it allows for modelling networks and mixing patterns . This is a valuable attribute in case the model will be used for evaluating the cost-effectiveness of interventions which aim , for example , to prevent reinfection through household level interventions ( e . g . ensuring treated CS patients return to scabies-free homes ) , or community interventions to reduce the prevalence of scabies . Mixing patterns can be based on assumptions or , preferably , appropriate data collection . For example , to inform scabies transmission modelling in indigenous communities in Australia , information is currently being collected on the number of infections and reinfections in the various communities , living conditions ( e . g . number of persons per household ) , and the extent to which scabies-free zones are being established when CS patients return from the hospital . Network size and structure will be dependent on the type of community that is being modelled , including age structure since children tend to have a higher probability of acquiring scabies [27] . For more information on challenges when modelling contact networks , see Eames et al . 2015 [28] . The proposed modelling framework also aims to capture the biology and natural history of scabies transmission in humans , in particular the life-cycle of the mites as this can impact treatment success rates [24] . Depending on the modelling question at hand , it may be possible to simplify the proposed modelling framework or use other types of modelling methods . For example , when the difference between ovicidal versus non-ovicidal treatments is irrelevant to the question at hand , it may not be needed to track life-cycle stages of mites in individual patients . As a rule , simple models should be preferred over complex ones when the decision problem allows , since they are easier to understand , less prone to inaccuracies , and quicker to develop and run [29] . On the other hand , oversimplification may result in unreliable or invalid results when relevant risk-factors or dependencies between modelled states or agents are not taken into account . While it may be possible to simplify the model for some questions , others might require additional health states , for example to allow modelling of long-term complications ( e . g . chronic renal failure ) and their effects . For more information on different types of modelling methods , see Siettos and Russo 2013 [30] . Within the proposed modelling framework , model time should be counted in days , to account for processes like infection , the scabies life-cycle , and treatment effects . Other processes may take substantially longer , such as processes related to certain complications , and impacts on life expectancy . For most modelling questions , a life time horizon will be sufficient . None of the identified models explicitly included CS . This is a shortcoming , since CS is associated with high infectivity , morbidity and mortality compared to simple scabies , and has often been overlooked in scabies program design . The proposed modelling framework incorporates a probability of moving from simple scabies to CS , which can be dependent on ( amongst other factors ) immune status of the patient . While other infectious disease models often include some notion of seasonality , this has not been the case for scabies models . Although scabies mites show increased mite movement and increased transmission in a warm environment , a study in Malawi found that scabies was more prevalent during the cold , dry season , possibly due to close interpersonal contact in crowded indoor environments [31] . Other studies , however , show no obvious seasonal variation at all [31–33] . In the absence of evidence to the contrary , the proposed modelling framework does not accommodate seasonality . The following sections of this paper discuss input parameters that can be used to inform the suggested modelling framework , or other newly developed scabies models . Following Lydeamore et al . [24] , it is crucial to account for the life cycle of the scabies mites to model treatment effectiveness , especially when aiming to discriminate between ovicidal and non-ovicidal treatments . The literature review identified 14 articles presenting information on the life cycle of scabies mites ( see Table 3 ) . Since there is often a lack of Sarcoptes scabiei var . hominis mites , many studies have relied on animal strains of scabies mites and a host animal model such as rabbits or pigs [34] . The studies obtained through the search strategy did not provide any other information specifically on the life cycle of Sarcoptes scabiei var . hominis apart from “survival away from host” . However , based on direct comparisons , Sarcoptes scabiei var . canis seems to be a suitable model for sarcoptes scabiei var . hominis [34 , 35] . The literature review identified 3 QoL studies performed in scabies patients: 1 from China ( = 96 ) , 1 from Brazil ( n = 105 ) and 1 from India ( n = 102 ) . None of these studies addressed the QoL of CS patients compared to the QoL associated with simple scabies , and two of the studies [48 , 49] excluded CS patients . Jin-Gang et al . [48] used the Dermatology Life Quality Index ( DLQI ) , and Worth [50] and Nair [49] used a modified version of that same questionnaire . Modifications made by Worth et al . included: 1 ) adapting the language to local culture and attitudes; 2 ) modifying questions to increase relevance for persons living in an urban slum in the tropics; and 3 ) changing questions that were not applicable in children . Nair et al . used the modified version from Worth et al . , with slight modifications as per the requirements of the Indian population . Table 4 shows QoL results from the three studies , as per category of effect from scabies on QoL . Note that categorisation was based on classifiers described in the studies ( e . g . “small effect” ) , not the modified DLQI item scores , since the questionnaires differed slightly between studies . Jin-Gang et al . reported a mean DLQI score of 10 . 09 ( sd 5 . 96 ) , with most QoL impact of scabies due to symptoms , embarrassment , work or study and sexual difficulties . Most common categories of impairment according to Worth et al . were feelings of shame ( 77 . 2% in adults , 46 . 6% in children ) , the need to dress differently ( 35 . 1% in adults , 29 . 3% in children ) , restriction on leisure activities ( 24 . 6% in adults , 36 . 8% in children ) , stigmatisation at work/school ( 21 . 1% in adults , 25 . 0% in children ) , social exclusion ( 24 . 6% in adults , 17 . 9% in children ) , teasing ( 26 . 3% in children ) , and problems with sexual partners ( 10 . 9% in adults ) . Women/girls perceived more restrictions than men/boys . A review of studies using the Children’s Dermatology Life Quality Index ( CDLQI ) questionnaire to measure QoL in skin conditions [51] found an overall estimated CDLQI score of 9 . 2 ( 95%CI: 0 . 0–20 . 3 ) associated with scabies . The review identified two studies that were not identified in our literature review: Balci et al . [52] and Lewis-Jones & Finlay [53] . Both included children with a wide range of skin diseases , including only few scabies patients ( n = 9 and n = 6 , respectively ) . Olsen et al . commented that while scabies might have a large effect on QoL at the time of completing the questionnaire , this may only be over a short time as it is curable . While also curable , the disutility of CS may be more substantial , given the severity of associated symptoms and complications . While simple scabies is relatively straightforward to treat , patients may not seek care , may wait a long time before doing so , and may be misdiagnosed . In Cameroon , Kouotou et al . [54] found that it takes 4 to 720 days between the onset of symptoms and the first consultation with a dermatologist , with a mean of 77 . 1 days ( sd 63 . 7 ) . At the first consultation with a dermatologist , 74 . 9% had already tried previous treatment , such as antibiotics , antifungals , antihistamines or plant-based medicines . Based on claims data for the employer-sponsored privately-insured population in the United States , treating one episode of scabies costs on average 95 USD [55] . When selecting on episodes for which drug treatment was claimed , costs were 163 USD per episode . Given the incidence of scabies , this results in an overall annual economic burden of 10 . 4 million USD for treating scabies in this population , most of which ( 3 . 7 million USD ) is for children ≤15 years . Costs between alternative treatment options differ substantially . For a cost-benefit analysis from a US perspective , we refer to Elgart [56] . In some populations , scabies-associated health resource use is substantial . In five remote communities of Northern Australia , only a few aboriginal children ( 16% ) manage to reach their first birthday without having at least one documented episode of scabies and/or skin sores [57] . Here , the median number of presentations per child under 12 months due to scabies is 3 ( IQR 1 , 5 ) . Of these children , 70 . 5% present more than once , and the average age of first presentation is 4 months ( IQR 2–7 ) . In another Australian study , Whitehall et al . [32] found that the mean duration of hospital admissions for children with scabies is 4 . 5 days . Scabies comprised 4 . 2% of the total number of admissions for all reasons , and 8 . 3% of all bed days . The minimum cost per admission was 9 , 584 . 07 AUD . In Australia , the estimated annual cost associated with the management of pediatric scabies and pyoderma per patient was 10 , 000 AUD in 2013 [58] . Resource use may differ substantially between locations/communities , depending on the healthcare system , funding , remoteness , and cultural differences , amongst other factors . Local data collection will generally be required to inform model inputs . Resource use or costs for treating CS have not been published . A cost-of-illness study from an Australian perspective is currently being performed by ( part of ) the authors of this paper . Note that the cost-effectiveness of interventions to prevent CS may be substantially impacted by their ability to prevent long-term complications ( e . g . rheumatic fever and chronic valvular heart disease ) which may increase costs and decrease patient life expectancy and quality of life .
Based on a systematic literature review , this paper discusses published models and proposes a new , comprehensive modelling framework to develop cost-effectiveness analyses of treatments for scabies . Models should be informed by population , disease and treatment characteristics , which may differ between communities . Available information on required model inputs was systematically reviewed . Prior to this review , the literature lacked a good account of these inputs , including the life cycle of scabies mites , patient QoL , and resource use . This review resolves this problem and should be supplemented by locally specific data collections and expert opinion where required . There is a lack of reliable , comprehensive information about scabies biology and the impacts this disease has on patients and society . This may be due to the limited amount of resources directed towards scabies research [7 , 8] , and its tendency to affect resource-poor populations . Given the efficacy of available treatments and the relatively low costs of these treatments ( although still prohibitively expensive in some low-income settings ) , current high prevalence rates of scabies are unacceptable . Interventions should aim to reduce scabies incidence in a sustainable , cost-effective manner . In doing so , it may be worth focusing additional efforts on identifying and treating patients with CS , who can be “core transmitters” of the disease , while experiencing high morbidity and mortality rates [38] . The importance of targeting CS patients has often been overlooked in program design for simple scabies . Scabies elimination efforts should be prioritised for communities that are worst affected , and with sustained intervention , this is a realistic goal [59] . Given that many of these communities are resource-poor , cost-effective use of resources is crucial and can be informed by health-economic modelling , taking into account community-specific resource constraints and expected budget impact of proposed interventions . Furthermore , careful data collection ( for example , aided by making scabies a notifiable disease ) may help guide funds to where they are most needed . While the current article provides a comprehensive overview of key issues and a proposed modelling framework to aid future scabies modelling work , it is only a first step in this direction . Researchers and policy makers are encouraged to use and adjust this modelling framework to develop an economic evaluation predicting the ( cost ) -effectiveness of interventions against scabies in their population ( s ) of interest . Any input on the proposed modelling framework by external parties is welcomed . As with all health economic models , model transparency and validation of the results is critical to its success and potential impact . The current modelling framework has not been validated and should only be used as an aid for model development . By using the current review and proposed modelling framework to substantiate the modelling approach and select appropriate inputs , transparency can be improved . Proper validation involves face validity , verification of internal validity , external validity , and predictive validity . Health economists and modellers working in the field of scabies are referred to the ISPOR report on model transparency and validation for recommendations on how to appropriately validate and report on their model and results in a population of interest [60] . For data inputs that are uncertain , real-world data collection may be crucial to ensure reliability of modelling outcomes . Furthermore , the impact of uncertain model inputs can be tested by using sensitivity analyses to determine how variation in modelling inputs impacts the results , both deterministically and probabilistically . Given the identified knowledge gaps , it is important to perform extensive sensitivity analysis in any scabies model that will be developed . Meanwhile , grant bodies are encouraged to invest in scabies research to address the knowledge gaps identified in this review regarding the biology , QoL and cost impact of simple scabies and CS . As far as the authors are aware , transmission modelling has seldom been used to answer questions on scabies interventions . One reason for this may be the lack of readily available information to inform modelling work , which this review aims to ( at least partially ) address . Another reason may be unfamiliarity or scepticism on the side of authorities and funding bodies with respect to the value of theoretical results obtained from modelling . Health economists and other scientists can best illustrate the value of modelling by using evidence-based , validated approaches to tackle relevant , real-world questions which can directly inform clinical or governmental decision-making . | Scabies is a neglected tropical disease affecting more than 130 million people , with major costs on health care systems worldwide . While effective treatments exist , it is unknown which treatment strategies result in the best outcomes against the lowest costs , and to what extent this differs between communities . Health economic modelling can help answer these questions , but has rarely been used in this disease area . This review discusses all available scabies transmission models ( n = 4 ) , and uses them to create a new , comprehensive modelling framework . This framework can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed . The current paper also reviews the data that is needed to inform scabies modelling: on scabies biology , quality of life and resource use . Unfortunately , available data is limited and particularly data on crusted scabies ( associated with high morbidity and mortality rates ) is rare . With this review , we hope to assist researchers and policy makers to predict and/or evaluate the cost-effectiveness of interventions against scabies in their population ( s ) of interest . To tackle scabies , it is key to use effective treatment strategies in a cost-effective and sustainable way . The models and data described in this review , may help researchers , clinicians and funding bodies to facilitate this . | [
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] | 2019 | A systematic review of scabies transmission models and data to evaluate the cost-effectiveness of scabies interventions |
Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry ( MALDI-TOF MS ) is an emerging tool for routine identification of bacteria , archaea and fungi . It has also been recently applied as an accurate approach for arthropod identification . Preliminary studies have shown that the MALDI-TOF MS was able to differentiate whether ticks and mosquitoes were infected or not with some bacteria and Plasmodium parasites , respectively . The aim of the present study was to test the efficiency of MALDI-TOF MS tool in distinguishing protein profiles between uninfected mosquitoes from specimens infected by filarioid helminths . Aedes aegypti mosquitoes were engorged on microfilaremic blood infected with Dirofilaria immitis , Brugia malayi or Brugia pahangi . Fifteen days post-infective blood feeding , a total of 534 mosquitoes were killed by freezing . To assess mass spectra ( MS ) profile changes following filariae infections , one compartment ( legs , thorax , head or thorax and head ) per mosquito was submitted for MALDI-TOF MS analysis; the remaining body parts were used to establish filariae infectious status by real-time qPCR . A database of reference MS , based on the mass profiles of at least two individual mosquitoes per compartment , was created . Subsequently , the remaining compartment spectra ( N = 350 ) from Ae . aegypti infected or not infected by filariae were blind tested against the spectral database . In total , 37 discriminating peak masses ranging from 2062 to 14869 daltons were identified , of which 17 , 11 , 12 and 7 peak masses were for legs , thorax , thorax-head and head respectively . Two peak masses ( 4073 and 8847 Da ) were specific to spectra from Ae . aegypti infected with filariae , regardless of nematode species or mosquito compartment . The thorax-head part provided better classification with a specificity of 94 . 1% and sensitivity of 86 . 6 , 71 . 4 and 68 . 7% of D . immitis , B . malayi and B . pahangi respectively . This study presents the potential of MALDI-TOF MS as a reliable tool for differentiating non-infected and filariae-infected Ae . aegypti mosquitoes . Considering that the results might vary in other mosquito species , further studies are needed to consolidate the obtained preliminary results before applying this tool in entomological surveillance as a fast mass screening method of filariosis vectors in endemic areas .
Mosquitoes are blood-sucking arthropods with a global distribution . They represent a huge threat to humans and animals as vectors of pathogens [1 , 2] . In passing from host to host , some mosquito species may transmit parasitic diseases ( i . e . malaria , lymphatic filariosis and dirofilariosis ) , arboviroses ( i . e . dengue , west nile , zika , eastern equine encephalitis disease and others ) [3 , 4] , and possibly bacterial diseases ( i . e . Rickettsia felis infection ) [5] . Dirofilarioses due to Dirofilaria immitis and Dirofilaria repens are mosquito-borne parasitic infections of dogs and other wild carnivores , which function as reservoirs . Humans and cats are less suitable hosts [6 , 7] . D . immitis has a worldwide distribution and it is endemic in tropical and temperate regions throughout the world , whereas D . repens is exclusive to the Old World [8] . Lymphatic filariosis ( commonly known as elephantiasis ) is a neglected human borne-disease caused by infection with three different filarioid worms . Most of the infections worldwide are caused by Wunchereria bancrofti [9] . However , in Asia the disease can also be caused by Brugia malayi and Brugia timorii [10] . Brugia pahangi , another zoonotic lymphatic filarioid nematode that is naturally found in cats but also found in other types of hosts , can cause clinical infection in humans , with clinical presentations that are consistent with lymphatic filariosis [11] . All these filariae parasites have biphasic life cycles involving the definitive mammalian host and various genera of mosquito vectors , including Aedes , Anopheles , Culex , Mansonia , and Ochlerotatus [7 , 12] . The capture and identification of mosquitoes , as well as the detection of associated pathogens , are important steps for monitoring mosquito-borne diseases like dirofilariosis and lymphatic filariosis . Mosquito identification is performed using mainly morphological keys and/or molecular methods [13] . However , screening the mosquitoes according to their filarioid infection rate is based on dissecting freshly killed , individual female mosquitoes . In fact , mosquito dissection is considered the gold standard for measuring infection rates and densities in the vector [14] . However , this is a labor-intensive and time-consuming procedure requiring entomological expertise [15 , 16] . Molecular methods such as PCR and gene sequencing have been developed as a tool for detecting filarioid parasite DNA in mosquitoes . These methods have been applied as molecular xenomonitoring of filariosis [17] . However , molecular techniques are relatively expensive . But , sometimes , for economic reasons , it is not possible to routinely use molecular biology as a monitoring tool for mosquito vectors . Therefore , a faster and more cost-effective technique for the simultaneous identification of mosquito vector species and detection of their associated pathogens could improve entomological surveillance of mosquitoes and mosquito-borne diseases . MALDI-TOF MS has been introduced as a routine method in diagnostic microbiology laboratories for identifying bacteria , archaea and fungi isolated from different samples [18 , 19] . More recently , this proteomic approach has been used with success in the identification of arthropods such as mosquitoes , fleas and ticks [13] . In addition , two preliminary studies showed the ability of MALDI-TOF MS to differentiate ticks infected or not infected with Borrelia crocidurae or Rickettsia spp . using specimen legs [20 , 21] . Finally , MALDI-TOF MS showed a good performances of specificity ( 100% ) and sensitivity ( 92% ) when this tool was applied to screen mosquitoes infected or not infected with Plasmodium berghei protozoan parasites [22] The aim of this study was to determine MALDI-TOF MS’s effectiveness in detecting changes in the protein profiles of Ae . aegypti mosquitoes infected with filarioid helminths compared to uninfected ones .
Aedes aegypti ( Black-eyed Liverpool strain ) were artificially infected by feeding them on a membrane feeder which contained blood with microfilariae , as previously described [23] . This experiment was conducted at TRS Labs , Inc . in Athens , Georgia ( USA ) under AUP 15–07 ( 2 ) . The protocol was approved by the laboratory's Institutional Animal Care and Use Committee ( IACUC ) prior to the study beginning . D . immitis , B . malayi and B . pahangi infected and non-infected Ae . aegypti were provided by TRS Labs , Inc . in Athens , Georgia ( USA ) . For each nematode species two experimental groups of four- to six-day-old female mosquitoes were constructed: one infected group in which mosquitoes were fed with microfilaremic blood and one control non-infected group in which mosquitoes were feed with non-microfilaremic blood . All mosquitoes were starved for 24 hours prior to blood feeding . In brief , D . immitis microfilaremic blood were collected from naturally infected dog into syringes containing 3 . 8% sodium citrate . Mosquitoes were fed for at least 1 hour using an artificial feeding system ( Hemotek feeding system; Discovery Workshops , Lancashire , United Kingdom ) [24] loaded with 3 mL of infected ( 5 , 000 mf/ml ) or amicrofilaremic blood containing sodium citrate anticoagulant ( control ) . While , for B . malayi and B . pahangi infected or uninfected mosquitoes , female Ae . aegypti were allowed to feed for 40 mins on anaesthetized , infected or uninfected ( control ) jirds , Meriones unguiculatus with microfilaremiae of B . malayi or B . pahangi ranging from 192–1 , 008 mf/20 mL blood . After the blood meal , all mosquitoes were fed on 10% sucrose solution and kept under standard laboratory-rearing conditions for 15 days , the timeframe necessary for the mosquito parasite cycle . Subsequently , mosquitoes were killed by putting them in dry ice and stored at– 20°C for subsequent analysis .
Infection rates for each group of Ae . aegypti tested by qPCR were as follows: in Group 1 ( legs to be tested by MALDI-TOF MS ) , D . immitis , B . malayi and B . pahangi DNA were respectively detected in 77 . 3% ( 58/75 ) , 90% ( 54/60 ) and 83 . 5% ( 66/79 ) of mosquitoes ( Table 1 ) . Infection rates for Group 2 ( Thorax to be tested by MALDI-TOF MS ) , the infection rates were 73 . 9% ( 17/23 ) , 95 . 4% ( 42/44 ) and 80% ( 12/15 ) for D . immitis , B . malayi and B . pahangi , respectively . As for Group 3 ( thorax-head to be tested by MALDI-TOF MS ) , were 28 . 6% ( 18/63 ) , 62 . 5% ( 10/16 ) and 79 . 16% ( 19/24 ) for D . immitis , B . malayi and B . pahangi , respectively . Lastly , the infection rates for Group 4 ( head to be tested by MALDI-TOF MS ) , were 95 . 6% ( 22/23 ) and 85 . 7% ( 12/14 ) B . malayi and B . pahangi , respectively ( D . immitis was not tested by qPCR for this group because of a lack of samples ) ( Table 1 ) . A total of 428 body parts of Ae . aegypti mosquitoes were submitted for MALDI-TOF MS analysis . First , the MS spectra were assessed visually by comparing the average spectra ( MSP Main Spectrum Profile ) obtained from the four spectra of each sample tested using the flexAnalysis v3 . 3 and ClinProTools v2 . 2 software ( Bruker Daltonics ) . Inadequate spectra ( i . e . MS with low quality ) were excluded from the study . For example , all samples providing MS of which the most intense peaks were less than 2000 a . u . or with no detected spectra were systematically excluded . Based on these criteria , a total of 31 MS were excluded from the study . Next , spectra with a good reproducibility of at least two specimens per compartment ( control uninfected and filariae infected mosquitoes ) were randomly selected and loaded in MALDI-Biotyper 3 . 0 software to create a reference database . Thus , a total of 47 mosquito body parts were used to create this reference database . They are allocated as follows: 21 legs ( 5 control , 5 infected with D . immitis , 5 infected with B . malayi and 6 infected with B . pahangi ) , 9 thorax ( 2 control , 2 infected with D . immitis , 3 infected with B . malayi and 2 infected with B . pahangi ) , 10 thorax-head ( 3 control , 2 infected with D . immitis , 2 infected with B . malayi and 3 infected with B . pahangi ) and 7 head ( 3 control , 2 infected with B . malayi and 2 infected with B . pahangi ) . The remaining MS ( 350 mosquito parts ) were blind tested against the database . Visual inspection of spectral profiles obtained from different compartments showed consistent and reproducible spectra between specimens according to the compartments , namely legs ( Fig 2 ) , thorax ( Fig 3 ) , thorax-head ( Fig 4 ) and head ( Fig 5 ) , and the infectious status . Spectra alignment using Flex analysis software confirmed reproducibility but also revealed changes in the MS pattern according to the infectious status , with mass peaks present or absent between infected and uninfected mosquitoes . In total , 37 discriminating peak masses ranging from 2062 to 14869 Da were identified ( Table 2 ) , of which 17 , 11 , 12 and 7 peak masses were for legs , thorax , thorax-head and head spectra respectively . For Group 1 ( legs ) , regardless of the filariae species with which mosquitoes were infected , spectral profile analysis showed that there were at least two protein peaks ( 3509 and 14869 Da ) only present in spectra obtained from control mosquitoes compared to the infected ones ( Fig 2 ) , while three peak masses ( 2062 and 4073 and 8847 Da ) were exclusively present in infected mosquitoes ( Table 2 ) . For Group 2 ( thorax ) , three protein peaks ( 4073 , 8847 and 1071 Da ) were present in the infected mosquitoes compared to the non-infected ones ( Fig 3 ) . As for Group 3 ( thorax-head ) , six protein peaks ( 4073 , 5637 and 8847 Da ) and ( 2759 , 4179 and 6498 Da ) were only found in infected and control uninfected mosquitoes , respectively ( Fig 4 and Table 2 ) . Finally , for Group 4 ( head ) , two protein peaks ( 4073 and 8847 Da ) were found only in infected specimens compared to uninfected ones . It is important to note that of the 37 peak masses , two ( 4073 and 8847 Da ) were observed in all groups of filariae infected mosquitoes ( regardless of species ) compared with uninfected ones ( Table 2 ) . Discriminating peak masses can be present in all filarioid-infected specimens but overexpressed for one species more than others . For example , the 5290 , 6126 , 6781 , 7827 Da peaks are intensely expressed in mosquitoes’ legs ( Group 1 ) infected with B . malayi and B . pahangi compared to the D . immitis infected specimens ( Table 2 ) . Concerning Group 2 , the 2329 Da peak was intensely expressed in the mosquitoes infected with B . malayi and B . pahangi compared to those infected with D . immitis . As for Group 3 , the 3001 Da peak was more noticeable in the mosquitoes infected with D . immitis and B . pahangi compared to those infected with B . malayi . Finally , for Group 4 , one peak ( 2828 Da ) was more expressed in mosquitoes infected with B . malayi compared to those infected with B . pahangi . The specificity , estimated using control uninfected mosquitoes , varied slightly depending on tested compartment . It was 85 . 1% for leg , 76 . 9% for thorax , 94 . 1% for thorax-head and 80% for head analysis . In addition , the blind test showed correct identification rates for infected specimens varying according to the compartment tested . The sensitivity was 82 . 9% for legs , 60% for thorax and 86 . 6% for thorax-heads infected with D . immitis ( Table 3 ) . It was 61 . 6% for legs , 65 . 7% for thorax , 71 . 4% for thorax-head and 84 . 2% for heads infected with B . malayi . It was 75 . 4% for legs , 70% for thorax , 68 . 7% for thorax-heads and 70% for heads infected with B . pahangi .
This is the first study conducted on using MALDI-TOF MS to detect filariae in mosquitoes . Here , the ability of the MALDI-TOF MS to detect filariae in mosquitoes was evaluated using qPCR as a “gold standard”/reference . The reliability of nucleic acid amplification techniques for filariae detection in vectors has been addressed in a number of studies [25 , 26 , 29] . These studies showed that these PCR assays had high sensitivity and specificity toward the detection of the filariae in mosquitoes . In a recent study [22] it was reported that MALDI-TOF MS can correctly screen ( 100% of specificity and 92% and sensitivity ) mosquitoes infected or not infected with Plasmodium berghei parasites using head and thorax as the target part . Here , we investigated whether the MALDI-TOF MS tool could detect changes in the protein profiles of non-infected and filariae-infected Ae . aegypti mosquitoes , in other words generate a profile reflecting the infectious status . We were also interested in assessing which mosquito compartment was appropriate for determining infectious status using MALDI-TOF MS . It is worth noting that after ingestion by the mosquito , Dirofilaria spp . microfilariae remain in the midgut for approximately 24 h . Subsequently , they migrate into the large cells of the malpighian tubules [6] . After two molts ( L2 , L3 ) the filariae perforate the distal ends of the tubules and migrate via the haemocoel to the head of the mosquito on the 15th to 17th day [30 , 31] . For Brugia spp . development in mosquito , after ingestion , the microfilariae lose their sheaths and perforate the wall of the proventriculus and cardiac portion of the midgut to reach the thoracic muscles [32] . At this level , the microfilariae develop into first-stage larvae ( L1 ) and subsequently into third-stage larvae ( L3 ) within 8 to 10 days after the infecting blood meal [32 , 33] . Subsequently , the L3 larvae migrate through the hemocoel to the mosquito's prosbocis on the within 14 to 20 days . A small minority of larvae may stay in the haemocoele or enter some other thoracic structure in which they stay without signs of development [33] . Previous studies showed that changes occur in the mosquitoes' hemolymph as a result of infection by microorganisms [34 , 34–36] and this can provide a useful approach for examining changes in hemolymph proteins after infection by parasites [36] . It is acknowledged that these proteins may play important roles in the relationship between mosquitoes and the viral , protozoal and nematode pathogens they transmit [36] . In their study , Paskewitz et al . ( 2005 ) focused on evaluating changes in the protein profiles in the hemolymph of Anopheles gambiae following bacterial ( Escherichia coli ) inoculation , identifying 26 hemolymph proteins that belong to families linked to immunity , lipid transport , and iron regulation in insects [36] . Shi et al . ( 2004 ) reported two bacterial infection-related proteins in An . gambiae hemolymph using 2D SDS PAGE analysis . These two mosquito proteins are involved in immunity because they appear early in the hemolymph following mosquito exposure to bacterial infection , but not to other treatments that cause damage to the mosquito's body wall [37] . In their study , Brenda et al . ( 1990 ) showed that there is an increase in biosynthesis of the 84-kDa polypeptide in the hemolymph of Ae . aegypti mosquitoes inoculated with D . immitis microfilariae compared with those from saline-inoculated and uninoculated controls [38] . According to these authors , greater synthetization of this protein in D . immitis-inoculated mosquitoes may reflect the production of melanotic material necessary for the encapsulation reactions against microfilariae parasites [38] . All these changes occurring at the hemolymph level represent one of the reasons why we tested different parts of mosquitoes , including legs . Here , the infectious status of each mosquito was validated by means of a high sensitivity molecular tool . It has been demonstrated by dissecting mosquitoes that filariae , especially L3 stage larvae , are found in the abdominal hemocoel 15 days after infection [39] . This validates our analysis approach , in which we have tested the abdomen by qPCR in each group to detect filariae DNA , especially for the group in which head and thorax were tested by MALDI-TOF MS as a whole part . Nevertheless , for this group ( Group 3 ) , a low infection rate ( 28 . 5% ) for D . immitis was obtained , compared to Group 1 and Group 2 in which the infection rates were 77 . 3% and 73 . 9% respectively . This result can be explained by the low density ( or absence ) of the filariae present in the abdomen of the mosquito after migration of the L3 larvae to the head two weeks after the infecting blood meal . A comparison of spectra profiles for control and infected mosquitoes using ClinProTools showed a set of 37 biomarker masses that distinguish mosquitoes according to their infectious status as well as the filariae species with which the mosquito was infected . Of these peak masses , some are present only in infected specimens . It may be inferred that these proteins correspond to filariae proteins circulation or to the immune-induced proteins of the mosquitoes following infection as previously reported in mosquitoes and Drosophila fruit flies challenged with bacteria [36] [40] . Furthermore , we have noted that some discriminating peaks are detected in the uninfected control mosquitoes and are down-regulated or squarely suppressed in the infected specimens . This agrees with published literature in which it has been reported that certain genes coding for proteins involved in innate immunity are down-regulated after bacterial or malaria challenges of Anopheles gambiae mosquitoes [41] . The performance of MALDI-TOF MS for filariae detection in different Ae . aegypti mosquitoes’ compartments was based on the blind test following the database’s creation . The obtained results generally presented specificity and sensitivity rates ranging from 76 . 9% to 94 . 1% , and from 60% to 86 . 6% respectively , according to the target compartment . For legs ( Group 1 ) , the specificity is 85 . 1% while the sensitivity is 82 . 9% , 61 . 3% and 75 . 4% for specimens infected with D . immits , B . malayi and B . pahangi respectively . These values were closer than those reported in a previous study ( 93 . 7% of specificity and 88 . 9% of sensitivity ) in which another pathogen ( Borrelia crocidurae ) was detected in the legs of Ornithodoros sonrai ticks using MALDI-TOF MS [20] . For thorax ( Group 2 ) , the specificity is 76 . 9% while the sensitivity is 60% , 65 . 7% and 70% for specimens infected with D . immitis , B . malayi and B . pahangi respectively . The best specificity and sensitivity results were obtained from the thorax-head compartment ( Group 3 ) with values of 94 . 1% and 86 . 6% , 71 . 4% and 68 . 7% for control uninfected mosquitoes and specimens infected with D . immitis , B . malayi and B . pahangi respectively . In their study , Laroche et al . ( 2017 ) had better results ( 100% of specificity and 92 . 8% of sensitivity ) testing the thorax-head by MALDI-TOF MS to screen Anopheles stephensi mosquitoes infected or not infected with Plasmodium berghei parasites [22] . Lastly , the head ( Group 4 ) generated a specificity of 80% and a sensitivity of 84 . 2% and 70% for control uninfected mosquitoes and specimens infected with B . malayi and B . pahangi respectively . All these values of specificity and sensitivity can be considered good taking into account some limitations of the MALDI-TOF MS such as the relative low resolution and limited sensitivity for larger masses ( MS superior to 20 kDa ) [13] . This limitation may make this tool unable to detect all proteins that can differentiate the filariae species for which mosquitoes are tested . However , another promising technique can be used in combination with MALDI-TOF MS . This method , known as peptide mass fingerprinting or shotgun mass mapping , involves the proteolytic hydrolysis of the sample prior to MALDI-TOF MS reference database creation or interrogation [13 , 42] . It is based on the comparison of peptide MS spectra . The advantages of shotgun mass mapping are greater resolution in the lower mass range ( i . e . from 500 to 4000 Da ) and the ability to obtain peptide sequence information by analyzing the more stringent peptides with tandem mass spectrometry [13] . It is worth noting that the application of this technique in medical entomology has been successfully initiated by Uhlmann et al . ( 2014 ) , by determining the identity of 28 peptide peaks of Culicoides in which the mass ranged from 1 . 1 to 3 . 1 kDa [42] . This study demonstrated the potential of MALDI-TOF MS as a promising tool for screening Aedes aegypti mosquitoes as being non-infected or filariae-infected . For large scale studies , this technique can be applied to screen mosquitoes ( infected/not infected ) and then other tools can be used , such as PCR for pathogen species identification . Moreover , it is recognized that the MALDI-TOF MS-based approaches provides cheaper and faster method for routine microbial species identification than conventional phenotypic and 16S molecular sequencing identification methods , with equal or better accuracy [18 , 43–45] . In a study conducted by Dhiman N et al . ( 2011 ) [46] the authors reported a reagent cost of $0 . 50 and an average hands-on-time of 5 . 1 min per isolate for yeast identification . In their study , Cherkaoui et al . ( 2010 ) [47] reported that of a total of 720 isolates belonging to different bacterial species , the average cost of conventional and MALDI-TOF MS identifications was approximately $10 and $0 . 50 per isolate respectively . In addition , the estimated timeliness of conventional and MALDI-TOF MS methods was 24 h and 5 min per isolate , respectively . In a cost-benefit study published in 2015 , showed that out of 21 , 930 isolates composed of commonly isolated organisms ( e . g . , bacteria and yeast ) the total costs with traditional methods , including reagent , technologist time , and maintenance agreement contracts , were determined to be $6 . 50 per isolate reported , compared to $3 . 14 for with MALDI-TOF MS [44] . It is noteworthy that for 16S molecular sequencing , reagent costs are 5–10 times higher than of MALDI-TOF MS [44] . In addition , the cost of the instrument and software ( $150 , 000 ) is comparable to that for DNA-sequencing platforms [46] . This suggests that , once the MALDI Biotyper machine is purchased , the analyzing cost per sample remains much lower by MALDI-TOF MS than by molecular biology . This implies that in the coming years , MALDI-TOF MS will be a routine tool in monitoring and managing human and animal vector-borne diseases ( e . g . filariosis ) . Furthermore , we recommend that other studies be conducted using other species of mosquitoes challenged with different filarioid species to create a large database and consolidate the results obtained in this scope of research . Additionally , the characterization of the proteins ( i . e . amino acid composition and sequence ) from discriminating peaks will precise the protein candidates involved in MS profile changes following nematode infection . | Filariosis is a disease group affecting humans and animals , caused by nematode parasites of the family Onchocercidae , superfamily Filarioidea . These parasites can be transmitted , essentially , by mosquitoes during blood meals of infected female specimens . Screening vectors for these filariae currently relies on time- and resource-consuming methods such as dissection and polymerase chain reaction-based methods . Here , we applied matrix-assisted laser desorption/ionization time-of-flight mass spectrometry ( MALDI-TOF-MS ) to assess whether this tool can detect changes in the protein profiles of Aedes aegypti infected with filarioid helminths compared to those uninfected by testing different parts of mosquitoes . First a reference mass spectra database from Ae . aegypti infected or not infected by filariae was created using MS from 47 specimen compartments . Then we tested the remaining mass spectra ( 350 x 4 ) in a blind validation test . Regardless of filariae species , the best correct classification rate was obtained from the thorax-head part with a specificity of 94 . 1% and sensitivity of 86 . 6 , 71 . 4 and 68 . 7% for non-infected and D . immitis , B . malayi and B . pahangi infected mosquitoes respectively . The results indicated that MALDI-TOF MS is potentially able to screen Aedes aegypti mosquitoes as being non-infected or filariae-infected . Furthermore , complementary works using other mosquito species infected with different filarioids are needed to reinforce these preliminary results prior to apply this tool on field samples . | [
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] | 2017 | Assessment of MALDI-TOF mass spectrometry for filariae detection in Aedes aegypti mosquitoes |
Small molecule inhibitors of hepatitis C virus ( HCV ) are being developed to complement or replace treatments with pegylated interferons and ribavirin , which have poor response rates and significant side effects . Resistance to these inhibitors emerges rapidly in the clinic , suggesting that successful therapy will involve combination therapy with multiple inhibitors of different targets . The entry process of HCV into hepatocytes represents another series of potential targets for therapeutic intervention , involving viral structural proteins that have not been extensively explored due to experimental limitations . To discover HCV entry inhibitors , we utilized HCV pseudoparticles ( HCVpp ) incorporating E1-E2 envelope proteins from a genotype 1b clinical isolate . Screening of a small molecule library identified a potent HCV-specific triazine inhibitor , EI-1 . A series of HCVpp with E1-E2 sequences from various HCV isolates was used to show activity against all genotype 1a and 1b HCVpp tested , with median EC50 values of 0 . 134 and 0 . 027 µM , respectively . Time-of-addition experiments demonstrated a block in HCVpp entry , downstream of initial attachment to the cell surface , and prior to or concomitant with bafilomycin inhibition of endosomal acidification . EI-1 was equally active against cell-culture adapted HCV ( HCVcc ) , blocking both cell-free entry and cell-to-cell transmission of virus . HCVcc with high-level resistance to EI-1 was selected by sequential passage in the presence of inhibitor , and resistance was shown to be conferred by changes to residue 719 in the carboxy-terminal transmembrane anchor region of E2 , implicating this envelope protein in EI-1 susceptibility . Combinations of EI-1 with interferon , or inhibitors of NS3 or NS5A , resulted in additive to synergistic activity . These results suggest that inhibitors of HCV entry could be added to replication inhibitors and interferons already in development .
Hepatitis C virus ( HCV ) , a member of the Flaviviridae family of positive-strand RNA viruses , chronically infects approximately 170 million people worldwide [1] , [2] . Over time , ongoing virus replication within the liver often leads to severe clinical manifestations such as fibrosis , cirrhosis , and hepatocellular carcinoma [3] , [4] . Consequently , HCV-induced disease is the leading indication for liver transplantation [5] . Medical treatment for HCV is limited by the lack of a vaccine or approved therapies that specifically target the virus . Currently , patients undergo treatment with a combination of parenterally administered pegylated interferon-alpha ( IFN-α ) and oral ribavirin [6] . Genotype 1 HCV has proven to be the most difficult to treat and elimination of the virus ( sustained virologic response ) is achieved for only approximately 50% of patients [7] , [8] . This poor treatment response , combined with often severe side effects induced by therapy , highlight a need for improved antiviral drugs with better efficacy and safety profiles . Studies with isolated HCV replication enzymes and replicon cell-based systems have been exploited to identify several inhibitors of HCV replication that are currently in clinical development [9] . While these have demonstrated potent reduction of circulating virus in early clinical trials , preexisting or rapidly-emerging resistance is a characteristic of the highly mutable HCV genome [9] , [10] . As with HIV treatment paradigms , these results dictate that combination therapy , targeting multiple stages or functions of the HCV infection cycle , will be required to treat HCV . Therefore , we sought to search for inhibitors that could complement those currently in development . HCV encodes two envelope glycoproteins , E1 and E2 , which together mediate binding and entry of the virus into primary hepatocytes and hepatocyte cell lines . The sequence of events leading to virus internalization has not been completely defined , but recent evidence implicates several cell surface molecules in the process . The initial attachment ( adsorption ) of the virus is likely facilitated by a low affinity interaction of E2 with heparan sulfate proteoglycans ( HSPGs ) on the cell surface [11] , [12] , [13] . Subsequently , higher affinity interactions with several host cell surface receptors have been shown to be required for HCV entry . These include the low-density lipoprotein receptor ( LDL-R ) [14] , [15] , [16] , the tetraspanin CD81 [17] , [18] , [19] , [20] , [21] , [22] , scavenger receptor class B type I ( SR-BI ) [18] , [19] , [20] , [23] , [24] , [25] , [26] , and tight junction proteins claudin ( CLDN ) -1 , 6 , or 9 , and occludin [27] , [28] , [29] , [30] . In vitro , non-permissive human , mouse , and hamster cell lines become permissive to infection with HCV pseudoparticles ( HCVpp ) if engineered for ectopic expression of SR-BI , CD81 , CLDN1 , and occludin suggesting these are the core HCV receptors required for entry of the virus into hepatocytes [29] . The LDL-R is postulated to function primarily in the context of serum HCV particles , and may facilitate the attachment or uptake of virions which are complexed with very low density lipoproteins in vivo [31] . There is some evidence to suggest that HCV interacts with CD81 and SR-BI earlier in the entry pathway , followed by CLDN1 and occludin at tight junctions , although the exact order of binding and the role of each receptor remain to be determined [18] , [23] , [27] . Following receptor binding , virions are internalized via clathrin-mediated endocytosis [32] , [33] , [34] , [35] . By analogy to the phylogeneticlly related flaviviruses [36] , [37] , [38] , the reduced pH of the endosome is thought to mediate a conformational change in the HCV virion that facilitates fusion of the viral and endosome membranes , depositing the nucleocapsid into the cytoplasm . Indeed , agents that prevent the acidification of the endosome block HCV entry if added within 3 hr after infection [39] . Structural features characteristic of class II viral fusion glycoproteins of flaviviruses and alphaviruses [40] , [41] have been identified within HCV E1 and E2 , and it remains to be determined if one or both of these proteins mediate the fusion process [42] , [43] , [44] , [45] , [46] . HCVpp , which consist of retroviral or lentiviral cores surrounded by an envelope containing HCV E1 and E2 , have proven to be a valuable surrogate system by which to study the viral and cellular determinants of the viral entry pathway [47] , [48] . The early steps of infection by infectious cell culture HCV ( HCVcc ) , including receptor binding , internalization , and pH-dependent endosomal fusion , are mimicked by HCVpp . In addition , pseudoparticles can be engineered to express reporter proteins , affording a convenient system to quantify E1E2-mediated entry in the absence of other HCV-encoded functions . The HCVpp system is easily amenable to genetic manipulation of E1 and E2 , allowing the characterization of envelope protein genotype variation and identification of functionally important regions through mutagenesis . The molecular targets for current HCV direct-acting antiviral agents in drug development are focused on the non-structural proteins required for replication such as the NS3 protease , NS5A , and the NS5B RNA-dependent RNA polymerase . The viral entry pathway encompasses several additional potential points for intervention , and therapies targeting entry would provide a differentiated mechanism that could be a component of future drug combination regimes [49] . Here , we characterize a small molecule entry inhibitor identified through a high-throughput HCVpp screening effort . The inhibitor is most potent against genotype 1 HCV and functions at a post-HSPG binding step , prior to or concomitant with fusion . Using chimeric HCVcc expressing genotype 1a , 1b , or 2a envelope proteins , we demonstrate comparable potency and the ability to block cell-to-cell spread . HCVcc resistant to this molecule was isolated and amino acid changes were mapped to a residue in the transmembrane domain ( TMD ) of E2 , distinct from regions identified in receptor-binding functions . Combination of the entry inhibitor with IFN-α or other HCV-specific antivirals resulted in additive to synergistic activity .
293T cells ( ATCC , Manassas , VA ) , Huh-7 cells ( a gift from Ralf Bartenschlager ) , and Huh-H1 cells were maintained in Dulbecco's Modified Eagle Medium ( DMEM ) ( Invitrogen , Carlsbad , CA ) supplemented with 10% fetal bovine serum ( FBS ) ( HyClone , Logan , UT ) , 2 mM L-glutamine , 1mM sodium pyruvate , 10% nonessential amino acids , 10 mM HEPES , 100 units/ml penicillin , and 100 units/ml streptomycin . Huh-7 . 5 cells ( Apath , Brooklyn , NY ) were maintained in DMEM containing 10% FBS , 10% nonessential amino acids , 100 units/ml penicillin , and 100 units/ml streptomycin . Madin-Darby canine kidney ( MDCK ) cells ( ATCC ) were maintained in Minimum Essential Medium ( MEM ) ( Invitrogen , Carlsbad , CA ) containing 5% FBS and 0 . 1% sodium bicarbonate . Madin-Darby bovine kidney ( MDBK ) cells ( ATCC ) were maintained in DMEM supplemented with 10% Ultra-Low IgG FBS ( Invitrogen ) , 2 mM L-glutamine , 100 units/ml penicillin , and 100 units/ml streptomycin . MT2 cells were obtained from the NIH Research and Reference Reagent Program and maintained in RPMI 1640 supplemented with 10% FBS , 10 mM HEPES , 2 mM L-glutamine , 100 units/ml penicillin , and 100 units/ml streptomycin . CD81 cDNA was isolated from Huh-7 cells by reverse transcriptase PCR ( RT-PCR ) and cloned under the transcriptional control of the CMV IE promoter in pcDNA3 . 1 ( - ) ( Invitrogen ) to create p131-C1 . Huh7B cells were transfected with p131-C1 using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's protocol and subjected to selection with 1 mg/ml G418 . Limiting dilution and flow cytometry using anti-CD81 monoclonal antibody ( BD Biosciences , San Jose , CA ) were used to segregate the cells expressing the highest levels of CD81 . A cell clone , designated here as Huh-H1 , was expanded and maintained in media containing 0 . 5 mg/ml G418 . HCV RNA was isolated from infected human sera , obtained following informed consent , using the QiaAmp Viral RNA Extraction kit ( Qiagen , Valencia , CA ) according to the manufacturer's instructions . HCV RNA was reverse transcribed using Thermoscript ( Invitrogen ) and genotype-specific primers designed using publically available sequences . Subgenomic amplicons harboring complete core-p7 regions were amplified using genotype-specific forward primers within the HCV 5′ UTR and reverse primers within NS2 or NS3 . Sequences encoding the last 21 amino acids of core ( E1 signal sequence ) through the end of E2 were amplified using patient-specific primers and cloned into pcDNA3 . 1 ( + ) ( Invitrogen ) for HCVpp production . HCVpp pseudotyped with envelope proteins from genotype 1b isolate 432-4 ( GenBank accession number HM049503 ) were used for the compound library screening , as well as other experiments presented here . Prototypical laboratory HCVpp envelopes were derived from genotype 1a H77C ( GenBank accession number AF009606 ) , genotype 1b Con1 ( GenBank accession number AJ238799 ) , genotype 2a JHF1 ( GenBank accession number AB047639 ) , or genotype 2a J6 ( GenBank accession number AF177036 ) and cloned into expression vector pcDNA3 . 1 ( + ) . Murine leukemia virus ( MLV ) -based pseudoparticles containing HCV E1E2 or vesicular stomatitis virus ( VSV ) glycoprotein G envelope proteins were produced by a modification of published procedures [47] , [48] . Briefly , 3 . 5×107 293T cells were transfected with 17 . 5 µg of pVPack-GP ( Stratagene , La Jolla , CA ) expressing the murine leukemia virus capsid and polymerase proteins , 17 . 5 µg of pFB-luc2 ( a derivative of pFB-luc ( Stratagene ) in which the firefly luciferase gene was replaced with a human codon optimized firefly luciferase from pGL4 . 10 ( Promega , Madison WI ) ) encoding an MLV genome expressing luciferase reporter gene , and 4 . 4 µg of either pVSV-G ( Clontech , Mountain View , CA ) expressing the VSV envelope glycoprotein G or one of the HCV E1E2-expressing plasmids ( above ) . Transfections were performed for 6 hrs using Lipofectamine 2000 as described by the manufacturer ( Invitrogen ) , at which time the medium was removed and replaced with DMEM/10% FBS . Media containing HCV pseudoparticles ( HCVpp ) or VSV pseudoparticles ( VSVpp ) was collected 3 days following transfection , clarified by passage through a 0 . 45 µm filter ( Millipore , Bedford , MA ) , and stored at −70°C as a viral stock . For compound library screening , infections were performed in 384-well plates by mixing HCVpp or VSVpp with 1×104 Huh-H1 cells/well in the presence or absence of test inhibitors , followed by incubation at 37°C . Luciferase activity , reflecting the degree of entry of the pseudoparticles into host cells , was measured 2 days after infection using the Steady-Glo Reagent ( Promega ) . Test compounds were serially diluted 3-fold in dimethyl sulfoxide ( DMSO ) to give a final concentration range in the assay of 50 . 0 µM to 0 . 04 pM . Maximum activity ( 100% of control ) and background were derived from control wells containing DMSO alone or from uninfected wells , respectively . The individual signals in each of the compound test wells were then divided by the averaged control values ( wells lacking inhibitor ) , after background subtraction , and multiplied by 100% to determine percent activity . The corresponding % inhibition values were then calculated by subtracting this value from 100 . Assays were performed in triplicate and average EC50 values ( reflecting the concentration at which 50% inhibition of virus replication was achieved ) were calculated using XLfit for Excel ( ID Business Solutions , Burlington , MA ) . The specificity of the compounds for inhibiting HCV was determined by evaluating inhibition of VSVpp infection in parallel . HCVcc utilized consisted of chimeric viruses containing structural genes from genotype 1a , 1b , or 2a and nonstructural regions from genotype 2a JFH-1 . The full length JFH1 genome was chemically synthesized . The genotype 1a chimeric JFH1 virus containing the H77C structural region ( HCV-1a/2a ) was constructed as described [50] . The genotype 1b chimeric JFH1 virus , containing the core to the NS2 C3 junction from the genotype 1b isolate 432-4 ( HCV-1b/2a ) , was constructed as described [51] . The genotype 2a chimeric JHF1 virus containing the J6CF structural region ( HCV-2a/2a ) was constructed as described [51] . Chimeric reporter viruses contained an in-frame Renilla luciferase gene ( HCVcc-1a/2a-Rluc , HCVcc-1b/2a-Rluc , and HCVcc-2a/2a-Rluc ) inserted in-between the NS5A and NS5B coding sequences such that the NS3 protease cleavage sequences were reconstituted on either side of luciferase ( Burt Rose , unpublished ) . In vitro RNA was prepared from these cloned sequences using the MEGAscript kit ( Ambion , Austin , TX ) and transfected by electroporation into Huh-7 . 5 cells as described [52] . Media containing virus was collected , clarified by low speed centrifugation , and stored at −70°C . HCVcc titers were determined by infection of Huh-7 . 5 cells with serial dilutions of virus , followed by indirect immunofluorescence for HCV core protein as described below , and expressed as focus forming units ( ffu ) /ml . Virus preparations required several passages for adaptation required to generate high titer stocks . Infections utilizing HCVcc chimeras expressing the Renilla luciferase protein were quantified by infecting Huh-7 . 5 cells ( with or without inhibitors ) , incubating at 37°C for 3 days , and measuring luciferase activity using the EnduRen substrate ( Promega ) as described by the manufacturer . Infections utilizing HCVcc without a reporter were quantified by indirect immunofluorescence . HCVcc was added to Huh-7 . 5 cells ( with or without inhibitors ) in special-optics , collagen-coated 96-well plates ( BD Biosciences ) and incubated at 37°C for 2–4 days . Cells were then washed twice with PBS and fixed with 4% paraformaldehyde in PBS ( Sigma ) for 30 min at room temperature . Following 2 washes in PBS , cells were permeabilized with 0 . 25% Triton X-100 ( Pierce , Rockford , IL ) in PBS for 10 min and blocked with 2% bovine serum albumen ( Sigma ) in PBS for 30 min . Samples were incubated for 2 hr with 3 µg/ml anti-HCV Core monoclonal antibody ( ABR-Affinity Bioreagents , Golden , CO ) , washed 4 times with PBS , and incubated with a 1/500 dilution of Alexa Fluor 488-labeled donkey anti-mouse secondary antibody ( Invitrogen ) for 1 hr . Samples were washed three times with PBS and 0 . 5 µg/ml of Hoechst 33258 ( Sigma ) was added to the final wash to visualize nuclei . Infected cell foci were visualized using a Nikon Eclipse TE300 inverted epi-fluorescence microscope . EC50 determinations were performed as described above . EI-1 was serial diluted in DMSO as above and added to cells and virus during infection . Influenza virus cell protection assays were performed essentially as described [53] by infecting MDCK cells with 0 . 005 plaque forming units ( pfu ) /cell of influenza virus strain A/WS/33 ( ATCC ) . Infected cells were incubated at 37°C for 3 days and virus-induced cytopathicity was measured using the Alamar Blue reagent ( Invitrogen ) . BVDV cell protection assays were performed essentially as described [54] by infecting MDBK cells with 0 . 1 pfu/cell of BVDV strain NADL . Infected cells were incubated at 37°C for 3 days and virus-induced cytopathicity was measured using the Cell Titer-Glo reagent ( Promega ) . HIV assays were performed as described [55] by infecting MT2 cells with 0 . 05 pfu/cell of the NL4-3 strain of HIV ( NIH Research and Reference Reagent Program ) containing the Renilla luciferase gene ( NL-Rluc ) in the Nef locus . Infected cells were incubated at 37°C for 5 days and virus replication was measured by reverse transcriptase assay using a scintillation proximity assay as described [55] . Huh-H1 cells were seeded at 5×103 cells/well in 96-well plates . The following day the plates were chilled to 4°C and the media was removed and replaced with HCVpp inoculum in a volume of 50 µl . Plates were incubated at 4°C for 1 . 5 hr on a rocking platform . The inoculum was removed and unbound virus was removed by 2 washes with 4°C media . Fresh media was added and the plates were shifted to a 37°C incubator . 200 µg/ml porcine intestinal heparin ( Sigma ) , 10 nM bafilomycin A1 ( Sigma ) , 2 µg/ml anti-CD81 monoclonal antibody ( BD Biosciences ) , or 0 . 125 µM EI-1 were added at specific time points during infection . At 3 days post infection , firefly luciferase activity was quantified using the Steady-Glo Reagent ( Promega ) according to the manufacturer's instructions . Experiments were done in triplicate and the mean % inhibition was calculated relative to control infections lacking inhibitor . Huh-7 . 5 cells were plated in 24-well plates at 6 . 5×104 cells/well and infected with HCVcc-1b/2a at 2 ffu/cell in the presence of EI-1 ( 40 or 200 nm ) , 8 nM NS5A inhibitor BMS-790052 [56] , or DMSO control . Cells were incubated at 37°C for 5 hrs , after which the inoculum was removed and the monolayers washed 3× with phosphate buffered saline ( PBS ) . Fresh media was added containing inhibitors or DMSO as above and the cells incubated at 37°C . Wells in which inhibitors were present continually for the duration of the infection measured the effect on entry , whereas wells that received inhibitors at 5 hrs post infection measured the effect on post-entry events . Media was removed at 2 days post infection and clarified by centrifugation at 1 , 000×g for 5 min . To titer the infectious HCVcc , media was serially diluted and virus quantified by indirect immunofluorescence as described above . To measure total viral particles released into the media , HCV RNA was isolated from 0 . 05 ml of culture supernatants using the MagMax-96 Viral RNA Isolation Kit ( Ambion , Austin , TX ) according to the manufacturer's protocol . Purified HCV RNA was analyzed by quantitative RT-PCR using the AgPath-ID One-Step RT-PCR Kit ( Ambion ) according to the manufacturer's protocol using HCV specific forward primer ( 5′-CGG GAG AGC CAT AGT GG-3′ ) , reverse primer ( 5′-AGT ACC ACA AGG CCT TTC G-3′ ) and probe ( 5′-FAM-CTG CGG AAC CGG TGA GTA CAC-BHQ-3′ ) ( Biosearch Technologies , Novato , CA ) . Samples were run on an Applied Biosystems 7900HT instrument using the 40-cycle RT-PCR protocol and the data analyzed using the SDS 2 . 2 . 2 software ( Applied Biosystems , Foster City , CA ) . All results are the mean of triplicate assays . An assay incorporating a semisolid medium was used to access cell-to-cell spread as described [57] . HCVcc-1a/c2a was added to 1 . 6×104 Huh-7 . 5 cells at 0 . 001 ffu/cell incubated at 37°C . At 12 hr post infection , the inoculum was removed and replaced with DMEM/2% FBS/1% Seaplaque low melting temperature agarose ( Lonza , Rockland , ME ) containing EI-1 ( 0 . 5 µM ) or an equivalent volume of DMSO control . Cells were incubated at 37°C for 2 , 3 or 4 days at which time the agarose was removed and the infected cells were detected using indirect immunofluorescence for the HCV core protein as described above . The mean number of infected cells/ffu was determined from ≥100 foci for each data point . HCVcc-1a/2a or HCV-1b/2a was used to infect Huh-7 . 5 cells in media containing EI-1 . Either infected cells or cell-free virus from media was serially passaged and selective pressure was progressively increased in sequential passages by raising the concentration of the inhibitor present . HCVcc replication in the presence of EI-1 was monitored by determining the spread of virus infection , using immunofluorescence , at each passage . Generally , virus stocks were prepared when HCVcc was ≥50-fold resistant relative to wild-type parental virus . The HCV genome was amplified by RT-PCR , cloned and amino acid changes that arose during inhibitor selection were identified by analysis of the DNA sequence compared to the parent and control passages in the absence of inhibitor . EI-1 was tested individually or in combination with NS5A inhibitor BMS-790052 [56] , NS33 inhibitor BMS-605339 [58] , or recombinant IFN-α-2b ( Myoderm Medical Supply , Norristown , PA ) . Antiviral assays were performed with HCVcc-1a/2a-Rluc , and quantified as described above . Concentration-response curves were fit to the normalized responses from each inhibitor . The combination indices ( CI ) and Lowe's synergy were determined and analyzed as described [59] . In practice , additivity is indicated if the CI = 1 . 0 , synergy if the CI<1 . 0 , and antagonism if the CI>1 . 0 . To take into account the inherent variability involved with cultured cells , the calculated 95% confidence intervals for all of the combination indices were presented . The final results were derived from 8 independent experiments for each combination .
An HCV pseudoparticle ( HCVpp ) infection system , utilizing a firefly luciferase reporter , was developed for high-throughput screening ( HTS ) of a small molecule library of >1 million compounds for inhibitors of HCV entry . In order to facilitate the screen , assay performance was improved by modifying the properties of the parental host cell line and the pseudovirus . First , we created a Huh-7B-derived cell population ( Huh-H1 ) that overexpresses the CD81 receptor in order to improve HCVpp entry [60] , resulting in an enhancement to HCVpp infection of approximately 3 fold relative to the parental Huh-7B cells ( data not shown ) . Second , HCVpp used for the HTS contained envelope proteins E1 and E2 that were derived from genotype 1b clinical isolate 432-4 ( HCVpp-1b ) . Infection with these pseudoparticles resulted in an approximate 3-fold increase in luciferase activity relative to HCVpp containing the prototypical 1b Con1 envelope proteins ( data not shown ) . Third , the pseudoviruses were engineered to express a human codon optimized luciferase reporter that improved the sensitivity of the assay by increasing activity approximately 100-fold compared to wild-type firefly luciferase ( data not shown ) . Test compounds were incubated together with HCVpp-1b and Huh-H1 cells for 2 days , at which time viral entry was quantified by luciferase activity . Compounds found to inhibit HCVpp-1b infection were then counterscreened against pseudoparticles containing the genotype 1a H77C envelope proteins ( HCVpp-1a ) and pseudoparticles containing the VSV glycoprotein G envelope protein ( VSVpp ) in order to identify HCVpp-selective inhibitors . From this process , counterscreens against other viruses , and structure-activity relationship ( SAR ) testing using other compounds from the collection , we identified a chemical series consisting of several structurally related compounds defined by a common triazine core . An example of one such compound , EI-1 ( Fig . 1A ) , inhibited HCVpp-1b and VSVpp with EC50 values of 0 . 016±0 . 001 and 33±2 . 1 µM respectively , giving a VSVpp/HCVpp selectivity index of >2 , 000 ( Fig . 1B ) . Other studies showed that pseudovirus with MLV envelope proteins was also not inhibited ( data not shown ) . Because HCVpp and VSVpp differed only in their viral envelope proteins , and both pseudoparticles enter cells via the clathrin-mediated endocytosis pathway , the results suggested that the inhibitors targeted the HCV E1E2 proteins and not common cellular factors utilized for entry . Synthetic chemistry efforts were used to further explore the SAR of the triazine series using EI-1 [N2- ( 4-nitrophenyl ) -N4-pentyl-6- ( 2 , 2 , 2-trifluoroethoxy ) -1 , 3 , 5-triazine-2 , 4-diamine] as a starting point . Our initial synthetic strategy focused on identifying an equipotent replacement for the pharmaceutically undesirable nitro group . A library of compounds was synthesized to explore the effect of substitutions at the 4′ ( para ) and 3′ ( meta ) positions of the aniline ring ( Fig 1A ) , and these compounds were assessed for activity against HCVpp-1a and HCVpp-1b ( Table 1 ) . Removing the nitro group from EI-2 afforded a 3- to 6-fold decrease in potency in the HCVpp 1a and 1b assays , respectively . Surprisingly , the electron-donating p- and m-methoxy analogs ( EI-3 and EI-9 ) were equipotent with the corresponding nitro group-containing analogs , while the p- and m-methyl analogs ( EI-4 and EI-10 ) afforded HCVpp EC50 values within 4-fold of the corresponding nitro analogs . In contrast , introduction of the electron-withdrawing p-CF3 group ( EI-5 ) resulted in a 15- to 45-fold decrease in 1a/1b potency in comparison with EI-1 . This activity discrepancy did not extend to the m-CF3 analog ( EI-11 ) , which afforded equipotent genotype 1a/1b activity in comparison with the m-nitro analog ( EI-8 ) . As illustrated with EI-6 and EI-7 , the introduction of para substituents which more closely mimicked the shape and polarity of the nitro group afforded sub-100 nM EC50 values in both the 1a and 1b HCVpp assays , but the HCVpp 1a activity was significantly diminished for the meta-substituted analogs EI-12 and EI-13 . All analogs contained in Table 1 provided greater than 10-fold VSV selectivity , indicating that the activity of the compounds did not arise from cytotoxicity . In order to investigate the activity of EI-1 against various HCV genotypes , we established HCVpp containing E1 and E2 proteins from different genotypic backgrounds . Serum samples from patients infected with HCV genotypes 1–5 were used as a source for cloning the E1E2 coding sequences by RT-PCR . Individual clones were then tested for the ability to support pseudovirus infectivity . From this process , a collection of HCVpp containing functional envelope proteins from each of 40 separate patient samples was obtained . The panel consists of 16 genotype 1a isolates , 15 genotype 1b isolates , 2 isolates each of genotypes 2a , 2b , 3a , and 4a , and 1 genotype 5a isolate . The potency of EI-1 was then assessed against this HCVpp panel ( Fig . 2 ) . As compared to VSVpp , EI-1 potently and selectively inhibited all 31 genotype 1 isolates with median 1a and 1b EC50 values of 0 . 134 and 0 . 027 µM , respectively ( 1a range 0 . 007–1 . 2 µM , 1b range 0 . 005–0 . 2 µM ) . By contrast , EI-1 inhibition of HCVpp genotypes 2–5 was minimal , with EC50s of 7 . 1 to >36 µM ( equivalent to VSVpp activity , thus likely through nonselective cell cytotoxicity ) . Greater potency towards genotype 1 HCV was a characteristic of other related analogs tested ( data not shown ) , providing further support to the hypothesis that the target of the compounds was likely to be a viral factor and not a general HCV entry cellular factor . While HCVpp are thought to enter cells in a manner analogous to authentic HCV , it was important to validate the antiviral activity of EI-1 using the fully replicating cell culture-adapted HCV ( HCVcc ) . HCVcc luciferase reporter virus chimeras HCVcc-1a/2a-Rluc , HCVcc-1b/2a-Rluc , or HCVcc-2a/2a-Rluc expressing either genotype 1a ( H77C ) , 1b ( 432-4 ) , or 2a ( J6 ) structural proteins , respectively , in the JFH1 background were utilized for these experiments . Huh-7 . 5 cells were infected in the presence of EI-1 and productive infection was determined by measuring luciferase activity at 3 days post infection . Infection by the genotype 1a/2a and 1b/2a chimeras was prevented , with EC50 values of 0 . 024 and 0 . 012 µM , respectively ( Table 2 ) . EI-1 was not active against genotype 2a/2a chimeric virus , consistent with the results obtained with the genotype 2a HCVpp ( Fig . 2 ) . Although EI-1 displayed similar potency against HCVcc and HCVpp expressing the genotype 1b 432–4 envelope proteins ( Table 1 and Table 2 ) , EI-1 was 8 . 8-fold more potent against HCVcc expressing the genotype 1a H77C envelope proteins as compared to the corresponding HCVpp . This may be related to the observation that the 1a/2a chimeric virus infection spreads more efficiently relative to the 1b/2a chimeric virus in culture ( unpublished observations ) . The resulting additional rounds of replication can increase the apparent potency of the entry inhibitors in the HCVcc system vs . the single-cycle HCVpp assay . EI-1 selectivity for HCVcc was assessed by determining the activity against a panel of viruses that enter cells by fusion with the plasma membrane ( HIV ) or , like HCV , undergo pH-dependent fusion with the endosomal membrane ( BVDV and Influenza ) . In addition , compound-induced cytotoxicity in each cell line used for the infection assays was measured . No significant inhibition by EI-1 was observed against these viruses at concentrations up to the cytotoxic levels ( >50 µM ) , which was markedly removed from the concentrations that were active against HCV ( Table 2 ) . These results support the antiviral specificity of the EI-1 HCV inhibitor . Although EI-1 blocked HCVcc infection , no inhibition of genotype 1a or 1b HCV replicons was observed ( data not shown ) . To confirm that EI-1 acted at the entry stage of infection , we characterized the kinetics of compound activity using time-of-addition assays . Synchronous infection was initiated by adding HCVpp-1b to Huh-H1 cells at 4°C for 1 . 5 hr to allow attachment to the cell surface , presumably through HSPGs [11] , [12] , [13] . Unbound virus was removed and the temperature was shifted to 37°C to allow entry to proceed . EI-1 ( 0 . 125 µM ) was then added at various time intervals up to 4 hrs . To define the endpoint of the entry process , bafilomycin A1 , an inhibitor of endosomal acidification that prevents the final fusion step between the virus envelope and the endosomal membrane , was tested in a parallel assay . As previously reported [39] , sensitivity to bafilomycin was lost at times ≥3 hrs after the 37°C temperature shift , indicating that HCVpp entry and fusion were completed within this time frame ( Fig . 3A ) . The kinetics of EI-1 activity demonstrated that inhibition was likewise exerted within 3 hours of infection , confirming a point of action during HCVpp entry . Similar results were obtained with the HCVcc virus ( data not shown ) . We next examined whether EI-1 blocks the initial attachment step to HSPGs , or a downstream event in the HCV entry process . EI-1 was added together with HCVpp to cells during the 4°C attachment step only , and then removed prior to shifting to 37°C . Alternatively , EI-1 was added only following the temperature shift to measure the effect on the post-attachment events . Control inhibitors included the heparan sulfate homolog heparin , a monoclonal antibody against the CD81 receptor , and bafilomycin A1 . As previously reported , heparin was only effective at preventing entry when added during the 4°C attachment step , while the CD81 monoclonal antibody and bafilomycin were only effective during the post-attachment stage ( Fig 3B ) . EI-1 had little effect on the attachment of HCVpp to HSPGs , but exhibited >90% inhibition when added after the 37°C infection phase . Taken together , the data demonstrate that EI-1 blocks an event in HCV entry that lies temporally downstream of attachment to HSPGs , either prior to or during fusion . Following infection of Huh-7 . 5 cells with cell-free HCVcc , transmission of the virus to adjacent cells results in focal areas of spreading infection ( foci ) . Cell-to-cell spread of HCV differs from infection with cell-free virus in that it is refractory to neutralization by HCV E2 monoclonal antibodies and occurs in a CD81-independent manner [57] , [61] , thus representing an alternative mode of transmission that may be important in vivo . We therefore conducted experiments to determine if EI-1 could block cell-to-cell spread in culture . Huh-7 . 5 cells were infected with the HCVcc-1a/2a chimera at a ratio of 0 . 001 infectious units per cell . At 12 hr post infection , the inoculum was removed and replaced with a semisolid medium/agarose overlay that has been shown to prevent the cell-free diffusion of virus but not cell-to-cell transmission [57] . Discrete foci formed in the presence of the agarose overlay lacking EI-1 ( Fig 4A ) , although they were somewhat smaller in size compared to those observed using a liquid medium ( data not shown ) , consistent with previous observations [57] . Foci size increased over time , from an average of 2 infected cells at day 2 to an average of 40 infected cells at day 4 ( Fig 4B ) . However , in the presence of EI-1 , cell-to-cell transmission was abrogated ( Fig 4A ) , with foci containing only an average of 6 infected cells/focus at 4 days post infection ( Fig 4B ) . This foci size represents division of the initially-infected cell only ( data not shown ) . In addition , the total number of foci did not increase between days 2 and 4 , indicating that the agarose overlay was effective in preventing the formation of satellite foci that can result from the cell-free dissemination of virus from the primary sites of infection ( Fig 4C ) . Overall , these results demonstrate that EI-1 is effective at preventing HCVcc infection by the cell-to-cell transmission pathway . If EI-1 mediates inhibition of cell-free virus and cell-to-cell spread via interaction with E1 and/or E2 , compound binding to the envelope protein ( s ) during viral replication or assembly could result in a decreased virus production or infectivity , consequently decreasing the luciferase signal and foci size . To address the potential effects of the entry inhibitor on the post-entry events of the HCV life cycle , EI-1 was added to the culture either during HCVcc infection , or 5 hours later . At two days post-infection , total virions ( HCV RNA copies ) and infectious virus ( HCV ffu ) released into the cell media were quantified . As expected , if EI-1 was present during entry , there was a dose-dependent reduction in subsequent progeny virus release ( Fig 5 ) . However , addition of EI-1 post entry did not affect the levels of virion particles or infectious virus produced . In contrast , an NS5A inhibitor BMS-790052 , which targets HCV replication [56] , prevented virus release when added 5 hours post infection . These results demonstrate that the EI-1 antiviral activity is confined to the pre-replication ( entry ) stage of infection . To investigate whether E1 and/or E2 is the molecular target of EI-1 , experiments were performed to determine if HCVcc resistance to the compound could be achieved by selective pressure in cell culture . HCVcc-1a/2a or HCVcc-1b/2a chimeras were propagated for several passages in the presence of increasing concentrations of EI-1 . Virus was monitored for decreased susceptibility ( increased EC50 ) to the compound at intervals during the selection procedure and by increased spreading of the virus through immunofluorescence detection of HCV core protein . When the HCVcc population had become ≥50-fold resistant relative to the untreated controls , and virus spreading to uninfected cells was abundant , the virus population was isolated and the genome amplified by RT-PCR . Changes in the DNA and amino acid sequence of the entire HCV polyprotein were then determined . Two independent selection experiments were conducted with independent stocks of HCVcc-1a/2a . Both EI-1 resistant virus isolates contained an amino acid substitution at residue 719 of the E2 protein ( based on the H77C polyprotein numbering ) in which valine changed to either phenylalanine or glycine ( Table 3 ) . E2:V719G also emerged in a third selection experiment using the HCVcc-1b/2a chimera , together with an E1:V227V/A mixture . In two of the virus populations , additional amino acid changes were also identified in non-structural proteins NS4B and NS5A . To determine the role each of the amino acid changes in the EI-1 resistance phenotype , substitutions were introduced separately into wild-type HCVcc or HCVpp by site-directed mutagenesis . Substitutions that arose in NS4B and NS5A seemed unlikely to contribute to resistance of an HCV entry inhibitor . To test this hypothesis , the single E2:V719G substitution was created in the HCVcc-1a/2a chimera and the resistance level of this clone was compared to the EI-1-selected virus containing the E2:V719G+NS4B:T1936A+NS5A:I2345T ( Table 3 ) . Similar resistance levels were observed ( 86 vs . 91-fold/WT ) , suggesting the E2 mutation alone confirmed the HCVcc resistance phenotype ( Table 4 ) . Next , to confirm that the HCVcc resistance to EI-1 is mediated at the level of viral entry , each of the E1 and E2 substitutions that emerged during selection were tested in the HCVpp background . The E2:V719F and G substitutions recapitulated resistance to EI-1 in both genotype 1a and 1b HCVpp ( Table 4 ) . However , in contrast to the HCVcc results , the E2:V719G substitution in the genotype 1a background rendered the HCVpp completely resistant to EI-1 ( >5 , 000 fold/WT ) . The only substitution found in the E1 envelope protein during selection with EI-1 , V227A , conferred a small ( ∼2-fold ) increase in resistance when present alone , and did not contribute significant additional resistance in combination with E2:V719G . Residue 719 is located within the proposed carboxy-terminal TMD of E2 near the interface with the ectodomain ( Fig 6 ) . Examination of E2 sequences containing this region in public database repositories revealed that 719V is the predominate amino acid , present in 85% of sequences . The resistance changes identified here , V719F and V719G , were not found . Other naturally occurring polymorphisms at E2:719 are isoleucine , leucine , and alanine . E2:719I is found in 14% of sequences ( typically genotypes 1b , 5 , and 6 ) . This variant is represented in genotype 1a , 1b , and 5a isolates in our HCVpp genotype panel ( Fig . 6 ) , yet the genotype 1 HCVpp were fully susceptible to EI-1 ( 1b EC50 range 0 . 016–0 . 052 µM , 1a EC50 0 . 041 µM ) . V:719L or A was found in only 1% of publicly available sequences . These variants were constructed by site-directed mutagenesis and found to impart moderate ( V719L , 17-fold WT ) to high ( V719A , 317-fold WT ) levels of resistance to EI-1 ( Table 4 ) . Overall , the results implicate changes to E2 residue 719 in EI-1 resistance in genotype 1 HCV . However , HCVpp panel isolates from genotypes 2–5 , which are intrinsically resistant to EI-1 , contain E2:719V or I , suggesting susceptibility is likely modulated by other residues or regions as well ( Fig . 6 ) . Due to the high rate of preexisting or emerging viral resistance , effective treatment of HCV-infected patients will likely require a combination of inhibitors targeting distinct viral or host functions . Therefore , we performed experiments using the HCVcc-1a/2a-Rluc chimera to characterize the antiviral effects of the EI-1 entry inhibitor in combination with HCV replication inhibitors targeting NS5A ( BMS-790052 ) [56] or NS3 ( BMS-605339 ) [58] , or IFN-α . Each inhibitor was tested alone or in combination with entry inhibitor EI-1 . The data from 8 replicate experiments were analyzed for departure of the results from additivity at the EC50 level for each combination using the Loewe model as described by Chou [59] . By this method , a combination index ( CI ) equal to 1 indicates additivity , and a CI<1 or >1 indicates synergy or antagonism , respectively . Combinations of EI-1 with the NS5A inhibitor or IFN-α resulted in CIs of 0 . 95–0 . 96 , reflective of additivity ( Table 5 ) . The NS3 protease inhibitor combined with EI-1 gave a CI of 0 . 82 , indicative of a moderate degree of synergy . Similar results were also obtained for all combinations at the EC90 level ( data not shown ) . The absence of antagonism likely results from the mechanistically distinct targets of the entry and replication inhibitors , and indicates that entry inhibitors could provide a valuable component of combination therapy .
HCV entry represents an attractive target for drug discovery from a mechanistic view , with opportunities to prevent multiple virus-receptor interactions and to interfere with virus-cell membrane fusion [49] . Each of these steps , although not completely defined , is likely mediated by the HCV E1 and/or E2 envelope glycoproteins . In vitro , proof-of-concept for inhibiting the HCV entry process has been demonstrated using cyanovirin-N that targets the N-linked glycans of the viral envelope proteins and prevents E2-CD81 interaction [62] , neutralizing antibodies directed against the HCV E1 and E2 proteins [63] , [64] , [65] , [66] , [67] , [68] , antibodies against cellular receptors CD81 [17] , [18] , [19] , [20] , [21] , [22] and SR-BI [17] , [18] , [19] , [20] , [21] , [22] , and agents that block endosomal acidification [39] , [48] . In vivo studies using human liver-u-PA-SCID mice have also demonstrated prophylactic efficacy of anti-CD81 antibodies [69] . In the present study , we used the HCVpp system in order to isolate the entry pathway from other HCV replication functions , and undertook a screening campaign that led to discovery of a class of small molecule HCV-specific inhibitors , exemplified by EI-1 . Inhibition of entry was confirmed by using time-of-addition experiments to demonstrate that EI-1 activity is confined to the first 3 hours of infection , with inhibition occurring post-attachment and closely linked to the inhibition kinetics of the endosomal acidification inhibitor bafilomycin . EI-1 does not inhibit entry of VSVpp , which also undergoes receptor-mediated endocytosis and pH-dependent endosomal fusion , thus making cellular factors required for internalization unlikely targets of this compound . Furthermore , although EI-1 inhibited all 31 genotype 1a and 1b isolates in our HCVpp panel , activity towards isolates with envelope genotypes 2–5 was greatly diminished . This result also argues against a cellular protein as the target for EI-1 as such an inhibitor should display similar activity across genotypes . Lastly , genotype 1 HCVcc resistance to EI-1 is conferred by a V719F/G change in the C-terminal TMD region of HCV E2 , supporting the concept that EI-1 blocks HCV entry by inhibiting the function of the HCV envelope glycoproteins . It is tempting to speculate that EI-1 binds to E2 in part through an interaction with the valine or isoleucine residue 719 . Alternatively , E2:V719 may represent an allosteric site , whereby changes induce a conformational alteration of E2 and/or E1 that prevent EI-1 binding . However , the E2 protein of genotypes 2–5 in our HCVpp panel also contains valine or leucine , yet these isolates are not susceptible to EI-1suggesting the determinant ( s ) for the intrinsic resistance of non-genotype 1 HCV may lay elsewhere . Indeed , further HCVcc resistance selection experiments suggest that changes to residues within E1 can also modulate susceptibility to other members of the EI-1 chemotype ( data not shown ) . Ultimately , conclusive evidence for the target of EI-1 awaits biophysical experiments designed to demonstrate a direct compound-protein interaction . It was critical to determine whether the EI-1 entry inhibitor prevented infection by HCVcc as well as HCVpp . While experimental findings obtained with the HCVpp model have generally extended to those with the HCVcc system , this is not always the case . For example , while a small molecule targeting SR-BI [70] potently inhibits HCVcc infection , it does so at a markedly reduced potency in the HCVpp system ( unpublished observations ) . More importantly , however , it was unclear if small molecule inhibitors discovered through the HCVpp system could prove to inhibit HCVcc infection , especially since much of the HCVcc infection occurs through a cell-to-cell transmission route that is shielded from neutralizing antibodies [57] and bypasses the requirement for the CD81 receptor [61] . Since it is assumed that cell-to-cell infection is an important feature of viral pathogenesis , inhibitors that operated through both prevention of cell-free virus infection and cell-to-cell spread of virus would logically be needed for therapy . Our results demonstrate that EI-1 is potent at blocking genotype 1 HCVpp and HCVcc entry , as well as direct cell-to-cell spread of HCVcc . However , because circulating HCV in patients is highly associated with lipoprotein particles [31] , [52] , [71] , [72] , [73] , [74] , it will be important to determine the efficacy of EI-1 and similar HCV entry inhibitors in cell culture systems using serum-derived HCV or in human liver chimeric mouse model systems [69] . The structure of either of the HCV envelope proteins has yet to be solved . However , high-resolution structural models for the related flavivirus class II envelope glycoproteins of dengue virus , tick-borne encephalitis virus , and West Nile virus have been reported in both the pre- and post-fusion states [36] , [75] , [76] , [77] . It is unclear how the HCV E1 and E2 proteins perform the functions of the homologous proteins in other flaviviruses . However , structural features characteristic of class II viral fusion proteins , such as a membrane proximal heptad repeat and a putative hydrophobic fusion peptide have been identified within both E1 and E2 [42] , [43] , [46] , [78] . In addition , other laboratories have used mutational analysis to ascribe E1-E2 heterodimerization , entry , and membrane fusion functions to residues in the E2 stem and TMD [79] , [80] . These results have lead to the hypothesis that flavivirus glycoproteins form intermolecular hairpin motifs projecting hydrophobic fusion peptides that facilitate the final fusion of viral envelopes with cellular membranes [81] . Further defining the target of EI-1 and elucidating the mechanism of inhibition may contribute to understanding the functional roles of the HCV envelope proteins . A dengue virus entry inhibitor , the detergent n-octyl-β-D-glucoside ( β-OG ) was found to bind to a hydrophobic pocket formed in a postulated hinge region between domains I and II in the viral envelope E protein [36] . Several other inhibitors of dengue virus entry were found based on an exercise of modeling candidate compounds into this pocket [82] , [83] . Modeling of domains I , II and III of the dengue E protein with HCV E1 and E2 proteins suggested that the β-OG site between domains II and III localized to the HCV E2 protein and not to E1 . While β-OG did not inhibit HCV entry in our hands ( data not shown ) , it is unclear what portion of HCV E1 or E2 may be analogous to the hinge region . In contrast to the β-OG binding site , which is within the soluble fragment of dengue virus E protein , HCV resistance to the EI-1 compound described here maps to the second amino acid of the putative TMD region of genotype 1 E2 . Perhaps multiple binding sites within the HCV entry proteins exist , accessible during the numerous conformational states that may operate during receptor binding and fusion . Consistent with this concept is the finding that during our own discovery efforts , several HCV entry inhibitors with diverse structural characteristics and resistance mapping were identified ( data not shown ) . Downstream implications of these findings are the possibility that multiple , diverse inhibitors of HCV entry could contribute to combination therapy for HCV . Similarities between the HCV entry inhibitors described here and diverse compounds inhibiting the entry of arenaviruses into cells are intriguing . Both pseudotype [84] and infectious virus screening [85] identified broadly active arenavirus entry inhibitors . Isolation and mapping of resistant viruses , as well as chimeras between sensitive and resistant strains , mapped the target of activity to the GP2 subunit of the G envelope protein complex , specifically the interface between the C-terminal stem and TMD domains [84] , [85] . These results are strikingly similar to those described in the current work in that resistance to EI-1 occurs in the same region of the genotype 1 HCV E2 envelope glycoprotein Mechanistic studies showed that these arenavirus inhibitors prevented low-pH-induced fusion by blocking reorganization between the GP2 stem with N-terminal domains of the G protein complex [86] . By analogy , perhaps the HCV entry inhibitors described here prevent pH-induced reorganization of the HCV E1E2 complex that mediates fusion [81] . The activity surrounding the search for antivirals targeting HCV is considerable . Each antiviral therapy is accompanied by a unique set of challenges for its development . Although HCV entry inhibitors could be a valuable component of therapy , their development will provide differences from those of replication inhibitor compounds , currently in clinical development . Preclinical development of entry inhibitors require infection assay formats , with pseudotyped or full-length HCV , such as those described here . While capable experimental systems have been developed , these are not as robust as many virus systems such as HIV , influenza or herpesviruses . HCV replication inhibitor assays , on the other hand , require assays with the more facile stable , transformed , replicon cell line . The inherent genetic variation in viral envelope proteins also presents a unique target for entry inhibitors . We have addressed these issues with various assay systems and an HCVpp genotype panel assembled from patient isolates , which demonstrated the genotype 1 specificity of EI-1 . Furthermore , as noted above , it has been reported that the viral envelope lipoprotein content differs between cell culture HCV and virus isolated from patients . Additionally , cellular receptors and entry processes may vary from the transformed cells used here and primary hepatocytes . For these reasons , it will be important to evaluate the efficacy of small molecule HCV entry inhibitors in primary cells , using patient-derived virus , and potentially in in vivo model systems . Finally , current HCV inhibitor clinical studies have been limited in duration to prevent the development of resistance . The efficacy of these replication inhibitors , however , can quickly be assessed through circulating levels of HCV RNA produced by chronically-infected cells . Since entry inhibitors will prevent new infections of uninfected cells , they will have no immediate impact on the levels of circulating virus in the blood . Thus , more protracted clinical studies may be required . Several small molecule inhibitors have been advanced to the clinic , and some have progressed after an initial high attrition rate [87] . Unfortunately , both the high replication and error rates of the viral polymerase leads to exceeding diversity of viral sequences , thus resulting in preexisting and rapidly emerging resistance [9] , [10] , [88] . Despite potent efficacy , it is now well understood that combinations of inhibitors , including both small molecules targeting the virus and interferon regimens acting through host targets , will be required for optimal treatment [9] , [88] . By analogy to HIV , safe , potent inhibitors of multiple viral targets will be needed to prevent resistance from emerging or for optimal management of patients with resistance . We have shown the EI-1 entry inhibitor functions additively or synergistically with other HCV replication inhibitors and IFN-α in the HCVcc cell culture assay system . Entry inhibitors , such as those described here , by virtue of their distinct , relatively new targets , may provide a valuable component in the eventual optimal therapy for HCV infection . | Approximately 170 million people worldwide are chronically infected with hepatitis C virus ( HCV ) , which is a leading cause of chronic liver disease . Current treatments are not optimal; however , several molecules that inhibit HCV replication are in development . However , resistance to individual antivirals is likely to develop , requiring therapy consisting of a combination of drugs targeting different stages of the viral life cycle . The entry of HCV into hepatocytes is a multistep process , involving at least four cellular receptors , leading to virion endocytosis and fusion of the viral and cellular membranes . Unlike the HCV replication process , these steps have not been thoroughly exploited as targets for antiviral intervention . Therefore , we screened a small molecule library for inhibitors of HCV entry and identified a compound , EI-1 , that potently blocked genotype 1a and 1b HCV infection . Importantly , EI-1 also prevented direct cell-to-cell spread of HCV , a potentially significant route of transmission in infected livers . In addition , our studies suggest that EI-1 susceptibility is mediated by the viral E2 envelope glycoprotein , as resistance in E2 can overcome inhibition . The antiviral activity of EI-1 is potentiated by combinations with other HCV inhibitors , demonstrating the value of entry inhibitors in potential combination antiviral regimens . | [
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] | 2010 | A Novel Small Molecule Inhibitor of Hepatitis C Virus Entry |
Akt represents a nodal point between the Insulin receptor and TOR signaling , and its activation by phosphorylation controls cell proliferation , cell size , and metabolism . The activity of Akt must be carefully balanced , as increased Akt signaling is frequently associated with cancer and as insufficient Akt signaling is linked to metabolic disease and diabetes mellitus . Using a genome-wide RNAi screen in Drosophila cells in culture , and in vivo analyses in the third instar wing imaginal disc , we studied the regulatory circuitries that define dAkt activation . We provide evidence that negative feedback regulation of dAkt occurs during normal Drosophila development in vivo . Whereas in cell culture dAkt is regulated by S6 Kinase ( S6K ) –dependent negative feedback , this feedback inhibition only plays a minor role in vivo . In contrast , dAkt activation under wild-type conditions is defined by feedback inhibition that depends on TOR Complex 1 ( TORC1 ) , but is S6K–independent . This feedback inhibition is switched from TORC1 to S6K only in the context of enhanced TORC1 activity , as triggered by mutations in tsc2 . These results illustrate how the Akt–TOR pathway dynamically adapts the routing of negative feedback in response to the activity load of its signaling circuit in vivo .
The development of multi-cellular organisms depends on the precise choreography of a diverse array of signal transduction pathways . Besides the requirement of some signaling events to occur in a spatial or temporal on-off manner , other pathways need to stay homeostatically active within physiological boundaries . This requires balanced regulation by activating as well as repressing signals . Mechanistically , three basic concepts of downregulating signaling pathway have emerged: ( 1 ) control via specific inhibitory ligands or receptors [1] , [2] , ( 2 ) negative cross-regulation by distinct signaling pathways [3] , and ( 3 ) auto-regulation by negative feedback mechanisms [4] , [5] . In most cases , the molecular component that executes the feedback-mediated inhibition is transcriptionally targeted by the very pathway that it regulates . This mechanism ensures an interdependence of signaling activity and feedback regulation and is often viewed as an inherent means to downregulate signaling pathways after stimulation . Loss of negative feedback regulation has been correlated with the initiation , growth and progression of tumors . For example , loss of negative feedback in Hedgehog ( Hh ) signaling by impeding patched function results in ectopic Hh signaling , basal cell carcinoma and medulloblastomas [6] . The expression of axin2 or dickkopf-1 , which encode feedback inhibitors of Wnt signaling , is silenced in colon and breast carcinomas and early lung adenocarcinoma [7] , [8] . Negative feedback regulators of Ras signaling , such as Sprouty proteins and MAPK phosphatases , are downregulated in liver , prostate and breast cancers [9] , [10] , [11] , [12] . Similarly , inhibition of negative feedback regulation has been reported for JAK/STAT , TGF-beta and NF-kappaB signaling pathways [13] , [14] , [15] . These observations indicate that some cancers arise by “breaching” auto-regulatory control mechanisms of signaling pathways via mutational inactivation or epigenetic silencing of negative feedback regulators . The Akt-TOR pathway has emerged as a central signaling nexus that integrates responses to growth factors , nutrients , metabolites and stress . Most prominently , activation of Akt is initiated by the insulin receptor ( InR ) , relayed via an intracellular signaling cascade comprising insulin receptor substrate ( IRS ) , class IA PI3 Kinase ( PI3K ) , PDK1 and TOR complex 2 ( TORC2 ) , consisting of TOR , Rictor , Sin1 , Lst8 and PRR5L [16] , [17] , [18] , [19] , [20] . Among other substrates , Akt inhibits the activities of the transcription factor FoxO [21] and the Rheb-specific GTPase activating protein ( GAP ) Tsc2 . In turn , Rheb regulates the TOR complex 1 ( TORC1 ) , containing TOR , Raptor and Lst8 [17] , [22] , [23] . TORC1 targets several well characterized substrates , most notably S6 Kinase ( S6K ) [24] , [25] . Hence , the two distinct TOR complexes TORC1 and TORC2 both participate in Akt-TOR signaling , but act at different levels in the Akt-TOR signaling pathway and integrate distinct stimuli . TORC2 responds to growth factors and might determine the substrate specificity of Akt [26] , [27] , [28] , [29] , while TORC1 mediates signaling by amino acids and cellular energy stress [30] , [31] , [32] , [33] . Ectopic activation of the core Akt-TOR signaling pathway by a variety of mechanisms is a frequent event in cancer biology 18 , 34 . Moreover , chronic diseases such as obesity and type II diabetes show pathological alteration of Akt-TOR activity [35] . Negative feedback mechanisms regulate the signaling input into the Akt-TOR pathway . Indeed , FoxO transcription factors inhibit the activity of the phosphatases PP2A and calcineurin by driving the expression of Atrogin-1 , causing elevated levels of Akt phosphorylation and activity [36] , [37] . Furthermore , Akt-dependent inhibition of the FoxO transcription factor results in reduced transcription of the inR gene . Conversely , low Akt-TOR signaling selectively increases InR mRNA translation relative to the total mRNA pool . In conjunction , both mechanisms reduce the relative levels of InR expression when Akt-TOR activity is high , thereby desensitizing against a stimulating ligand [38] , [39] , [40] . In addition , an S6K-dependent negative feedback mechanism leads to IRS1 destabilization , thus decreasing Akt activity [41] , [42] , [43] , [44] . While these negative feedback mechanisms have been defined in cell culture , it is currently unknown whether and how feedback regulation within the Akt-TOR signaling pathway is exerted during development in vivo . In Drosophila , the dAkt-TOR signaling pathway is conserved and regulates cell proliferation , and developmental timing and sizing of cells , organs and the whole fly [45] , [46] , [47] . As with the mammalian counterparts , Drosophila Akt receives regulatory inputs from TORC2 as well as PDK1 . The phosphorylation site in the C-terminal hydrophobic motif of Drosophila Akt is conserved , and , while dispensable for normal Drosophila development , is required for relaying high PI3K signaling levels [29] , [48] , [49] , [50] . Similarly , prostate-specific ablation of C-terminal Akt phosphorylation in mice conditionally mutant for Rictor delays lethality of Pten+/- induced prostate cancer [51] . In general , the C-terminal phosphorylation of Akt has emerged as a valuable and reliable tool to detect Akt activity in vivo and in vitro [52] , [53] . In contrast to the three Akt genes in mammalian genomes , Drosophila contains only a single dAkt gene . In addition , the InR and IRS families are represented solely as single genes , and the insulin/InR-related IGF-1/IGFR system is absent in flies . This simplicity underscores the suitability of the fly as a model organism for studying complex processes like the in vivo analysis of feedback mechanisms . To date , the analysis of feedback-mediated Akt-TOR pathway adaptation has been pursued under genetic or metabolic conditions that trigger high , possibly supra-physiological activity of TORC1 and S6K , and mostly in cell culture systems [18] , [54] . In this study , we present evidence that regulation by negative feedback is an integral part of the dAkt-TOR pathway in vivo . Importantly , we demonstrate that the pathway utilizes two distinct modes of negative feedback to downregulate its activity in vivo , independently of FoxO . Conditions of wild-type TORC1 activity favor a dampening feedback signal emanating from TORC1 itself , independent of S6K . In contrast , conditions that induce high TORC1 activity trigger an S6K-dependent feedback mechanism to dampen dAkt-TOR pathway signaling . Our observations suggest that S6K acts as a load-sensitive regulator of Akt-TOR signaling . We propose the presence of a novel dual “overload protection” circuit that emphasizes the importance of tight control over Akt-TOR pathway signal levels .
We established a cell-based assay for regulators of insulin signaling in Drosophila that could be used in a genome-wide RNAi screen . Testing of more than 64 commercially available phospho-antibodies against components of this signaling cascade revealed that none of them recognized an insulin-induced antigen using immunohistochemistry ( data not shown ) . Thus , we generated a phospho-Akt antiserum recognizing the phosphorylation of the C-terminal hydrophobic motif of Drosophila Akt . The single dakt gene encodes two splice forms of 513 and 611 amino acids in length . The antibody ( hereafter referred to as anti P-dAkt ) recognizes two bands in a western blot assay , likely corresponding to the phosphorylated forms of the short and long splice form , respectively . In order to test if the phosphorylation of this hydrophobic motif correlates well with activity of Akt [52] , [53] , we stimulated Drosophila Kc167 cells with insulin for 10 min and induced a robust P-dAkt signal . The hydrophobic motif phosphorylation was strongly suppressed when known components of the insulin signaling cascade , including InR , Chico , the catalytic subunit of PI3K , PI3K92E and dAkt itself were silenced by RNAi ( Figure 1A–1E and 1A'–1E' ) . We next asked whether the anti P-dAkt antibody detected differences in dAkt phosphorylation in the third instar imaginal disc , an established system to study cell and tissue size alterations dependent InR signaling in vivo [55] , [56] , [57] . To validate our assay , we expressed dominant negative insulin receptor ( InRDN ) or a constitutively active catalytic subunit of PI3K ( PI3KCAAX ) , utilizing the UAS-Gal4 expression system [58] . Using apterous-Gal4 ( ap-Gal4 ) to drive expression of InRDN and PI3KCAAX concomitant with membrane-tagged GFP in the dorsal compartment of the wing disc , we compared the levels of P-dAkt immunoreactivity in the dorsal compartment cells to non-expressing ventral cells as controls ( Figure 1K–1L and 1K'–1L' ) . Expression of InRDN resulted in a reduction of P-dAkt levels ( Figure 1F and 1F' ) , whereas PI3KCAAX expression drastically increased the P-dAkt intensity when compared to ventral control cells ( Figure 1G and 1G' ) . Staining of wild-type imaginal wing discs did not reveal a pattern of P-dAkt immunoreactivity associated with compartments or their boundaries ( not shown ) . Western blotting of extracts of Kc167 cells treated with various dsRNAs against components of the insulin signaling pathway confirmed the specificity found by immunostaining of cells and Drosophila tissue ( Figure S1 ) . RNAi-mediated knockdown of InR , PI3K92E or dAkt abolished the anti-P-dAkt reactivity . Together , our data show that anti P-dAkt faithfully detects dAkt phosphorylation , and that the hydrophobic phosphorylation motif correlates with InR-PI3K regulated dAkt activity in cell culture and in vivo . To identify novel regulatory inputs in the insulin signal transduction pathway , we used the Cytoblotting/In Cell Western method in combination with the newly generated anti P-dAkt antibody ( Figure S2A ) as a fast and quantitative cell-based high throughput assay . Cells were grown in 384-well plates and , after three days in the presence of gene-specific dsRNAs of the genome-wide dsRNA library [59] , were fixed and immunostained with anti P-dAkt antiserum . Bound primary antibody was quantified and normalized to cell number . Using this approach , we carried out genome-wide RNAi screens in duplicates without stimulation and after 10 min . of insulin stimulation . We identified 79 dsRNAs that conferred suppression of dAkt phosphorylation , and 56 dsRNAs that enhanced P-dAkt immunoreactivity ( Table S1 ) . Importantly , five out of eight known components functioning upstream of dAkt were identified , validating the reliability of this method ( Figure S2B , S2C , and Table S1 ) . dsRNAs against Chico , PHLLP and Pten , the remaining 3 regulators of dAkt , scored below the cutoff threshold . In our screens , we found that dsRNAs against the small GTPase Rheb , the TORC1 component Raptor and S6K , all downstream mediators required for insulin signal transduction , induced enhanced phosphorylation of dAkt in the absence of insulin . Conversely , dsRNAs against the negative regulators Tsc1 and Tsc2 suppressed the P-dAkt signal when the pathway was activated by insulin . In total , we identified ten out of eleven components known to participate in the Tsc1/Tsc1-TOR signaling branch , with Tctp [60] as the single component not identified by any of our screens ( Figure S2 and Table S1 ) . Interestingly , the function of Tctp as a regulator of Rheb is controversial [61] , [62] . The results of the genome-wide RNAi screen were validated using independent dsRNAs against known insulin pathway components ( Figure 2A and 2B ) . dsRNAs against CSK , MEKK1 and Thread were used as negative controls , and Pten dsRNA as positive control . As observed in the genome-wide screen , removal of the negative regulators Tsc1 and Tsc2 resulted in suppression of P-dAkt in the presence of insulin , while knock down of S6K elevated P-dAkt at baseline conditions . Thus , dAkt phosphorylation is sensitive to interference by Tsc1/Tsc2-TOR-S6K signaling , classically viewed as signaling downstream of dAkt [23] , [63] , [64] , [65] , [66] , [67] . These results are consistent with the existence of an inhibitory feedback signal by the components downstream of dAkt , namely Rheb , Raptor , Tsc1/2 and S6K [50] , [65] . To test the feedback by different means than RNAi , two different strategies were used to inhibit the activator of S6K , TORC1 ( Figure 2C ) . In a chemical approach , we exposed cultured cells to rapamycin , an effective , small molecule inhibitor of TORC1 [68] , [69] , [70] . In a metabolic approach , we starved cultured cells in amino acid-free media , thereby potently inhibiting TORC1 activity [65] , [68] , [69] , [70] , [71] . Rapamycin-induced TORC1 inhibition and amino acid starvation both led to a highly significant increase in P-dAkt compared to control cells treated with solvent control or amino acid-containing medium , respectively . These results confirm the RNAi data and validate the existence of a negative feedback loop that regulates the activation of the pathway by insulin [65] . Since dsRNA-mediated knockdown of S6K enhanced dAkt phosphorylation , we asked whether the inhibitory effect of S6K on dAkt phosphorylation was related to its activity . The activation of Drosophila S6K can be scored using phosphorylation of Thr 398 ( orthologous to Thr 389 in mammalian S6K1 ) as readout ( Figure 3A ) [50] , [65] . We analyzed lysates of Drosophila Kc167 cells pretreated with dsRNAs against Tsc2 , Raptor , S6K and Rheb , for both S6K and dAkt phosphorylation . Cells treated with dsRNA against luciferase and non-RNAi treated cells served as negative controls ( Figure 3A , lanes 4 and 6 ) . Enhanced P-dAkt reactivity correlated with suppression of S6K phosphorylation , with a clear elevation of dAkt phosphorylation when Rheb , Raptor or S6K expression was knocked down . To address how S6K mediates its feedback inhibition of dAkt phosphorylation , we induced dAkt phosphorylation by exclusively removing the negative feedback inhibition in Kc167 cells using RNAi against S6K in the absence of insulin stimulation . The robust enhancement of P-dAkt due to the knockdown of S6K expression was not affected by further RNAi-mediated knockdown of control genes such as GFP , CSK or MEKK1/4 . We then knocked down the individual components of the insulin signaling pathway to assess whether they were required for the enhanced dAkt phosphorylation caused by S6K silencing ( Figure 3B ) . In the S6KRNAi background , RNAi-mediated silencing of Pten ( positive control ) further enhanced the P-dAkt levels , while dsRNA to dAkt ( negative control ) reduced P-dAkt to baseline levels . Importantly , RNAi against the signaling effectors InR or PI3K suppressed the enhanced dAkt phosphorylation conferred by S6K RNAi , indicating that the S6K-dependent feedback inhibition requires the functions of these two upstream signaling effectors . Cell autonomous regulation of dAkt phosphorylation by direct negative feedback has to date not been shown to occur in vivo . To test whether the negative feedback on dAkt phosphorylation also occurs in vivo , i . e . during Drosophila development , we used the wing imaginal disc of the third instar larva . We took advantage of the unique features of the aktq allele [72] , a loss of function allele that encodes a kinase-inactive dAkt protein due to mutation of the DF327G motif in kinase subdomain VII into DI327G . This dAkt mutant protein is unable to phosphorylate downstream components , but is readily expressed and can be phosphorylated by upstream signaling components [72] . We generated homozygous mutant aktq clones in the wing imaginal discs using FLP-FRT-mediated mitotic recombination using the MARCM technique [73] , [74] . This confers GFP expression to the cells expressing the mutant allele only . dAkt protein expression in aktq mutant clones and in the neighboring cells expressing wild-type dAkt were at similar levels ( Figure S3 ) . We then visualized dAkt phosphorylation of aktq mutant and wild-type cells by immunofluorescence using the anti P-dAkt antibody . The presence of an inhibitory mechanism that depends on the activity of the dAkt kinase and negatively feeds back on dAkt phosphorylation predicts enhanced P-dAkt levels in aktq mutant cells when compared to cells expressing wild-type dAkt . Consistently , we observed drastically enhanced phosphorylation of dAkt in clones homozygous for the aktq mutation ( Figure 4A ) . The increased dAkt phosphorylation in aktq mutant cells thus indicates that inactivation of the dAkt kinase function removes repression on dAkt phosphorylation by negative feedback . It further demonstrates the cell autonomous presence of this regulatory loop in imaginal wing discs of third instar larvae . Having established that feedback inhibition leads to repression of dAkt phosphorylation in vivo , we asked whether changes in Tsc1/Tsc2 function would affect the feedback activity on dAkt phosphorylation ( Figure 4 ) . dAkt-mediated phosphorylation of Tsc2 inhibits the function of the Tsc1/Tsc2 tumor suppressor complex [63] , [75] . First , we co-expressed Tsc1 and Tsc2 in the dorsal compartment of the third instar imaginal wing disc under the control of ap-Gal4 . If the Tsc1/Tsc2 complex defines the feedback inhibition of dAkt phosphorylation in vivo , overexpression of Tsc1/Tsc2 is expected to result in increased dAkt phosphorylation . Indeed , compared to ventral control cells , dAkt phosphorylation is clearly elevated in dorsal cells ( Figure 4B ) . This result indicates that forced Tsc1/Tsc2 expression represses the feedback inhibition . Conversely , we induced homozygous mutant clones of either tsc1Q87X or tsc2192 , resulting in the lack of functional Tsc1/Tsc2 tumor suppressor complex [76] , [77] , [78] . Complementary to the Tsc1/Tsc2 overexpression experiment , we expected a decrease in dAkt phosphorylation . Indeed , we found reduced dAkt phosphorylation levels in tsc1Q87X homozygous mutant cells , when compared to wild-type control cells ( Figure 4C ) . Next , we addressed whether the dAkt feedback signaling is routed from dAkt to the Tsc1/Tsc2 complex by evaluating the P-dAkt immunoreactivity in aktq , tsc1Q87X double mutant clones . If Tsc1/Tsc2 transduces the feedback signal originating from dAkt , we expect that the additional elimination of tsc1 function in an aktq clone reverses the increased dAkt phosphorylation found in a clone of aktq single mutant cells . Consistent with this prediction , the level of dAkt phosphorylation in aktq , tsc1Q87X double mutant clones was not elevated when compared to cells with wild-type expression of Tsc1/Tsc2 and dAkt . To the contrary , P-dAkt levels were decreased , more like cells singly deficient in tsc1 function ( Figure 4C ) . We therefore conclude that the Tsc1/Tsc2 tumor suppressor complex controls dAkt phosphorylation in vivo by defining the feedback inhibition . Transcription factors of the FoxO family have emerged as central mediators of the PI3K-dAkt signal transduction pathway [21] , [79] . In Drosophila cell culture and the adult fly , the InR transcript is selectively transcribed and translated under conditions of low dAkt signaling levels [38] , [39] , [40] . Since these data suggests an alternative route of feedback mediated regulation of dAkt phosphorylation that is independent of TORC1 and S6K , we wanted to test the role of dFOXO in the negative feedback regulation of dAkt phosphorylation in the third instar imaginal wing disc . To do so , we used an activated form of dFOXO ( dFOXO-TM ) , in which the dAkt phosphorylation sites have been replaced by alanines ( Figure S4 ) [80] . These mutations result in constitutively nuclear localization of dFOXO , and strong transactivation of target genes both in vitro and in vivo [38] , [39] , [81] . Expression of dFOXO-TM by means of ap-Gal4 did not reveal any discernable differences in dAkt phosphorylation between dFOXO-TM expressing versus non-expressing cells ( Figure S4A ) . Furthermore , mitotic clones homozygous for the dfoxo25 loss of function allele , which is predicted to encode a truncated protein [81] , retain a similar amount of P-dAkt as wild-type control cells ( Figure S4B ) . Finally , aktq , dfoxo25 double mutant clones show increased dAkt phosphorylation , similar to homozygous clones of aktq alone ( Figure S4C ) . These data indicate that dFOXO is not involved in the negative feedback regulation of dAkt phosphorylation in the developing wing disc . To further delineate the feedback inhibition pathway mediated by the Tsc1/Tsc2 complex , we analyzed the requirement of TORC1 in the downregulation of dAkt phosphorylation . The protein kinase TOR has been found in close physical proximity of Tsc1 and Tsc2 , and biochemical and genetic evidence have established that TOR is a central mediator of Tsc1/Tsc2 signaling [76] , [82] , [83] , [84] , [85] . However , TOR is part of TORC1 as well as TORC2 , and the former is required for S6K activation , which , in cell culture , brings forth negative feedback on dAkt phosphorylation , while the latter is required for hydrophobic motif phosphorylation of dAkt [16] . To interfere specifically with TORC1 function , we expressed an RNAi hairpin construct against Raptor ( raptorRNAi ) , a component present only in TORC1 and not in TORC2 [49] , [86] . Using ap-Gal4 , we compared the dAkt phosphorylation levels in UAS-RaptorRNAi expressing , GFP-positive dorsal cells to those in wild-type , GFP-negative control cells in the ventral compartment ( Figure 5A ) . If TORC1 is required to drive feedback inhibition of dAkt phosphorylation , its inactivation should augment the level of P-dAkt immunoreactivity . Accordingly , we observed increased P-dAkt staining in RaptorRNAi cells . We further tested whether TORC1 is required for the decrease in dAkt phosphorylation observed in tsc1 mutant wing disc cells . We expressed raptorRNAi in mitotic clones homozygously mutant for tsc1W243X . Loss of tsc1 results in derepression of Rheb and TORC1 activity [23] , [64] , which , accordingly , resulted in excessive feedback inhibition of dAkt phosphorylation in tsc1 mutant cells ( Figure 5B ) . Feedback inhibition by Tsc1/Tsc2 mediated through TORC1 predicts that loss of tsc1 concomitant with a reduction of raptor function by RNAi confers the same P-dAkt phenotype as that of raptorRNAi alone . Indeed , raptorRNAi expression in tsc1W243X mutant cells displayed an increase in P-dAkt immunostaining , as seen in cells expressing raptorRNAi alone ( Figure 5C ) . This is consistent with the loss of negative feedback inhibition of dAkt phosphorylation in raptorRNAi , tsc1W243X cells , indicating an epistatic relationship of TORC1 to Tsc1 in the negative feedback circuit . S6K is a central player downstream of TORC1 , and TORC1 activity is directly required for the activation of S6K [25] , [35] . To test the role of S6K in the feedback inhibition of dAkt phosphorylation , we generated homozygous clones of an s6K null allele ( s6Kl-1 ) [24] , and investigated the level of dAkt phosphorylation in reference to wild-type tissue . If S6K mediates the feedback inhibition emanating from TORC1 , dAkt phosphorylation should be increased in s6Kl-1 cells , since the inhibitory feedback on dAkt would be released . To our surprise , no change in P-dAkt level was apparent ( Figure 6A ) , suggesting that S6K does not regulate the negative feedback signaling mediated by Tsc1/Tsc2 and TORC1 at this stage of wing imaginal disc development . Because this in vivo finding differed strikingly from the results in Drosophila cell culture , we verified that the s6Kl-1 chromosome did not carry additional mutations . The lethality associated with our s6Kl-1 stock [24] was rescued by expressing a S6KWT cDNA from an act-Gal4 driver . To further assess if the phosphorylation status of dAkt varies in the s6K l-1 mutant background in a tissue dependent fashion , we performed a western blot analysis of extracts from whole third instar wild-type and s6Kl-1 mutant larvae ( Figure S5 ) . Consistent with our result in wing imaginal disc clones of s6K l-1 , we did not observe an increase of P-dAkt . However , we detected a downregulation of total Akt protein expression in extracts from s6Kl-1 mutant larvae when compared to wt , thus suggesting additional regulatory mechanisms controlling dAkt in different tissues . Most studies on feedback regulation of dAkt by S6K are carried out in the context of either tsc1 or tsc2 mutants or other experimental settings of putatively high TORC1 activity [18] , [87] . This led us to probe the dependence of the feedback inhibition on S6K in a high TORC1 signaling background induced by a tsc2 mutant context . First , we confirmed that , similar to tsc1Q87X ( Figure 4C ) , P-dAkt is downregulated in cells homozygously mutant for tsc2192 ( Figure 6B ) . Subsequently , we generated s6kl-1 , tsc2192 double mutant clones and stained the cells with anti P-dAkt antiserum ( Figure 6C ) . Surprisingly , and in contrast to s6kl-1 single mutant clones and tsc2192 single mutant clones , tsc2192 , s6kl-1 double mutant tissue of the wing imaginal disc displayed elevated P-dAkt levels compared to wild-type cells ( Figure 6 ) . We observed a similar result using a different allelic combination , s6Kl-1 , tsc2* ( data not shown ) . This result suggests that the feedback inhibition of dAkt phosphorylation depends on S6K only when TORC1 signaling is elevated above its wild-type activity . The observation that ablation of S6K function only affects feedback inhibition of dAkt phosphorylation when TORC1 signaling is elevated suggests that activation of S6K by TORC1 in a wild-type context is insufficient to affect dAkt phosphorylation . The activation of S6K by TORC1 involves phosphorylation of several sites in the auto-inhibitory domain and the linker region of S6K [88] , [89] . We used ap-Gal4 to express either wild-type S6K ( S6KWT ) , or mutant S6K forms ( S6KTE , S6KSTDE or S6KSTDETE [90] , which are intrinsically activated due to substitution of several serine and threonine residues by acidic amino acids in the linker ( S6KTE ) , the autoinhibitory domain ( S6KSTDE ) , or both ( S6KSTDETE ) . Overexpression of S6KWT did not visibly change the level of dAkt phosphorylation when compared to ventral , non-S6K-expressing control cells ( Figure S6 ) . However , expression of the activated alleles S6KTE , S6KSTDETE and , to a limited extent , S6KSTDE , resulted in decreased dAkt phosphorylation , when compared to ventral non-expressing cells , reminiscent of the effect of high TORC1 signaling . We further addressed whether activated S6K is also sufficient to elicit inhibition of P-dAkt when TORC1 activity is low . To this end , we co-expressed Tsc1 and Tsc2 , to dominantly inhibit TORC1 , and assessed P-dAkt levels in the absence or presence of S6KTE co-expression ( Figure S7 ) . As observed above ( Figure 4 ) , Tsc1/Tsc2 expression caused a pronounced increase in P-dAkt ( Figure S7A and S7A' ) . Strikingly , simultaneous expression of dominant active S6K ( S6KTE , Figure S7B and S7B' ) reversed the elevated P-dAkt down to a near wild-type level . Altogether , these results suggest that activation of S6K is sufficient to elicit feedback inhibition of dAkt phosphorylation under normal or inhibited TORC1 activity levels . However , in wild-type wing imaginal disc cells , endogenous S6K is not sufficiently activated to regulate the feedback inhibition of dAkt phosphorylation . These results suggest that S6K serves as a sensor and homeostatic regulator of dAkt-TOR signaling intensity in vivo .
Our initial experiments on dAkt phosphorylation in wing imaginal discs demonstrated that expression of activated PI3K ( PI3KCAAX ) or dominant negative InR ( InRK1409A ) elevated or repressed , respectively , the level of dAkt phosphorylation . This observation indicates that in these cells , dAkt phosphorylation can be enhanced or repressed , depending on the signaling input , and thus that dAkt has “room” for quantitative regulation of activation . Accordingly , high levels of negative feedback , caused by mutational inactivation of tsc1 or tsc2 , suppress dAkt phosphorylation , while low levels of negative feedback signaling , as in RaptorRNAi-expressing cells , enhance dAkt phosphorylation . These results lead to the conclusion that the wild-type level of dAkt phosphorylation in these cells is set by negative feedback regulation that is executed by the Tsc1/Tsc2-Rheb-TORC1 arm of the dAkt-TOR pathway . Interestingly , in s6Kl-1 mutant clones , levels of P-dAkt are unchanged , indicating the independence of the negative feedback circuit from S6K activity under these conditions . These results differ from the reported elevated Akt kinase activity in extracts from whole s6Kl-1 second instar larvae [65] , [91] . Studies of whole larval extracts may reflect the regulation in endoreplicating tissues , which dominate the body mass at that stage of development , whereas our results of s6Kl-1 mutant clones in the wing disc examine Akt phosphorylation in a mitotically active tissue . The disparity in Akt phosphorylation may therefore reflect differences in negative feedback regulation of Akt at discrete stages of development and in distinct tissues . The recent observation that inhibition of TORC1 by rapamycin treatment in adult flies results in loss of S6K phosphorylation and , presumably , activity without eliciting changes in dAkt phosphorylation serves as a case in point [92] . The tissue specificity of dAkt feedback regulation will be an interesting topic of future investigation . In the wing imaginal disc , the feedback-driven changes in dAkt phosphorylation occur in a manner that is uncoupled from changes in dAkt protein expression . Indeed , the genetic manipulations that resulted in changes in dAkt-TOR signaling activity left the dAkt expression levels unchanged . The only exception of a slight reduction in overall dAkt level , yet increased dAkt phosphorylation , was observed upon expression of PI3KCAAX ( see Figure S3E and S3E' ) . We therefore propose that , in the wing imaginal disc , a change in the phosphorylation status of dAkt , but not in protein expression , represents the relevant regulatory event in vivo that is targeted by TORC1-dependent negative feedback . Changes in dAkt activity by manipulating the negative feedback can have significant biological effects [42] , [93] , [94] . In contrast to our findings in wing discs , we do observe a reduction of Akt protein levels in whole third instar larval extracts of s6Kl-1 mutants . Since in third instar larvae the wing imaginal discs represent only a minor and select fraction of cells and tissues , the mass disparity of larval vs . imaginal wing tissue may explain this differing result of dAkt in our western blot vs . the clonal wing imaginal disc analysis . Of note , our western blot analyses differed significantly from those reported by Radimerki et al . [65] , [91] , in the use of third versus second instar larval extracts , differences in protein extraction , antibodies , and normalization against total protein versus Akt levels . We interpret the divergent results as due to different assays and developmental stages analyzed . Importantly , a change of Akt protein levels under altered Akt-TOR signaling conditions is not unprecedented [95] . Although the TORC1-dependent feedback needs to be biochemically characterized , two mechanisms may be envisioned . First , TORC1 could participate in an inhibitory step required for downregulation of dAkt activity . This possibility may be supported by experimental evidence that TORC1 can elicit a direct inhibitory phosphorylation of IRS1 in mammalian cell culture [96] , [97] . Second , disruption of TORC1 by RNAi knockdown against specific components of the complex may release the remaining components of TORC1 , and might shift a mass-action equilibrium between TORC1 and TORC2 towards TORC2 . While such equilibrium has been suggested [98] , there is , to our knowledge , only experimental evidence for an equilibrium shift towards TORC1 when TORC2 is disrupted , but not vice versa [16] , [50] . Lastly , in mammalian cells the Tsc1/Tsc2 complex is required for proper TORC2 activation , independently from its role in negative feedback signaling [99] . Nevertheless , we observe that RaptorRNAi hairpin expression reverses the decrease in dAkt phosphorylation in a tsc1 mutant clone , although not to the same extent as expression of the RaptorRNAi hairpin driven by ap-Gal4 . These findings suggest that negative feedback is a central route of Tsc1/Tsc2 to regulate dAkt phosphorylation . However , we cannot exclude a functional role of Tsc1/Tsc2 in the activation of TORC2 [99] . We also provide evidence for S6K-dependent negative feedback inhibiting the phosphorylation of dAkt in vivo . The S6K-dependent mode of feedback was previously proposed based on data in mammalian or Drosophila cell culture . Accordingly , we observed an S6K-dependent negative feedback circuit inhibiting the phosphorylation of dAkt in Drosophila Kc176 cells [65] , [66] , [67] . In vivo , however , this mode of feedback was observed only in cells with high TORC1 signaling , and was not seen in wild-type conditions , where the negative feedback mechanism is TORC1-dependent and does not depend on S6K . We therefore propose that in the wing imaginal disc , under conditions of high TORC1 signaling , the cells switch their feedback mechanism from a TORC1-dependent mode to an S6K-dependent mechanism similar to what is observed in Kc167 cell culture . We suggest that the constant presence of serum , insulin and high amino acid concentrations in the cell culture medium foster high TORC1 activity , favoring the S6K dependent negative feedback route . However , it is possible that in cultured Kc167 cells , both feedback mechanisms are simultaneously operative . Indeed , we found that , in Kc167 cells , RNAi-mediated knockdown of the TORC1 component Raptor triggers a stronger increase in dAkt phosphorylation than RNAi against S6K ( see Figure S2B and Figure S8 ) . The elevated dAkt phosphorylation observed in cells with increased Tsc1 and Tsc2 expression strongly supports a negative feedback regulation of dAkt phosphorylation by the Tsc1/Tsc2 complex in vivo . Nevertheless , this finding is surprising at two levels . First , dAkt has been described as a positive regulator of cell size , and aktq homozygous mutant clones show reduced cell size [100] . However , forced expression of Tsc1/Tsc2 results in reduced cell size , despite elevated dAkt phosphorylation [76] , [77] , [78] . The reciprocal experiment highlights the same paradox: tsc1Q87X or tsc2192 mutant cells have a larger size , despite decreased dAkt phosphorylation and activity [76] , [77] , [78] . These results may indicate that , for cell size , dAkt's function is to regulate Tsc1/Tsc2 activity , which is supported by the fact that so far no other dAkt substrate ( e . g . FoxO , Gsk3beta ) has been shown to elicit a cell size defect [81] . Correspondingly , the ability of Akt1 and Akt2 deficiency to suppress H-Ras mediated oncogenesis in mouse mammary glands is overcome by inactivation of tsc2 , again supporting the hypothesis that a central function of dAkt in vivo is the regulation of TORC1 activity [101] . The consistency of the data in Drosophila with those in mice indicates that this function of dAkt in dAkt-TOR signaling is conserved . The second surprise relates to the elevated dAkt phosphorylation in the presence of ectopic Tsc1/Tsc2 expression , which may at first seem intuitive . The circular structure predicts that increased Tsc1/Tsc2 expression should inactivate the feedback inhibition of dAkt phosphorylation by repressing TORC1 , thus releasing dAkt from negative feedback regulation , hence increasing dAkt phosphorylation . However , a perfectly circular dAkt-TOR pathway predicts that increased Tsc1/Tsc2 levels should trigger high dAkt activity , which in turn should inactivate the Tsc1/Tsc2 complex by direct phosphorylation of Tsc2 [75] , [102] , [103] . Thus , depending on the strength of the dAkt-Tsc2 connection , dAkt phosphorylation could either remain unchanged or even be reduced . However , the dAkt-Tsc2 link might be less physiologically relevant than initially suggested [75] , [104] , [105] , pointing to additional regulatory connections of the InR-PI3K-dAkt and Tsc1/Tsc2-Rheb-TORC1 signaling branches [20] , [99] , [106] , [107] , [108] , [109] . Alternatively , overexpressed Tsc1/Tsc2 may localize to a subcellular compartment where it escapes phosphorylation by active dAkt , yet can inhibit TORC1 , or the derepressed activity level of dAkt might be insufficient to effectively control overexpressed Tsc1/Tsc2 . In the developing Drosophila wing disc , S6K is a central mediator of TORC1 activity , especially as the fly 4E-BP1 ortholog , Thor , is not expressed [110] . Since ectopic expression of activated S6K , but not wild-type S6K , results in decreased dAkt phosphorylation , we conclude that S6K activation is sufficient to elicit a negative feedback on dAkt . In the activated S6K mutants , sites in the linker and autoinhibitory domain that are normally phosphorylated by TORC1 are replaced by phospho-mimetic acidic amino acids [90] . Our data therefore suggest that linker and the autoinhibitory domain phosphorylation of S6K may function as sensor for the TORC1 signaling load . Thus , only when TORC1 is highly active , S6K will become sufficiently phosphorylated to drive the negative feedback , a scenario that is mimicked by ectopic expression of activated S6K . Of note , the S6KTE and S6KSTDETE phospho-mimetic mutants , which have the linker site mutation , exert a visibly stronger inhibition of dAkt phosphorylation than the S6KSDTE phospho-mimetic mutant of the autoinhibitory domain only . These differences may suggest that the phosphorylation site in the linker region of S6K is the predominant site for transducing TORC1 activity . In Drosophila cell culture , supra-physiological levels of nutrients and amino acids may then trigger the high TORC1 activity required to drive S6K-mediated feedback on dAkt phosphorylation . Since mTOR has recently been shown to be targeted for phosphorylation by S6K [111] , it is tempting to speculate about a mechanism involving a feedback by S6K on TORC1 that then could drive the switch between TORC1- and S6K-dependent feedback inhibition of Akt phosphorylation . However , the T2446 and S2448 sites in mTor that are phosphorylated by S6K are not conserved in Drosophila TOR . In conclusion , we demonstrate that dependent on TORC1 signaling load , the negative feedback signal regulating dAkt activity is dynamically routed in vivo . It is either independent of S6K ( under “normal” TORC1 activity ) or dependent on S6K ( when TORC1 activity is high ) . Therefore , we interpret the function of S6K as a sensor of TORC1 signaling that selectively provides additional dampening of the signaling input once TORC1 is highly active . These findings predict that pharmacological tools selectively impinging on S6K activity , in the context of obesity treatment or other conditions with high S6K activity , might carry the significant risk of uncontrollable TORC1 activity .
For 384-well plate experiments , cells were uniformly dispensed into clear bottom black 384-well plates ( Corning ) containing 250ng of individual , arrayed dsRNAs using a MultiDrop liquid dispenser ( Thermo ) . 8×103 cells per well in 10 ul of serum-free media per well were seeded . After 60 min of incubation , 70 ul of 10% serum-containing culture medium ( Schneider's Medium , Invitrogen ) per well was added . After three days of incubation at 25°C , cells were washed once and starved in 80 ul serum-free medium overnight ( 12 hrs ) . For insulin stimulation , cells were exposed to a final concentration of 387 nM bovine insulin ( Sigma ) for 10 min . Rapamycin was used at a final concentration of 50 nM for 4 hrs , amino acid free media ( Atlanta Biologicals ) was used for 8 hrs . For western blotting , six-well dishes wereused and the conditions were scaled accordingly . 10 ug of dsRNA per well was added to 1 . 5×106 cells per well in 1 ml of serum-free media , supplemented after 60 min with 5 ml of serum-containing media . For immunofluorescence , we used eight-well chamber slides , and cells were treated as described above , using 2 ug of dsRNA per well in 100 ul of serum-free media , complemented with 500 ul of serum-containing media after 60 min . All primer sequences of the genome-wide dsRNA library are available on the website of the Drosophila RNAi Screening Center ( www . flyrnai . org ) . dsRNAs were generated from PCR-derived DNA templates by T7 RNA polymerase driven run-off transcription in vitro ( Ambion ) . The generic T7 promoter sequence TAATACGACTCACTATAGG was added 5′ to all gene specific primers . All gene-specific primers were designed using Primer3 [112] , and conceptual PCR products were controlled against off-target effects using SnapDragon ( http://flyrnai . org/cgi-bin/RNAi_find_primers . pl ) . Gene-specific primer sequences used: GFP: CAAGGGCGAGGAGCTGTT , GTCGTCCTTGAAGAAGATGGTG; CSK: GAGGAAGCAGACGGCAAC , GGGACTTGGGCGAATGAT; MEKK1: AAGTGTGTGTTGGTGCTGGA , ATCTTCGGGAGGCAGGTC; Thread: GCTGGACTGGCTGGATAAAC , ATTCGGGATACTGGGGAAAA; InR: CAGCGCGAAAACTTCAATATCTTT , TGTTTTATCCAGTCCATCGGCTAT; Chico: CCAAGCATAGATTTGTCATTGTGC , GATCACCAGATCCCAAGACACTTT; PI3K92E: GAGGCACCAGATCCAAAATC , ATACAGCCGGAAGTCGTCAA; PI3K21B: GCTTTATCGAGACGGACCTG , GCATCCAGCAGATTGAGGAG; Pten: TGTATTATGCCAAGCGGAAGA , TCAATCGTTGGAGGGTTATGA , dAkt: GTCCACAAATCATCCGTTCC , ACCTCCTCCACCAAAATCAA; Tsc1: GAGGTAAACAATACGCGATGGAAG , AACTGAACTGACTCTGCTGGTCCT; Tsc2: CTAGACAGTCGTCAGGTGATCGTG , ACGCGACTAAGGATTTCTTCTTCA; S6K: TCTGCACCAAGACACTGAGG , GCAGTATGTTCTCGGGCTTC; Raptor: ACCTGGGTAAGGTGATTAGCAACA , AGGTGCAGAGCTTCTTAACGTCAT; Rheb: GCTAGGAGTGGTATTTCGGCTTC , CCAGTGCTTTGAAATAAATGGAGA; PDK1: CAAGGAGAAAGCATCAGCAA , GCCTATGTAACGACCGAAAATG . Kc167 cells were rinsed once , scraped in PBS , and pelleted at low speed in a table top centrifuge . The cell pellet was lysed in standard SDS-PAGE loading buffer without dye . Extracts of third instar larvae were prepared by mechanical homogenization and lysis in 50 mM Tris , 120 mM NaCl , 30 mM NaF , 50 uM NaVO4 , 1% Triton X100 , 0 . 1% SDS with Complete protease inhibitor cocktail ( Roche ) . Lysates were cleared from debris and lipids by 10 min centrifugation in a table top centrifuge . For all protein lysates , protein concentrations were determined using Protein DC Assay ( Bio-Rad ) , and total protein concentrations of lysates were adjusted accordingly . The Cytoblot protocol for 384 well plates used here consists of 4 steps: ( 1 ) fixation , ( 2 ) permeabilization , ( 3 ) P-dAkt staining comprising of incubations with primary and secondary antibodies , and ( 4 ) DNA staining to assess total cell numbers in each individual well . Two versions of the cytoblot were used . The “first generation” cytoblot utilized HRP-conjugated secondary antibody and chemiluminescence to detect anti P-dAkt . This protocol was applied to the non-stimulated RNAi screen . The “second generation” cytoblot employed a fluorescently labeled secondary antibody and was used for the insulin-stimulated RNAi screen . The availability of a LiCor Aerius plate reader allowed the switch from luminescence to fluorescence based detection . ( 1 ) Fixation: Tissue culture medium from the 384 well plates was removed and cells were fixed with 6% Formaldehyde for 90 minutes , followed by three washes with 80 ul of PBS . ( 2 ) Permeabilization: 0 . 1% Triton X-100 in PBS for 30 minutes ( 40 ul per well ) , followed by 3 washes in 80 ul PBS for 10 min . ( 3 ) P-dAkt staining: Cells were blocked in 5% non-fat milk in PBS for 60 minutes ( 90 ul per well ) . Anti-Drosophila P-dAkt Ser505 primary antibody ( 20 ul per well , 1∶800 diluted in 5% non-fat milk , Cell Signaling Technology , Beverley , MA ) was added and incubated at 4°C overnight . After 3 washes with PBS ( 80 ul per well , 10 min ) , 20 ul secondary antibody ( goat anti-Rabbit AlexaFluor 680 , diluted 1∶2 , 500 , Invitrogen , for “second generation Cytoblot” , used for insulin-stimulated RNAi screens ) or a 1∶1 , 200 dilution of goat anti-Rabbit HRP ( “first generation Cytoblot” , used for insulin-stimulated RNAi screen , Jackson Laboratories ) , was added in 5% non-fat milk and incubated for 4 hrs , followed by 3 washes with 80 ul of PBS , 10 min and 20 ul PBS was added to each well . Signal was developed by 20 ul SuperSignal West Pico Chemiluminescent Substrate ( Pierce ) . HRP luminescence was read in Molecular Devices plate reader . Alexa 680 fluorescence was measured using a LiCor Aerius plate reader ( 680/720 nm ) . HRP luminescence and Alexa 680 fluorescence were interpreted as amounts of P-dAkt per well . ( 4 ) DNA staining was performed with Sytox Green ( Invitrogen ) 1∶20 , 000 in PBS for 30 min ( 40 ul per well ) . After 3 additional washes with PBS , plates were filled with 20 ul PBS per well and the fluorescent value of the Sytox dye DNA stain were read in a Molecular Devices plate reader ( 520/560 nm ) . This value , referred hereafter to as nuclear fluorescence ( NucFl ) , is interpreted as the value representing relative cell numbers per well . All liquid manipulation steps were performed using a MultiDrop liquid handling device ( Thermo ) . All individual values quantifying amounts of P-dAkt were normalized to the cell number per well using the nuclear fluorescent value from the DNA stain . For the insulin-stimulated screen , linear regression was performed on the log2 ( P-dAkt/NucFl ) values of each individual screen plate , and residuals from the log2 ( P-dAkt/NucFl ) values to the regression line were calculated . All residuals of each genome-wide screen were pooled and a cell number dependent error model was developed to determine locally weighted standard deviations ( SD ) and averages in dependence of cell number . Z-Scores using these two parameters were calculated , expressing the deviation from the local average value in SDs . All Z-Scores were corrected against position effects by setting the Mean Z-Score of each individual well position across one genome-wide screen replicate to zero . All dsRNAs with predicted off-target effects ( homologies to non-target genes of 19 bp or more ) were excluded from data processing and the result file . Results from the two screening replicates were averaged and a cut-off value of +/− 2 . 5 applied . Due to high variation within each 384 well screening plate caused by individual 96-well source plates ( each 384-well screening plate is composed of aliquots from four 96-well source plates ) , Z-Scores for the baseline ( no insulin stimulation ) genome wide RNAi screen were calculated as follows: The 384 ( P-dAkt/NucFl ) values of each single screening plate were decomposed into the four 96 well groups , each defined by a single source plate , and the mean and SD was set to zero and one , respectively . This step compensated these inequalities , and data were recombined to 384 well plate data sets . Mean and SD for each individual 384 well plate were calculated , averaged between screens , and a cut-off value of three SDs was applied . For non-genome-wide RNAi experiments , an external standard consisting of 768 values of non-RNAi treated cells covering the whole spectrum of cell densities was used to determine cell number dependent averages and SDs to calculate experimental Z-Scores of RNAi treated wells . All P-dAkt values of non-stimulated cells were normalized using a baseline standard curve ( the average non-treated , non-stimulated experiment scores zero ) . For the insulin-stimulated data set , the P-dAkt values of insulin-treated cells were normalized using a standard curve derived from insulin-stimulated cells ( the average non-treated , insulin-stimulated experiment scores zero ) . All P-dAkt indirect immunofluorescence images , Cytoblots and western blots were performed using anti-Drosophila P-dAkt Ser505 ( Lot 1 and Lot 2 , Cell Signaling Technology ) using a 1∶200 , 1∶800 and 1∶200 dilution , respectively . For immunofluorescence , AlexaFluor594 and AlexaFluor488 conjugated secondary antibodies against Rabbit , Mouse and Goat were used 1∶500 ( Invitrogen ) . Western blotting was performed using HRP conjugated anti-rabbit and anti-mouse antisera ( Amersham ) . Pan-dAkt and P-S6K Ser398 ( Cell Signaling Technology ) were used 1∶200 . Anti-GFP was purchased from Cappel and used at 1∶4000 . Mouse anti alpha-Tubulin ( Sigma ) was used 1∶2000 for immunofluorescence . Rabbit anti S6K was a generous gift from Mary Steward and used 1∶10 , 000 for western blotting . Imaginal discs and Drosophila Kc167 cells were fixed using 6% Formaldyhyde in PBS ( cells 10 min at room temperature , imaginal discs at 4°C overnight ) , permeabilized in 0 . 1% Triton X-100 ( 10 min for cells , 2 hrs for imaginal discs ) and blocked with 5% BSA in PBS ( 1 hr ) . Primary antibody incubation was performed overnight at 4°C with antibody dilutions as indicated above , using 5% BSA . After 3 washes with PBS , secondary antibody was incubated overnight in 5% BSA , followed by 3 washes in PBS . Specimens were mounted using Vectrashield mounting medium with DAPI ( Vector Co . ) . All data were acquired using a Leica SP2 confocal microscope , a 63x lens , digital zoom factor of four , 1024×1024 pixel detector setting and processed using Adobe Photoshop software . Images of experimental and control cells were processed identically . Mutant wing imaginal disc clones were generated by FLP/FRT-mediated mitotic recombination using the following chromosomes: FRT82B , aktq [72]; FRT82B , aktq , tsc1Q87X [76]; FRT82B , aktEX4 ( derived by imprecise excision from aktP04226 , Bloomington Stock center ) FRT82 , tsc1Q87X [77]; FRT82B , tsc1W243X [77]; FRT82B , foxo25 [81]; FRT82B , foxo25 , aktq [81]; tsc2192 , FRT80B [77]; tsc2* , FRT80B ( generous gift from I . K . Hariharan ) ; s6Kl-1 , FRT80B [24]; s6Kl-1 , tsc2192 , FRT80B [71]; s6Kl-1 , tsc2* , FRT80B . Males of the respective genotypes were crossed to y , w , hs-FLP , UAS-mCD8::GFP; tub-Gal4; FRT82B , tub-Gal80/TM6B or y , w , hs-FLP , ubi-GFP , FRT80B females and larvae were heat shocked 60 hrs +/− 12 hrs after egg laying ( unless otherwise specified ) at 37°C for 45 min . Overexpression of PI3KCAAX [113] , InRDN ( Bloomington Drosophila Stock Center ) , Tsc1 and Tsc2 [77] , Foxo™ [80] , S6KWT [78] , S6KTE , S6KSTDE and S6KSTDETE [90] in the dorsal compartment of the wing imaginal disc was performed using the Gal4-UAS system [58] with y , w; ap-Gal4 , UAS-mCD8::GFP ( gift from C . Micchelli ) . The RaptorRNAi hairpin and transgenic line was generated using the VALIUM1 vector [114] as part of the transgenic RNAi project ( TRIP , http://flyrnai . org/TRiP-HOME . html ) . Based on BLAST searches , information in the public ortholog databases InParanoid [115] , and Homologene [116] published sequence homologies [117] , CG3004 ( Fbgn0030142 ) , and CG10105 ( Fbgn0033935 ) were referred to as Lst8 and Sin1 [29] , [50] , respectively . | The development of multi-cellular organisms depends on the precise choreography of a diverse array of signal transduction pathways . This requires balanced regulation by activating as well as repressing signals . Negative feedback , defined as a signaling response counteracting the stimulus , is a frequently used mechanism to dampen signaling pathway activity . Accordingly , loss of negative feedback is often observed during progression of cancer , while constitutive engagement of negative feedback contributes to chronic loss-of-function phenotypes . Ectopic activation of the Akt–TOR pathway is frequently associated with tumor susceptibility and cancer and contributes to obesity-induced metabolic disease and type II diabetes . Using Drosophila cell culture and the developing fly , we dissect the regulatory circuitry defining negative feedback regulation of dAkt . Our work shows that dAkt activity is regulated by two qualitatively different negative feedback mechanisms and that the activity level of the dAkt pathway dictates which feedback mechanism is utilized . Under normal physiological activity conditions , we observe a feedback mechanism that is dependent on TOR complex 1 , but independent of S6K . Under conditions of pathological high pathway activity , we observe an S6K–dependent negative feedback mechanism . Our identification of a quantitative-to-qualitative switch in dAkt–TOR negative feedback signaling might have important implications in the biology of cancer and metabolic diseases . | [
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Genomic conflict is perplexing because it causes the fitness of a species to decline rather than improve . Many diverse forms of genomic conflict have been identified , but this extant tally may be incomplete . Here , we show that the unusual characteristics of the sex chromosomes can , in principle , lead to a previously unappreciated form of sexual genomic conflict . The phenomenon occurs because there is selection in the heterogametic sex for sex-linked mutations that harm the sex of offspring that does not carry them , whenever there is competition among siblings . This harmful phenotype can be expressed as an antagonistic green-beard effect that is mediated by epigenetic parental effects , parental investment , and/or interactions among siblings . We call this form of genomic conflict sexually antagonistic “zygotic drive” , because it is functionally equivalent to meiotic drive , except that it operates during the zygotic and postzygotic stages of the life cycle rather than the meiotic and gametic stages . A combination of mathematical modeling and a survey of empirical studies is used to show that sexually antagonistic zygotic drive is feasible , likely to be widespread in nature , and that it can promote a genetic “arms race” between the homo- and heteromorphic sex chromosomes . This new category of genomic conflict has the potential to strongly influence other fundamental evolutionary processes , such as speciation and the degeneration of the Y and W sex chromosomes . It also fosters a new genetic hypothesis for the evolution of enigmatic fitness-reducing traits like the high frequency of spontaneous abortion , sterility , and homosexuality observed in humans .
Sex chromosomes are unusual compared to the autosomes for three reasons . First , when present in the heterogametic sex , the two types of sex chromosome are transmitted to opposite sex offspring . Second , it is common for recombination to be suppressed over a part or all of their length . Third , non-recombining sex chromosomes can evolve to become far more dimorphic than autosomes . It has long been recognized that these characteristics can contribute to genetic conflict in the context of meiotic drive , but other forms of potential sex-linked genetic conflict have received relatively little attention ( reviewed in [1] ) . Here we evaluate the potential for the special characteristics of the sex chromosomes to contribute to a meiotic-drive like process – sexually antagonistic zygotic drive ( hereafter , SA-zygotic drive ) – that operates due to competition among opposite-sex siblings , rather than gamete types . The phenotypes that fuel this process are sexually antagonistic green-beard effects ( hereafter SA-GrBd-effects ) that only operate when there is competition among siblings . A green-beard effect [2] , [3] is a complex trait coded by a pleiotropic gene , or a collection of tightly linked genes , with three distinct characteristics ( Figure 1 ) : they cause the carrier to i ) produce a distinguishing phenotype ( tag ) , ii ) differentiate among other individuals based on the presence or absence of the phenotype ( tag-differentiation ) , and iii ) augment the fitness of other individuals expressing the phenotype ( tag-directed-aid ) . A green-beard effect is antagonistic when it reduces the competitive ability of individuals that do not express the tag , thereby increasing the fitness of individuals carrying the gene that codes for it . Because green-beard effects require complex and multifarious pleiotropy , they have previously been presumed to be rare in nature [2] , [3] . However , documented examples of green-beard effects do exist ( e . g . , [4]–[6] ) . For example , in the red fire ant ( Solenopsis invicta ) egg-laying queens are heterozygotes for the a and b alleles at the Gp-9-locus . Homozygous queens are absent because the b allele is a recessive lethal and developing aa queens are killed by ab heterozygous workers ( but not by aa homozygous workers ) [4] . Queens with the ab genotype that were experimentally rubbed against aa queens were also killed by heterozygous workers . These data indicate that the b allele ( or an allele at a tightly linked locus ) displays an antagonistic green-beard phenotype because it enhances its own propagation by killing aa competitors ( identified by their smell ) that do not carry it . Green-beard effects may also feasibly operate in humans and other placental mammals ( by influencing resource transfer between maternal and fetal tissue ) in the context of self-recognizing gene products , e . g . , homophilic cell adhesion molecules that have extracellular domains that recognize copies of themselves expressed on other cells [7] . What has not been appreciated previously , however , is that the special characteristics of sex chromosomes greatly facilitate the evolution of SA-GrBd-effects whenever there is competition among siblings . For simplicity – but without loss of generality – we will assume male heterogamety . There are , however , some important biological differences between male ( XY ) and female ( ZW ) heterogamety , and when appropriate , we will point out how such differences may influence the course of evolution . Lastly , when we refer to the two types of sex chromosomes , we will be referring to the portion of these chromosomes that does not recombine in the heterogametic sex . Sex chromosomes are predicted to evolve to code for SA-GrBd-effects , and the sexually antagonistic zygotic drive that they propel , for three reasons . First , all X- and Y-linked genes co-segregate during male meiosis like a single Mendelian gene that is highly pleiotropic . As a consequence , different genes on the same sex chromosome , rather than pleiotropy of a single gene , can code for the multifarious phenotypes required for green-beard effects to operate . A second feature promoting X- and Y-coded SA-GrBd-effects is the presence of the master sex-determining gene on one of these chromosomes . This linkage creates a perfect association between the presence or absence of a father's X and Y in his offspring and all sexually dimorphic phenotypes that are coded by any gene in the genome , i . e . , within a family , all daughter-specific traits are effectively paternal X-tags and all son-specific traits are effectively Y-tags ( Figure 2 ) . The final feature contributing to sex chromosomes being hot-spots for SA-GrBd-effects is competition among siblings . In this case , any X- or Y-coded phenotype that differentially influences the competitive ability of the two sexes of offspring can cause a SA-GrBd-effect in three ways ( Figure 3 ) : The same logic applies to maternal SA-GrBd-effects in the context of ZW sex determination , but the opportunity for the epigenetic modification by the mother of an offspring's gene expression may be more substantial owing to her multifarious influences on the developing egg ( e . g . , deposition of steroid hormones in the yolk and RNAs in the egg's cytoplasm ) . The logic of SA-zygotic drive is an extension of the concepts of meiotic and gametic drive that operates postzygotically during ontogeny rather than prezygotically during meiosis and gametogenesis . As a consequence , many of the evolutionary principles developed for meiotic drive in classic papers by Sandler and Novitski ( 1957 ) [8] , Hiraizumi et al . ( 1960 ) [9] , Hamilton ( 1967 ) [10] , Hartl ( 1975 ) [11] , and others will also apply to SA-zygotic drive . However , we will show in this paper that the postzygotic operation of SA-zygotic drive ( unlike the prezygotic process of meiotic drive ) has a unique mode of operation that creates unprecedented , broad-scale opportunity for green-beard effects to evolve . These SA-GrBd-effects are predicted to be capable of causing a wide diversity of maladaptive phenotypes that are expressed in the diploid phase of the lifecycle . Previous theoretical work from our laboratories has shown that linkage to the W and Z chromosomes in species with female heterogamety facilitates the evolution of selfish genetic elements that code for heritable maternal effects [12] . Here we focus predominantly on X- and Y-coded green-beard effects that evolve due to paternal epigenetic effects , parental investment ( PI ) by either heterogametic sex ( XY or ZW ) , and sibling-sibling interactions ( competitive sib-sib-interactions ) . In the following sections we first evaluate the biological feasibility of the evolution of SA-zygotic drive of the sex chromosomes via SA-GrBd-effects , and how the autosomes would be expected to respond to such evolution . We focus especially on the feasibility of paternal epigenetic effects , because of the constraints imposed on their transmission between father and offspring via the sperm . We next develop a mathematical model of SA-zygotic drive due to coevolution between X and Y coded SA-GrBd-effects . Before discussing our collective findings , we describe how SA-zygotic drive can provides a new genetic hypothesis for the evolution of enigmatic traits , like high-frequencies of spontaneous abortion , sterility , and homosexuality , that reduce Darwinian fitness .
Consider the expression of the paternal X and Y chromosomes during spermatogenesis at a time when the developing gametes remain functionally diploid , i . e . , before the primary spermatogonial cell has divided into haploid spermatids , and also while the four developing spermatids derived from each spermatagonial cell remain connected by cytoplasmic bridges that permit RNA , steroid hormones , proteins and other molecules to be exchanged ( i . e . , most of spermatogenesis; [13] ) . With sib-competition , any X-coded epigenetic modification that influences gene expression in sons , and thereby reduces their competitive ability , would be favored by genic selection . An X-linked mutation producing such a paternal epigenetic effect represents a SA-GrBd-effect between a father and his offspring because it differentially helps those offspring that carry the mutation . For example , consider an X-coded mutation that was expressed during spermatogenesis and that epigenetically modified the expression of an autosomal gene ( in the zygote or developing embryo of the next generation ) in a manner that disrupted a male-specific ontogenetic pathway ( such as dosage compensation in Drosophila melanogaster ) and thereby reduced the competitive ability of sons during sib-competition . In this case , the green-beard ‘tag’ is the presence or absence of the male-specific ontogenetic pathway , the ‘tag-differentiation’ is the epigenetic modification of the expression of a gene in a male-specific ontogenetic pathway that harms only ( or disproportionately ) sons , and the ‘tag-directed-aid’ is the resulting increased competitive ability of daughters competing with debilitated brothers . When there is sib-competition , an X-coded green-beard effect that aids ( harms ) one sex of offspring necessarily harms ( aids ) the other sex – and hence such green-beard effects are necessarily sexually antagonistic . The same logic applies to Y-coded paternal epigenetic effects that help sons by harming daughters . For example , consider a Y-coded epigenetic effect that caused mis-expression of any gene located on the paternally inherited X chromosome . This phenotype would debilitate only daughters and thereby increase the fitness of the Y chromosome when there is sib-competition . Although the Y chromosome in many species may currently contain relatively few structural genes [14] , this would not have been true historically before degeneration of the Y occurred . Furthermore , a highly degenerated Y chromosome with respect to structural genes may retain substantial regulatory potential as recently shown for D . melanogaster [15] . With male heterogamety , sexually antagonistic epigenetic effects must operate through the sperm , which provides far more formidable barriers to expression of paternal effects compared to that of maternal effects through the egg [16]: sperm are much smaller than eggs , nearly all paternal cytoplasm is stripped away during spermatogenesis , paternal imprinting via histone modification is restricted due to protamines replacing paternal histones , and paternal imprinting via methylation is made difficult due to the nearly global demethylation of the paternal chromosomes after fertilization , as occurs in mammals . Nonetheless , a large body of extant evidence indicates that sexually antagonistic paternal ( and maternal ) effects can and do operate in nature , as described below . Most research on paternal epigenetic effects in animals has focused on methylation-based imprinting in mammals . This process , however , is unlikely to contribute substantially to SA-GrBd-effects coded by the sex chromosomes because it operates through cis-acting imprinting control regions ( ICRs , which are associated with relatively small proportion of genes ) [17] . In contrast , the X and Y are selected to produce trans-acting gene products that epigenetically modify the expression of other parts of the genome in offspring that do not carry the coding sex chromosome . In Text S1 , we summarize extant studies to provide evidence that: i ) epigenetic maternal and paternal effects have evolved many times that selectively kill offspring that do not carry them , ii ) mutations that cause antagonistic parental effects that selectively harm only one sex of offspring are well documented , at least in D . melanogaster in the context of maternal effects , iii ) the expression levels of hundreds of genes in D . melanogaster are influenced by both maternal and paternal effects , with no evidence that this phenomenon is caused by imprinting-based parent-of origin effects iv ) trans-acting epigenetic paternal effects ( that are not parent-of-origin effects , and that influence offspring that do not carry the coding gene ) can be produced by RNAs produced during spermatogenesis and transferred to the zygote ( as RNA or cDNA ) , and v ) epigenetic maternal effects that influence the competitive ability of one sex of offspring over the other can be produced by varying steroid levels in the yolk . Collectively these studies provide evidence that X and Y-coded ( and Z and W-coded ) SA-GrBd-effects can feasibly evolve through both paternal and maternal effects . Here , we briefly overview some examples of the material covered in Text S1 . Antagonistic maternal effects are well documented . In mice ( HSR , scat+ , OmDDK ) and beetles ( Medea factors ) , there are polymorphic alleles in natural populations that produce maternal effects that kill all of the siblings in a brood that do not carry them ( reviewed in [1] ) . In D . melanogaster , there are at least three established loci that can mutate to alleles that kill sons via a maternal effect ( snl , sok-1 , and sok-2 ) and three that similarly kill only daughters ( l ( 2 ) mat , da , and Ne ) [18] . In birds , a maternal effect ( elevated yolk androgen concentrations in the barn swallow , Hirundo rustica ) causes enhanced growth rate of sons but reduced growth rate of daughters [19] . Trans-generational epigenetic paternal effects are also well documented . In Caenorhabditis elegans , a pair of tightly linked genes ( peel-1 and zeel-1 ) code for a paternal effect that kills offspring that do not carry them [20] . In mice , a trans-generational epigenetic paternal effect , coded by an allele at the Kit locus , has been demonstrated to be mediated by RNAs produced during spermatogenesis and transmitted to the egg [21] . Human sperm transfer over 4 , 000 different types of RNA transcripts to the egg , including at least 68 miRNAs [16] . These studies demonstrate that mutations causing the phenotypes needed for SA-zygotic drive to operate do in fact occur . Past evolution of antagonistic X- and Y-coded SA-GrBd-effects should have selected for adaptations by the affected sex chromosome to suppress them , and by the autosomes to suppress them whenever they harm one sex of offspring more than they help the other sex . A candidate phenotype for such suppression is the enigmatic early-inactivation of sex chromosomes ( but not the autosomes ) during the process of spermatogenesis . This is a well documented phenomenon in organisms as diverse as fruit flies , worms and mammals , but its adaptive significance is poorly understood [22] , [23] . All chromosomes are inactivated during the latter stages of spermatogenesis when the sperm's DNA becomes highly condensed . However , the X and Y chromosomes are inactivated far in advance of the autosomes , during the early stages of spermatogenesis [24] , [25] . Although the selective factors that led to the evolution of the early-inactivation of the sex chromosomes are unknown , the phenomenon is consistent with what would be expected if X and Y-coded SA-GrBd-effects have been important historically . If early-inactivation of the X and Y reduced the production of RNAs coded by these chromosomes during spermatogenesis , this would interfere with RNA-based epigenetic modification of genes in the developing sperm as well as the embryo ( see Text S1 ) . It may also protect these chromosomes from SA-GrBd-effects coded by the other sex chromosome by restricting access of gene products that modify chromatin structure ( e . g . , acetylation of histones ) . Early inactivation , however , does not completely preclude X and Y-coded SA-GrBd-effects from occurring . Recent studies indicate that ∼10% of genes on the X remain active throughout spermatogenesis in mice , and that some early inactivated X-linked genes regain activity during the latter stages of spermatogenesis [25] . Lastly , although early inactivation of the sex chromosomes might feasibly have evolved as a defense against SA-zygotic drive , meiotic drive of the sex chromosomes [26] and sex-linked sexually antagonistic alleles [23] would also select for this phenotype . In sum , there is manifest evidence that sex chromosomes have the potential to evolve to code for SA-GrBd-effects that are mediated by parental epigenetic effects . Although the potential for such effects is greater through the egg in the case of female heterogamety , there is also substantial evidence that epigenetic paternal effects through the sperm also may be an important source of SA-GrBd-effects ( Text S1 ) . Antagonistic X and Y-coded SA-GrBd-effects may have been especially prominent during the initial stages of sex chromosome evolution , before early-inactivation of the sex chromosomes during spermatogenesis had evolved . Parental investment ( PI ) in offspring can be elicited by specific signals from the offspring , such as vocalizations , begging behavior , or markings such as those associated with the gaping mouth of soliciting offspring [27] . Consider an X-linked mutation that causes a father to i ) respond to a daughter-specific trait in a manner that increased PI , or ii ) respond to a son-specific trait that in a manner that reduced PI . Such a mutation would be favored by genic selection because it would increase the probability of its own propagation even if the net fitness of the father declined owing to the reduction in the fitness of his sons [2] , [3] . The same logic applies to a Y-linked mutation that increased PI allocated to sons at the expense of daughters . The potential for such sex-specific allocation of PI is illustrated by the barn swallow ( Hirundo rustica ) , in which the begging vocalizations are distinct between sons and daughters [28] , and the American kestrel ( Falco sparverius ) , in which the male and female nestlings have markedly different plumage [29] . In red deer , females permit sons to suckle longer and more frequently compared to daughters [30] , and such sex-specific discrepancies in parental investment are well documented across a wide diversity of taxa [31] . Both solicitation displays by offspring and response to them by parents have been shown to have measurable heritability across a wide diversity of taxa , and solicitation displays are known to be influenced by maternal effects [32] . Collectively these observations indicate that there is substantial evolutionary scope for sex chromosome-coded genes to evolve that cause parents to preferentially invest in one sex of offspring at the expense of the other sex , and hence to code for SA-GrBd-effects . The logic for sex-linked SA-GrBd-effects that are mediated by competitive sib-sib-interactions is similar to that described above for parental investment ( PI ) . The Y is selected to promote the competitive ability of brothers , the paternal X is selected to promote the competitive ability of sisters , and the maternal X and autosomes are selected to promote the survival of the brood as a whole . In other words , these chromosomes are selected in offspring in the same way that they are selected in their parents . There is a large body of empirical evidence indicating that siblings interact differently with each other in response to the sex of the interacting partners ( e . g . , [33] , [34] , so the requisite phenotypic variation is well established for the evolution of sex-linked SA-GrBd-effects that are mediated by competitive sib-sib-interactions . Evidence that SA-GrBd-effects have actually evolved would be established by showing that there are Y-linked genes that cause males to augment the survival of brothers at the expense of sisters , and vice versa for X-linked genes . To illustrate how easily SA-GrBd-effects could evolve via competitive sib-sib-interactions consider facultative siblicide ( i . e . , siblings are killed by other siblings in some , but not all , broods ) , which occurs in many species of birds , and some mammals [33] , [35] . If an X-linked gene caused its bearer to be less stimulated to kill a sister compared to a brother ( because sister-specific traits were less stimulating in inducing siblicide compared to brother-specific traits ) , an antagonistic green-beard effect would be manifest . As another example , cannibalism is common in a wide diversity of species during juvenile development [36] , [37] . If an X-lined gene caused females to be less likely to cannibalize their sisters and/or more likely to cannibalize their brothers , such a gene would necessarily produce a SA-GrBd-effect . The same logic applies to Y-coded genes that favor brothers over sisters . More generally , any gene located on the sex chromosomes that caused a sibling to be more , or less , stimulated to be aggressive or altruistic in response to sex-specific traits of competing siblings can feasibly lead to a SA-green-beard effect . The accumulation of X- and Y-coded SA-GrBd-effects will sometimes lead to selection pressure on the autosomes to evolve counter-measures that rescue the affected sex from the antagonistic paternal effects . If an X- or Y-coded paternal effect increases the fitness of one sex of offspring more than it harms the other sex , then the autosomes receive a net benefit and they are not selected to block the antagonistic paternal effect . Selection to block Y- and X-coded antagonistic paternal effects will occur , however , whenever they reduce the average fitness of a brood ( across both sexes ) , and hence reduce the fitness of the autosomes . However , unlike the strong selection on the X and Y to produce , and protect themselves from , sexually antagonistic paternal effects , selection on the autosomes to block them is relatively weak . To illustrate why , consider a new Y-linked mutation coding for a paternal effect that reduces the vigor of daughters and thereby increased the juvenile competitive ability of sons . Let the fitness gain to sons ( or the Y ) be a positive increment ( sson ) and the fitness loss to daughters ( or the X ) be a negative increment ( sdaughter ) . The fitness effect on the autosomes is the average of sson and sdaughter . Since one s-value is positive and the other negative , they tend to be counterbalancing , so that selection on the autosomes to block harmful paternal effects is closer to zero than selection on either the X or the Y to produce them . Hence selection on the autosomes to block antagonistic paternal effects coded by the sex chromosomes is absent , when they increase the average fitness of a brood , or relatively weak , unless they were to lead to a strong , population-wide imbalance in the sex ratio ( see [38] for constraints on selection in response to a biased sex ratio ) . Nonetheless , there is a large number of autosomal loci that may be capable of mutating to modifiers that shut down SA-zygotic drive . As a consequence , more extreme forms of SA-zygotic drive ( that reduce net brood fitness ) may be eventually silenced by counter-evolution on the autosomes , or to operate episodically when new forms of SA-zygotic drive evolve that are resistant to extant autosomal modifiers ( see for example [39] and references in [1] , chapter 3 ) . The same logic applies to sex-linked SA-GrBd-effects mediated by PI and competitive sib-sib-interactions . If a SA-GrBd-effect evolved that was coded by the Y and that favored sons at the expense of daughters , there would be counter-selection on the X to ameliorate this effect , and vice versa if a SA-GrBd-effect evolved that was coded by the X favoring daughters . Such selection and counter-selection could potentially lead to a genetic arms race ( Figure 4 ) with the autosomes being selected to block X- and Y-coded antagonistic paternal effects only when the net fitness of the brood was reduced . Here we explore the fate of mutations located on the X and Y chromosome that code for i ) paternal investment ( PI ) that is skewed toward the sex of offspring that carries them , ii ) epigenetic paternal effects that interfere with the ontogeny of the sex of offspring that do not carry them ( and thereby reduce their competitive ability during sibling competition ) , and iii ) competitive sib-sib-interactions that reduce the competitive ability of the sex that does not carry them ( by helping same sex siblings or harming opposite sex siblings ) . We specifically model coevolution between the X and Y . Because the X and Y do not recombine with each other , we model them as alleles at a simple Mendelian locus that determines sex ( XY is male , XX is female ) and pleiotropically influences epigenetic parental effects , paternal PI , or competitive sib-sib-interactions . This simplification ignores the recombination that is possible between X chromosomes in females that will lead to reduced Hill-Robertson interference on the X compared to the Y . As a consequence , our model will somewhat underestimate the rate of adaptive evolution of the X . Our model also ignores any counter-evolution by the autosomes , but this simplification should not change our qualitative conclusions owing to the expected weaker selection on the autosomes ( see above section ) . We start by formulating a model of differential paternal investment in sons and daughters which we then study numerically . At the end of this section , we show that a similar approach can be used , and similar conclusions apply , for epigenetic parental effects and competitive sib-sib-interactions that harm the sex that does not carry them . SA-zygotic drive provides a previously unexplored genetic model for the evolution of traits , such as sterility and homosexuality , which reduce Darwinian fitness , but yet can attain appreciable frequency in natural populations . We illustrate the heuristic potential of the concept of SA-zygotic drive by applying this genetic model to the unusual distribution of female homosexuality in human pedigrees ( Figure 6 , drawn from the data presented in Table 6 of [41] ) . We do not claim that this phenotype represents an established example of SA-zygotic drive , only that SA-zygotic drive provides a new functional form of hypothesis that can be tested to account for this – and other enigmatic – phenotypes that presently have no other genetic explanation . Relative to a proband ( i . e . , a focal homosexual female ) , female homosexuality was observed at rates elevated above the background level on the paternal but not the maternal side of the family , and here only among the daughters of the fathers' brothers . A proband's sisters also had elevated rates of homosexuality . There was also some indication that probands' daughters may have had elevated levels of homosexuality , but the number of daughters assayed was small , and their elevated rate of homosexuality was not statistically significant when high stringency in identifying homosexual probands was applied . The major pattern of female homosexuality in the pedigrees was that its occurrence was elevated only in relatives ( sisters and paternal female cousins ) whose fathers shared the same Y chromosome , and many of the same X-linked alleles . The observation that paternal aunts did not show elevated rates of homosexuality indicates that it was the X/Y combination of the father , rather than the Y alone , that was associated with an increased probability of female homosexuality . The weaker evidence for elevated rates of homosexuality in probands' daughters is also consistent with an epigenetic effect of the sex chromosomes since paternal epigenetic effects are know to sometimes carry-over to more than one generation ( e . g . , see description of the Kit-locus in Text S1 ) . The association of female homosexuality with only the patriline is consistent with the operation of SA-zygotic drive , yet we are aware of no previously available genetic model that predicts this association [42] . Male homosexuality has been found to be associated with the matriline , at least in some ethnic groups ( e . g . , [43] , but see [44] ) and more recent evidence indicates that it may be caused , in part , by sexually antagonistic alleles [45] . SA-zygotic drive provides a testable hypothesis for the association of female homosexuality with a different form of genomic conflict: SA-GrBd-effects . We see no rationale for why the Y would directly be selected to cause female homosexuality . Nonetheless , the Y is selected to epigenetically disrupt daughter-specific developmental pathways that influence their vigor . These effects could feasibly influence female sexual development outside the context of vigor through pleiotropy and lead to female homosexuality , despite there being no direct selection for this specific phenotype . SA-zygotic drive is also predicted to influence other enigmatic fitness-reducing traits that are controlled by sex-specific processes , like the high levels in humans of both sterility ( e . g . , ∼10% of couples are infertile , with males accounting for 30–50% of this value [46] ) and spontaneous abortion ( e . g . , ∼70% of human conceptions spontaneously abort , [47] , most of which are not due to aneuploidy [48] ) . The logic in these cases is identical to that described above for female homosexuality , but in this case the disrupted sex-specific developmental pathways lead to sterility and inviability of embryos rather than homosexuality . These examples illustrate how SA-zygotic drive provides a new theoretical framework that can be used to construct a more complete set of alternative genetic hypotheses when evaluating the evolution of traits that reduce Darwinian fitness .
Our theory of SA-zygotic drive can be used to generate testable predictions . The major – and counterintuitive – prediction concerning SA-zygotic drive is that a father's Y chromosome will be observed to sometimes strongly influence the fitness of his daughters and his X will similarly influence his sons . In the case of female heterogamety , analogous predictions apply to the W and Z chromosomes . The empirical work described above ( e . g . , the Kittm1alf mutation in mice [21] and the sex-specific maternal effect mutations in D . melanogaster [18] and birds e . g . , [19] proves that these types of effects can feasibly evolve ( see Text S1 ) . It has also been established in inbred strains of mice that a father's Y chromosome can influence the behavior [64] and immune function [65] of his daughters . Our theoretical study provides a motivation for researchers to screen in future studies for an influence of the X and Y ( and W and Z ) on the sex of offspring that does not carry them . A second prediction is that heritable paternal effects on offspring fitness should be found to be more common , and larger in magnitude , in species with male heterogamety , and within this group this pattern should be strengthened as the degree of monandry and sib-sib interactions increase . The absence of strong paternal effects in species lacking male parental care is commonly assumed in studies of quantitative genetics . Our theoretical work , however , predicts that this assumption will sometimes be violated due to polymorphism ( sex linked or autosomal ) influencing the expression of paternal SA-GrBd-effects . A taxonomic prediction is that SA-zygotic drive should be especially prevalent in birds . This taxon has unusually high levels of monogamy ( within a breeding season and despite low levels of extra-pair fertilizations , [66] , an absence of inactivation of the W and Z sex chromosomes during oogenesis [23] , and high levels of parental care and sib-sib interactions . Birds also have female heterogamety which facilitates parental epigenetic effects through the mother's large contribution to the embryo of RNAs and steroid hormones . The combination of these characteristics makes birds an ideal taxon to test for the existence of SA-zygotic drive . The main prediction concerning competitive sib-sib-interactions is that , in species with sex chromosomes , same-sex sibling interactions should be more altruistic and less aggressive compared to between-sex interactions ( excluding species with other factors magnifying same-sex sib competition , such as those with local mate competition or early dispersion of only one sex of offspring ) . A similar prediction has been made earlier by several other researchers ( reviewed in [67] ) based on the idea that X and Z sex chromosomes segregate the same way that haploid genomes do in species with haplodiploid sex determination . In haplodipoid species , full sisters are more closely related to each other ( R = proportion of shared polymorphic alleles = ¾ ) than to brothers ( R = 1/4 ) , and more closely related than bothers are to each other ( R = ½ ) . As a consequence , sister-sister interactions are predicted to be the more cooperative . Assuming that the heteromorphic sex chromosome ( Y or W ) is too degenerate to code substantially for cooperation , the X and Z have the same relationship in brothers and sisters as whole genomes do in haplodiploids , and hence X and Z-linked genes are predicted to evolve to make members of the homogametic sex to be more cooperative with each other . There is some support for this prediction based on taxonomic comparisons . For example , long-term cooperative groups are more common among brothers in birds and sisters in mammals [68] . However , we have found no relevant information ( pro or con ) in the literature concerning the more specific prediction of SA-zygotic drive that during sib-competition opposite-sex individuals will be more competitive with each other compared to same-sex individuals . We suspect , however , that this information may have been collected incidentally in many studies of animal behavior – but unreported . Our study should provide an impetus to publish such comparisons . The main prediction concerning PI is that , all else being equal , asymmetry in its allocation to sons and daughters should be higher , and sometimes more variable , in the heterogametic compared to the homogametic parent . The ‘all else being equal’ qualifier is important here because in taxa like birds males may vary in PI more than females owing to varying uncertainty in paternity . We have been unable to find any studies reporting this metric ( so we have found neither positive nor negative evidence ) , but again we suspect that it may have been collected incidentally but unreported in past studies of animal behavior . Lastly , when there is sib-competition , sexual dimorphism of offspring is predicted to be reduced in species with sex chromosomes , and within this group , lower yet when there is PI from the heterogametic parent . To illustrate the rationale for this prediction , suppose that an X-coded paternal effect evolved that caused fathers to increased PI in response to a daughter-specific trait , or reduce PI in response to a son-specific trait . Sons would be selected to converge in phenotype with their sisters , leading to the evolution of reduced sexual dimorphism during the period of sib-competition . Nonrecombining sex chromosomes create an unappreciated opportunity for the evolution of zygotic drive via sexually antagonistic green-beard effects whenever there is competition among siblings . The evolutionary scope for SA-zygotic drive is predicted to exceed that of meiotic , gestational , and autosomal-zygotic drive because all sexually dimorphic traits can acts as “tags” for sexually antagonistic green-beard effects . These sexually antagonistic phenotypes can , in principle , lead to an arms race between the two types of sex chromosomes ( sometimes also including the autosomes , which can slow , and temporarily or permanently halt , the process ) that can i ) accelerate the degeneration of the heteromorphic sex chromosome , ii ) cause genes that would otherwise be highly conserved to diverge among allopatric lineages and thereby leading to the evolution of Dobzhansky-Muller incompatibilities during speciation , and iii ) lead to the disruption of sex-specific ontogenetic pathways that can lead to increased levels of expression of traits , like homosexuality and sterility , that lower Darwinian fitness . We need to stress in closing , however , that we have only established the potential for SA-zygotic drive to operate in nature and it will remain a feasible but unproven possibility until suitable empirical testing has been undertaken .
For simplicity we assume that each mating results in two offspring . Let the parameter bson characterize the bias in paternal investment toward sons in families with one daughter and one son , with bson = 0 , bson>0 , and bson<0 implying equal investment in both offspring , higher investment in the son and higher investment in the daughter , respectively . Let x and y be the ( additive ) effects of X- and Y-linked genes in the father on the bias of his paternal investment . More specifically , we let bson = y−x and bdaughter = x−y = −bson , so that X-linked genes favored by selection ( that increase x ) cause the father to invest more in his daughter while Y-linked genes favored by selection ( that increase y ) cause him to invest more in his son . We assume that the fitness of a brother and a sister in a brother-sister brood are w ( bson ) and w ( bdaughter ) = w ( −bson ) , respectively , where w ( . ) is a symmetric function changing from 0 to 1 as bson changes from −∞ to +∞ with w ( 0 ) = 0 . 5 and w ( bson ) +w ( bdaughter ) = 1 ( see below ) . Interpreting fitness as the amount of a resource available , the latter two equalities imply that the overall amount of resource is fixed ( at 1 ) and that with no bias ( i . e . if bson = bdaughter = 0 ) , both sex of offspring get an equal share ( equal to 0 . 5 ) . The symmetry of this relationship is motivated by the idea that an extra unit of PI given to one sex of offspring is taken away from the other sex of offspring , and this implicitly assumes that the benefit of an extra unit of PI is equal to the cost of losing a unit of PI . Finally , we assume that fitness of each offspring in the families with the same-sex of offspring is equal to 0 . 5 . Under these conditions , the average fitness of sons and daughters of fathers with effects ( x , y ) are ( 1 ) The average fitness of sons and daughters given by eq . 1 are both limited to the interval [0 . 25 , 0 . 75] . The evolutionary dynamics in this model were analyzed by using stochastic , individual-based simulations that allowed for the effects of random genetic drift , mutation , and selection . Generations were discrete and non-overlapping and the population size was fixed at N males and N females . Individuals entered the mating pool with probabilities proportional to wson and wdaughter for males and females , respectively , and mating was random within the mating pool . The number of matings ( and families produced ) per individual of each sex was a binomial random variable . Mutation occurred in both parents with probability μ per chromosome per generation and changed effects x or y by a random value taken from a normal distribution with a mean of zero and a standard deviation of one . The fitness function w ( . ) was specified as: where α>0 is a parameter measuring the strength of selection ( larger values of α imply stronger selection; see Figure 7 ) . We assumed that initially there was no genetic variation and the x and y effects of all individuals were set to zero . We varied the mutation rate μ and the strength of selection α while the number of individuals of each sex was always set at N = 1000 . For each parameter combination , we did 20 runs each for 10000 generations . Overall , the dynamics are expected to be very similar to those observed in models of sexual conflict over mating rate [69]–[71] . | Our study describes a new form of sexual genomic conflict that operates through the process of antagonistic green-beard effects . Although past theoretical and empirical work indicated that green-beard effects rarely operate in nature , our new theory shows why this conclusion may have to be reevaluated . We integrate modeling analysis with extant empirical work to show that the unique properties of sex chromosomes can lead to a previously unappreciated form of sexual conflict ( sexually antagonistic zygotic drive ) that may be widespread in nature . It operates through harmful epigenetic parental effects , asymmetrical allocation of parental investment to sons and daughters , and asymmetrical interactions between brothers and sisters . Sexually antagonistic zygotic drive is functionally analogous to meiotic drive except that it operates due to competition among opposite-sex siblings rather than between competing gametes . | [
"Abstract",
"Introduction",
"Results",
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"Models"
] | [
"genetics",
"and",
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] | 2008 | Sexually Antagonistic “Zygotic Drive” of the Sex Chromosomes |
Obligate intracellular pathogens satisfy their nutrient requirements by coupling to host metabolic processes , often modulating these pathways to facilitate access to key metabolites . Such metabolic dependencies represent potential targets for pathogen control , but remain largely uncharacterized for the intracellular protozoan parasite and causative agent of Chagas disease , Trypanosoma cruzi . Perturbations in host central carbon and energy metabolism have been reported in mammalian T . cruzi infection , with no information regarding the impact of host metabolic changes on the intracellular amastigote life stage . Here , we performed cell-based studies to elucidate the interplay between infection with intracellular T . cruzi amastigotes and host cellular energy metabolism . T . cruzi infection of non-phagocytic cells was characterized by increased glucose uptake into infected cells and increased mitochondrial respiration and mitochondrial biogenesis . While intracellular amastigote growth was unaffected by decreased host respiratory capacity , restriction of extracellular glucose impaired amastigote proliferation and sensitized parasites to further growth inhibition by 2-deoxyglucose . These observations led us to consider whether intracellular T . cruzi amastigotes utilize glucose directly as a substrate to fuel metabolism . Consistent with this prediction , isolated T . cruzi amastigotes transport extracellular glucose with kinetics similar to trypomastigotes , with subsequent metabolism as demonstrated in 13C-glucose labeling and substrate utilization assays . Metabolic labeling of T . cruzi-infected cells further demonstrated the ability of intracellular parasites to access host hexose pools in situ . These findings are consistent with a model in which intracellular T . cruzi amastigotes capitalize on the host metabolic response to parasite infection , including the increase in glucose uptake , to fuel their own metabolism and replication in the host cytosol . Our findings enrich current views regarding available carbon sources for intracellular T . cruzi amastigotes and underscore the metabolic flexibility of this pathogen , a feature predicted to underlie successful colonization of tissues with distinct metabolic profiles in the mammalian host .
Chagas disease is a vector-borne parasitic disease caused by the kinetoplastid protozoan parasite Trypanosoma cruzi . Acute T . cruzi infection is most often asymptomatic or characterized by flu-like symptoms , but can cause severe and fatal myocarditis in the first weeks following infection [1] . More typically , parasites establish chronic infection that is controlled , but not eliminated , by host immune mechanisms [2] . A subset of chronically infected individuals develop progressive disease characterized by serious cardiac and gastrointestinal disturbances [3] , for which treatment options are limited [4] . T . cruzi exhibits a broad mammalian host range where it can colonize diverse tissue types [5 , 6] . In the chronic stage of infection , when parasites are maintained at very low densities , persistence has been reported most often in cardiac muscle , gastrointestinal smooth muscle and adipose tissue [6–9] . Knowledge of the molecular mechanisms governing successful intracellular colonization , replication and long-term persistence by T . cruzi are currently lacking but represent potentially exploitable processes for the development of new therapeutics . In mammalian hosts , T . cruzi transitions between two main developmental forms . The non-dividing , motile trypomastigote can actively penetrate most nucleated cell types by exploiting the host cell plasma membrane repair process [10] . Once inside the host cell , the parasite sheds its temporary vacuole [11] and progresses through a developmental program that culminates in the formation of the morphologically and biochemically distinct amastigote form that replicates in the host cell cytosol . Transcriptomic profiling of this developmental transition revealed strong signatures of global metabolic reprogramming in the parasite as it transforms from the trypomastigote to the amastigote stage [12] . As an obligate intracellular parasite , T . cruzi amastigotes are forced to draw from host nutrient pools to fuel their growth and survival , although nutrient uptake by amastigotes in situ has not been directly demonstrated . On the basis of expression data [12–14] , functional studies [15] , and metabolic assays conducted with isolated amastigotes [16 , 17] , it has been proposed that amino acids and fatty acids are the most likely sources of carbon for T . cruzi amastigotes to fuel their metabolism . Hexose sugars have largely been discounted as a potential carbon source for this cytosolic pathogen [13 , 15 , 18] , due to the perception that glucose is a negligible commodity in the interior of a mammalian host cell [18] and the failure to demonstrate hexose transporter expression or uptake in isolated T . cruzi amastigotes [13 , 15] . Nevertheless , the ability of T . cruzi to colonize a wide range of cell and tissue types predicts a degree of metabolic flexibility and/or the potential for the parasite to reprogram host metabolic pathways to suit its specific metabolic requirements as reported with some viral and bacterial pathogens [19–21] . Metabolic abnormalities have been reported in chronic Chagas patients [22–24] and in animal models of acute and chronic T . cruzi infection [25–29] . These include dysregulation of glucose [23 , 28] and lipid metabolism [29] as well as mitochondrial electron transport chain dysfunction [26 , 27 , 30] in T . cruzi-infected skeletal [31] and cardiac muscle [25] . Recently , metabolite profiling studies have revealed increased uptake and metabolism of glucose in T . cruzi-infected cardiac muscle [32] . At the cellular level , transcriptomic analyses reveal modulation of host metabolic pathway expression including upregulation of metabolite transporters in T . cruzi-infected fibroblasts [12 , 33] . How such metabolic changes in the host impact the intracellular T . cruzi amastigote life cycle have not been determined . However , results of genome-scale functional studies predict that the immediate metabolic environment can influence intracellular parasite growth [34] . In the present study , we sought to determine how T . cruzi infection impacts host glucose metabolism and mitochondrial respiration at the cellular level and how parasite-triggered changes in host cellular metabolism influence the intracellular infection cycle .
Metabolite profiling of T . cruzi-infected hearts has provided evidence of increased glucose uptake and metabolism at the whole organ level [32] . Here , we examined the impact of T . cruzi infection on glucose metabolism at the cellular level using low passage normal human dermal fibroblasts ( NHDF ) , which have been shown to increase host glucose transporter expression during T . cruzi infection [12 , 33] . Following infection ( 48 hpi ) we observed a significant increase in 2-deoxyglucose ( [3H]-2-DG ) uptake into infected fibroblast monolayers in a manner that correlated with increasing parasite load ( Fig 1A ) under conditions where host cell abundance remained unchanged ( S1A Fig ) . [3H]-2-DG uptake by both uninfected and infected NHDF was blocked by cytochalasin B ( Fig 1B ) , consistent with a role for host plasma membrane glucose transporters [35] in mediating this host cell response to T . cruzi infection . Glucose uptake assays performed in parallel with fibroblasts ( Fig 1C ) and mouse skeletal myoblasts ( Fig 1D ) following infection with one of three T . cruzi strains: Tulahuén , CL Brener and CL-14 , revealed comparable results with increased glucose uptake occurring in parasite-infected host cells as a generalized response among the isolates and mammalian cells tested here . Glucose transport by mammalian cells is a highly regulated process [36] that is responsive to acute changes in the environment , including glucose restriction [37] , intracellular pathogen infection [19–21] , and acute exposure to PAMPs or cytokines [38 , 39] . Physiologic triggers leading to increased glucose uptake , including pathogen infection , frequently promote increased glycolytic rates and lactate production from pyruvate , as well as decreased flux through the TCA cycle with reduced respiratory rates [40–42] . Unlike these examples , we find no increase in lactate secretion to accompany increased glucose uptake into T . cruzi-infected host cells ( Fig 2A ) , but evidence of increased mitochondrial respiration in parasite-infected fibroblasts ( Fig 2B ) as determined by monitoring the oxygen consumption rate ( OCR ) in cell monolayers using a Seahorse extracellular flux analyzer . While the OCR measured in T . cruzi-infected monolayers was consistently greater than that measured in uninfected cell monolayers ( Fig 2B ) , the potential for parasite respiration to contribute to the total OCR signal complicated immediate interpretation of this result . To examine this further , we sought a method to specifically inhibit T . cruzi amastigote respiration in situ in order to reveal the host and parasite contributions to the total OCR signal . For this , we utilized the endochin-like quinolone ELQ300 , which targets cytochrome bc1 in the mitochondrial electron transport chain of apicomplexan parasites without affecting mammalian respiratory complexes [43 , 44] . To validate the utility of ELQ300 for our purpose , we performed preliminary experiments to demonstrate that respiration in isolated T . cruzi amastigotes was inhibited by ELQ300 in a dose-dependent manner . Treatment with 1 μM ELQ300 resulted in maximal inhibition ( 90% ) of basal OCR in isolated amastigotes ( S1B Fig ) with no impact on host mitochondrial respiration ( S1C Fig ) . To confirm that ELQ300 was effective in blocking T . cruzi amastigote respiration in situ , we exploited a mitochondrial complex III-deficient human fibroblast cell line ( CIII mutant ) [45 , 46] with significantly lower respiratory rates than normal human fibroblast control lines ( S1D Fig ) . Treatment of infected CIII mutant fibroblasts with 1 μM ELQ300 almost completely abrogated the increase in OCR due to infection ( S1E Fig ) , consistent with the compound inhibiting >90% of amastigote respiration in situ . In contrast , experiments performed in parallel with respiration-competent , control human fibroblasts ( S1F Fig ) show that following treatment with 1 μM ELQ300 to inhibit parasite respiration , a significant residual OCR signal remained , attributable to increased host cell mitochondrial respiration associated with T . cruzi infection . Similar results were obtained with T . cruzi-infected NHDF treated with 1 μM ELQ300 ( Fig 2C ) or with 1 μM GNF7686 ( S1G–S1I Fig ) , a compound for which T . cruzi cytochrome b is the validated target [47] . Therefore , through differential targeting of T . cruzi mitochondrial complex III using small molecule inhibitors , we were able to measure intracellular amastigote respiration in situ and to determine that host mitochondrial respiration increases as a result of T . cruzi infection . We further demonstrate that increases in host mitochondrial respiration are accompanied by increased host mitochondrial content specifically within the parasitized subpopulation of the infected cell monolayers ( Fig 2D and 2E ( GFP+ ) ; S2 Fig ) . To assess the potential for altered host glucose and mitochondrial metabolism to impact intracellular amastigote replication , flow cytometry-based proliferation assays were performed that enabled determination of the number of divisions that an individual amastigote has undergone in infected host cells within a set time frame , following exposure to different conditions . Examination of T . cruzi amastigote proliferation in complex III mutant fibroblasts , which display a significantly reduced mitochondrial respiratory capacity as compared to normal fibroblasts ( S1D Fig ) , reveals nearly identical proliferation profiles for amastigotes in CIII mutant or normal control fibroblasts ( Fig 3A ) . In contrast , amastigote proliferation was substantially reduced when glucose was removed from the extracellular medium , with the majority of amastigotes completing only 3 divisions within 48 hours rather than 4 divisions as when glucose was present ( Fig 3B ) . The inhibitory effect of glucose restriction on T . cruzi amastigote growth was greatly enhanced by the addition of the glucose analogue 2-deoxyglucose ( 2-DG ) ( Fig 3C ) , which inhibits glycolysis . Notably , the fibroblast host monolayer was not measurably impacted even in the absence of exogenous glucose , until concentrations >2 mM 2-DG were reached ( S3A Fig ) . In the absence of exogenous glucose , amastigote proliferation was arrested by the addition of 2 mM 2-DG ( Fig 3D ) , where the median number of parasites/infected host cell was 1 ( S3B Fig ) . However , the continued presence of viable amastigotes in infected monolayers at 66 hpi in cultures treated with 2 mM 2-DG in the absence of glucose ( Fig 3E ) suggests that this effect is cytostatic rather than lethal for the parasite . The near complete arrest of intracellular T . cruzi growth in the presence of 2-DG , as opposed to the more modest effect of glucose restriction alone ( Fig 3B and 3E ) , suggests that 2-DG may directly inhibit parasite glucose metabolism in addition to its inhibitory effect on host glycolysis . This implies that the parasite can access and internalize this glucose analog in situ , which counters the hypothesis that glucose is not accessible to the amastigote stage of T . cruzi [15 , 18] . To explore the relationship between T . cruzi amastigotes and exogenous glucose more closely , we first examined the capacity of amastigotes , isolated from NHDF monolayers at 48 hpi , to utilize exogenous glucose to drive glycolysis and mitochondrial respiration , employing glutamine as a positive control to fuel respiration [17] . Extracellular flux analysis revealed that isolated T . cruzi amastigotes respond to exogenous glucose with significant increases in OCR ( Fig 4A ) and extracellular acidification rate ( ECAR ) , which correlates with glycolytic activity , ( Fig 4B ) that were quenched by the injection of 2-DG . A similar increase in amastigote OCR was observed in response to glutamine , with little change in ECAR as expected ( Fig 4A and 4B ) . To ensure that exogenous glucose is metabolized by the isolated amastigotes , rather than triggering metabolic changes in the parasite through an independent mechanism , metabolite profiling was performed following incubation of isolated amastigotes in medium containing [13C]-U-glucose for 3 hours . As shown in Table 1 , 13C incorporation was detected in glycolytic and TCA cycle intermediates as well as pentose phosphate pathway intermediates and several amino acids , providing direct confirmation that T . cruzi amastigotes are capable of internalizing and metabolizing exogenous glucose in catabolic and anabolic processes . Next , we performed transport assays to measure the kinetics of [3H]-2-DG uptake by freshly isolated intracellular amastigotes and extracellular trypomastigotes , a life cycle stage of T . cruzi for which hexose transporter expression is abundant [12 , 15] . The initial rates of hexose transport ( V0 ) measured for isolated amastigotes and trypomastigotes were found to be comparable , with a similar KM ( 87 . 0 ± 21 . 7 vs . 81 . 2 ± 3 . 7 μM ) and Vmax ( 857 . 0 ± 76 vs . 666 . 5 ± 36 . 1 pmol 2-DG/mg protein/min ) ( Fig 4C ) . We then sought to determine whether the capacity for glucose uptake and metabolism by T . cruzi amastigotes is relevant in the context of an intracellular infection of mammalian cells . Infected NHDF monolayers ( 48 hpi ) were pulsed with [3H]-2-DG for 20 minutes in the presence and absence of cytochalasin B , which significantly impairs glucose transport in mammalian cells but not in T . cruzi [35 , 48] . Intracellular amastigotes purified from host cells showed incorporation of [3H] when mammalian glucose uptake was not inhibited with cytochalasin B ( Fig 4D ) . To establish that [3H]-2-DG was internalized by the intracellular parasites and not non-specifically bound to amastigote surfaces following disruption of infected cells , isolated amastigotes were treated with the pore-forming peptide antibiotic , alamethicin [49] to permeabilize the parasite membrane ( S4A Fig ) , which resulted in the release of >50% of the amastigote-associated label on average ( Fig 4E ) . Similar evidence of in situ [3H]-2-DG uptake by intracellular T . cruzi was observed when parasites were resident in mouse skeletal myoblasts ( S4B Fig ) . Additional examination of CL Brener ( S4C Fig ) and CL-14 ( S4D Fig ) strain amastigotes in fibroblasts provided further evidence for the internalization of [3H]-2-DG by intracellular T . cruzi amastigotes in situ . We further demonstrate that glucose , as the sole exogenous carbon source available to isolated amastigotes , is capable of sustaining ATP pools in the parasite over a 24-hour period , at levels similar to a mixture of glucose , glutamine and pyruvate ( Fig 4F ) . Combined , these data demonstrate the potential for glucose to be utilized by intracellular T . cruzi amastigotes as a fuel for parasite metabolism in situ .
We have identified changes in host cellular metabolism associated with intracellular T . cruzi infection which include increased glucose uptake by infected host cell monolayers , increased mitochondrial respiration , and evidence of increased mitochondrial content specific to parasitized host cells . While these metabolic perturbations likely reflect multiple complex origins including compensatory changes triggered by increased metabolic demands , our data indicate that the parasite benefits from the increased glucose transport observed by infected cells , where exogenous glucose levels impact the proliferation rate of intracellular T . cruzi amastigotes . The growth-promoting effect of extracellular glucose could arise if cytosolically-localized T . cruzi amastigotes access intracellular glucose pools directly or if host cell metabolic processes fueled by extracellular glucose indirectly modulate the ability of amastigotes to efficiently replicate . While we cannot rule out the latter possibility , we provide evidence that intracellular amastigotes from different T . cruzi strains are capable of internalizing and retaining the radiolabeled glucose analog , [3H]-2-DG , during their intracellular replicative cycle in the mammalian cell cytoplasm . By studying amastigotes in isolation we also definitively demonstrate their capacity to take up exogenous glucose and metabolize this carbon to fuel glycolysis and mitochondrial respiration . 13C-glucose tracer studies confirm results of bioenergetics studies and further demonstrate that exogenous glucose is also shuttled into anabolic pathways including the pentose phosphate pathway . Combined with the finding that in the absence of other exogenous carbon sources , glucose is capable of sustaining ATP levels in isolated T . cruzi amastigotes to a similar degree as a mixture of substrates , our data indicate the potential for glucose to serve as an important substrate for the intracellular life stages of T . cruzi . These data counter the view that glucose is unlikely to be utilized by intracellular T . cruzi parasites [18] . While glucose concentrations in the mammalian cell cytoplasm have previously been considered insufficient to support the growth of cytosolic pathogens , this argument is weakened with studies using fluorescent glucose sensors that demonstrate the existence of a significant and dynamic pool of cytosolic glucose in multiple human cell lines [50 , 51] . A more direct argument against glucose as a potential substrate for intracellular T . cruzi amastigotes is the report that hexose transporter expression , as well as the ability to transport exogenous glucose , is negligible in this life cycle stage of the parasite [15] . However , our demonstration that isolated T . cruzi amastigotes transport glucose with similar kinetics as the trypomastigote stage of the parasite suggest that amastigotes utilize a facilitated hexose transporter with similar properties , if not identical , to the transporters expressed by extracellular T . cruzi life stages [52] . We further confirmed hexose transporter expression in amastigotes from three different T . cruzi strains by quantitative RT-PCR , albeit at much lower levels than in trypomastigotes . Notably , CL-14 amastigotes had the lowest expression ( S5 Fig ) , but the capacity to take up glucose from the host cytosol was exhibited by each parasite strain . However , without targeted molecular studies , the role of hexose transporters in glucose acquisition by intracellular T . cruzi amastigotes versus possible alternative mechanisms such as fluid-phase endocytosis through the amastigote cytostome [53] remains unresolved . Consistent with the potential for mammalian cells to sense fuel imbalances that may be incurred with the acquisition of glucose and other carbons by resident intracellular parasites and to mount a compensatory response [37 , 41] , we find that parasitized cells have more mitochondria and increased basal respiration . Selective inhibition of T . cruzi amastigote respiration with small molecule inhibitors of parasite cytochrome bc1 , ELQ300 [43] and GNF7686 [47] , enabled us to distinguish between parasite and host respiration and demonstrate elevated respiratory rates of mammalian cells during infection . However , unlike the impact of glucose restriction and/or 2-DG on amastigote proliferation , reduced host cell respiration , as seen in the mitochondrial complex III-deficient lines , did not impact T . cruzi replication . Infection outcomes are also anticipated to be host cell type–and perhaps parasite strain–dependent , as increased mitochondrial respiration in T . cruzi-infected macrophages was previously shown to be associated with increased nitric oxide production and parasite clearance [54] , while our results show no association in non-phagocytic cells . Additional studies are needed to better understand the complex interplay between T . cruzi and host metabolism at the cellular , organ and organismal levels . How metabolic changes incurred at the cellular level impact regional and global metabolism in infected mammalian hosts [26 , 27 , 30–32 , 55] and vice versa and how these changes impact the pathophysiology of disease are critical questions for future investigation . In summary , we demonstrate that T . cruzi infection modulates host cell metabolism , stimulating glucose uptake into infected monolayers , which can be scavenged directly by intracellular amastigotes for utilization in energy generating and biosynthetic processes . Thus , in addition to amino acids and fatty acids predicted to constitute the main intracellular source of carbon for T . cruzi amastigotes [13 , 18] , we propose that glucose offers additional flexibility with respect to fuel utilization by these intracellular parasites . While the exact degree of T . cruzi amastigote metabolic plasticity has yet to be determined , a greater number of nutrient options is predicted to enhance the chances of parasite survival in different host tissues and under varying environmental conditions , including pharmacological inhibition of specific metabolic pathways .
Mammalian cell lines: mouse skeletal muscle myoblast ( C2C12; ATCC #CRL-1772 ) , African green monkey kidney epithelial ( LLcMK2; ATCC #CCL-7 ) and human dermal fibroblasts ( NHDF; ATCC #CRL-2522 and NHDF-Neo; Lonza , #CC-2509 ) were propagated in Dulbecco’s Modified Eagle Medium ( DMEM; Hyclone ) supplemented with 1 mM pyruvate , 25 mM glucose , 2 mM glutamine , 100 U/ml penicillin , 10 μg/ml streptomycin and 10% fetal bovine serum ( FBS ) ( D-10 ) at 37°C and 5% CO2 . Human patient dermal fibroblast lines were purchased from the Cell line and DNA Bank of Genetic Movement Disorders and Mitochondrial Diseases ( GMD-MDbank ) : Complex III mutant fibroblast harbor a mutation in subunit BCS1L of ETC complex III [45] ( CIII mutant; GMD-MDbank #F-MT2614 ) , Normal 1 fibroblast ( GMD-MDbank #F-CR2631; Normal 2 fibroblast ( GMD-MDbank #F-CR2571 ) were propagated in D-10 medium containing 50 μg/mL uridine ( Sigma-Aldrich ) at 37°C and 5% CO2 . Mammalian cell lines expressing mCherry targeted to the mitochondrial matrix were generated by retroviral transduction of NHDF and C2C12 with a construct containing the sequence encoding the first 25 amino acids of the mouse Cox8a protein fused to mCherry [58 , 59] . Briefly , 5 x 105 Phoenix-AMPHO packaging cells ( ATCC #CRL-3213 ) were plated in a 100 mm tissue culture dish and transfected the following day with 10 μg of the plasmid pLNCX2 containing the chimeric sequence ( kindly provided by C-H Lee , HSPH ) using TransIT-LT1 ( Mirus Bio ) per manufacturer’s protocol . Virion-containing medium obtained from Phoenix cell cultures 2 days post-transfection was passed through a 0 . 45 μm filter , and stored at -80°C . Mammalian cells were seeded 1 . 5 x 105 per well in a 6 well plate , and viron-containing medium with 4 μg/mL polybrene was added the following day . Transgenic fibroblasts were selected with 400 μg/mL G418 ( Sigma-Aldrich ) starting two days post-transduction and confirmed by microscopy and flow cytometry after 2 weeks . Trypanosoma cruzi Tulahuén strain parasites ( from M . Perrin , Tufts University ) were transfected with pROCK-GFP-NEO for constitutive expression of GFP from the tubulin locus [60] . Axenically-grown T . cruzi epimastigotes were transfected as described [61] with minor alterations . Briefly , 10 μg of linearized plasmid was transfected into 4 x 107 epimastigotes in 100 μL of Tb BSF-buffer using the U-33 program of an Amaxa Nucleofector II ( Lonza ) . Cells were subsequently transferred to 5 mL LIT and incubated at 27°C overnight , then cloned in 96-well plates . After 3 weeks , clones were screened by flow cytometry , and GFP expressing parasites were confirmed by microscopy and PCR amplification of a 655 bp GFP sequence ( forward: 5’-TTCACTGGAGTTGTCC-3’; reverse: 5’-AGTTCATCCATGCCAT-3’ ) and 772 bp Neor sequence ( forward: 5’-ATGGGATCGGCCATT-3’; reverse: 5’-TCAGAAGAACTCGTCAAG-3’ ) using Taq DNA polymerase ( GenScript ) per manufacturer protocol . To generate trypomastigotes , 1 mL of stationary phase epimastigotes was added to a T75 flask of confluent LLcMK2 cells . The medium was changed to fresh D2 every day , and after 1 week newly emerging trypomastigotes were collected and used to start new mammalian stage cultures . Constitutive GFP expression in the parasite population was confirmed by routine fluorescence microscopy ( Nikon TE-300 ) . NHDF were plated at 1 . 5 x 106 in T75 flasks and infected with multiplicity of infection ( MOI ) of 10 for 18–24 h . At 48 hpi , monolayers were scraped and amastigotes were released from disrupted host cells by syringe passage ( 281/2G needle; BD ) into the indicated ice-cold buffer . Amastigotes were purified from debris by passage through a PD-10 desalting column ( GE Healthcare Life Sciences ) , and fractions containing clean amastigotes were centrifuged at 4000 g for 10 minutes at 4°C to pellet amastigotes , which were resuspended in warm ( 37°C ) buffer as needed for indicated applications . NHDF were plated and infected as for mammalian glucose uptake assays ( described below ) . At 48 hpi genomic DNA was isolated using the DNeasy kit ( Qiagen ) and eluted in water . 1 μL of sample was combined with 10 μL of iTaq Universal SYBR Green Supermix ( Bio-Rad ) and 5 μM of each human TNF primer ( forward: 5’-TAAGATCCCTCGGACCCAGT-3’; reverse: 5’-GCAACAGCCGGAAATCTCAC-3’ ) in a 20 μL reaction , run as above , and analyzed using the default Standard Curve ( absolute quantitation ) settings of a StepOnePlus . All transport assays were performed using 1 , 2-3H ( N ) -2-deoxyglucose , ( [3H]-2-DG; PerkinElmer ) . The wells of an XFe24 cell culture microplate ( Agilent Technologies ) were coated with 0 . 1% gelatin and incubated at 37°C for 1 hour before gelatin was aspirated and NHDF were plated at 1 . 5 x 104 in 250 μL . Cells were infected with MOI of 50 for 1 hour then placed in 250 μL D-2 . At 48 hpi media was changed to Seahorse XF base medium without phenol red ( DMEM-based medium; Agilent Technologies ) , supplemented with 2 mM glucose and 10 mM glutamine , by rinsing cells twice with 1 mL medium , then replacing it with 500 μL medium . After 1 hour of incubation at 37°C , the supernatant was collected and 2 μL of sample was assayed by lactate assay kit I ( Biovision ) per manufacturer protocol . NHDF or C2C12 stably expressing mCherry in the mitochondria were plated at 1 . 5 x 105 per well and infected with MOI of 20–40 GFP-expressing parasites for 18 h or treated with the indicated concentration of valproic acid starting from the time of infection . All centrifugation steps were carried out at room temperature at 350 g for 5 minutes . At 48 or 66 hpi , infected cells were trypsinized and centrifuged , then resuspended in 4% paraformaldehyde in PBS to fix on ice for 20 minutes . Cells were recentrifuged and the pellet was permeabilized in PBS + 0 . 1% Triton X-100 ( PBST ) with 0 . 5 μg/mL DAPI . Samples were run on a MACSQuant VYB and data was analyzed using FlowJo 7 . 6 . Cells were discriminated based on size and DAPI staining . Infected cells were identified based on GFP expression ( S2D Fig ) , and mCherry expression was examined for all populations . At least 10 , 000 events were collected per condition . Flow cytometric analysis of an endogenous mitochondrial marker , was carried out using antibodies to ATP5B ( Abcam #3D5 ) to stain infected and uninfected NHDF ( without mCherry ) as above . Following permeabilization for 20 min at room temperature , cells were centrifuged and resuspended in blocking solution ( 1% bovine serum albumin in PBS ) and incubated for 30 minutes at room temperature . After centrifugation , cells were incubated for 30 minutes at room temperature with mouse anti-ATP5B ( Abcam #3D5 ) at 1:2 , 000 in blocking solution then washed twice in PBST and incubated in 1:2 , 500 AlexaFluor 594 Goat anti-Mouse IgG ( Thermo Fisher Scientific ) in blocking solution for 30 minutes , washed as with primary antibody and resuspended in PBST with 0 . 5 μg/mL DAPI for flow cytometry analysis as above . NHDF were seeded at 1 . 5 x 106 in T75 flasks and infected with a MOI of 10 for 2 hours . At 48 hpi , cells were scraped , pelleted by centrifugation and resuspended in Laemmli SDS-PAGE sample buffer at 2 x 104 cells/μL . Cell lysates were treated with 0 . 5 μL benzonase to digest DNA , incubated on ice 30 minutes , then at 95°C for 10 minutes before clarification by centrifugation at 15 , 000 g for 10 minutes . Due to the presence of parasites in infected cells compared to mock-infected cells , we could not load gels based on protein quantification . Instead , 2 x 105 cells ( ~30 μg of uninfected cells ) were loaded in wells of a 4–15% Mini-Protean TGX Precast Gel ( Bio-Rad ) and the Western gel and wet transfer to a PVDF membrane was done per manufacturer protocol . All incubation and wash steps were done while shaking . The membrane was blocked in 1:1 SeaBlock:PBS ( Thermo Fisher Scientific ) for 1 hour at room temperature , incubated overnight at 4°C with the corresponding antibodies in 1:1 SeaBlock:PBS-0 . 1% Tween-20 , then washed three times in PBS-0 . 1% Tween-20 prior to incubation with secondary antibodies in 1:1 SeaBlock:PBS-0 . 1% Tween-20 for 30 min at room temperature . The membrane was washed 3 times briefly in PBS-0 . 1% Tween-20 and 3 times in PBS for 10 minutes before imaging using an Odyssey Imaging System ( Li-cor ) . The intensity of the signal for each antibody was assessed by Image Studio software ( version 5 . 2 ) . Lack of cross-reactivity of the antibodies with amastigote proteins was verified using amastigotes lysate following amastigote purification . Vimentin was used as a loading control . Primary antibodies: TOMM20 ( F-10 , Santa Cruz; 1:1 , 000 ) , ATP5B ( 3D5 , Abcam; 1:1 , 000 ) , vimentin ( 5741 , Cell Signaling; 1:2 , 000 ) . Secondary antibodies: Dylight 800 anti-rabbit ( Invitrogen , 1:10 , 000 ) , Dylight 800 anti-mouse ( Invitrogen , 1:5 , 000 ) , Dylight 700 anti-rabbit ( Invitrogen , 1:20 , 000 ) . To determine the number of divisions that an intracellular T . cruzi amastigote has undergone in a defined period of time , a modified flow cytometry protocol , based on [34] was performed . Briefly , NHDF were plated at 1 . 5 x 105 per well in 6 well plates and infected with MOI of 15 for 2 hours using CFSE-stained trypomastigotes . For staining , 5 x 106 trypomastigotes/mL were stained with 1 μM CFSE ( Thermo Fisher Scientific ) in PBS by incubating at 37°C for 15 minutes . Extra dye was quenched by addition of D-10 , and trypomastigotes were pelleted by centrifugation at 2100 g for 10 minutes and incubated in fresh D-10 at 37°C for 30 minutes before infection . At 18 ( pre-replication ) and 48 ( replicative phase ) hpi , infected monolayers were trypsinized , washed once in PBS , and cells were lysed to release amastigotes by passing the supernatant 10 times through a 281/2G needle . Lysate was fixed by adding paraformaldehyde ( Electron Microscopy Sciences ) to a final concentration of 1% and incubating 20 minutes on ice . Samples were centrifuged at 300 g for 5 minutes to pellet away host debris , and the supernatant centrifuged at 4000 g for 10 minutes to pellet amastigotes . Pellets were resuspended in PBS with 0 . 1% Triton X-100 and 0 . 01 μg/mL DAPI for analysis by flow cytometry . Amastigotes were run on a MACSQuant VYB ( Miltenyi Biotec ) or LSRII ( BD Biosciences ) and at least 10 , 000 events were collected per condition . Data was analyzed using FlowJo 7 . 6 , and amastigotes were discriminated based on size and DAPI staining . Proliferation was modeled using FlowJo 7 . 6 , and CFSE intensity at 18 hpi was set as peak 0 for all samples . Host cell and intracellular T . cruzi amastigote numbers were assessed as described with minor modifications [34] . Briefly , NHDF were plated at 1 . 5 x 103 per well in 384 well plates and infected with MOI 1 . 25 for 2 hours before incubation in phenol-free media at the indicated glucose concentration . At 18 hpi , the indicated concentration of 2-DG was added , and at 66 hpi media was removed and host cell number and parasite number were assessed using 10 μL of CellTiter-Fluor ( Promega ) and 10 μL of Beta-Glo ( Promega ) per well , respectively . Isolated amastigotes were resuspended in cytobuffer at 2 x 107 parasites/mL and incubated for 3 hours at 37°C with 6 mM U-13C-glucose ( Cambridge Isotope Laboratories ) or unlabeled glucose ( Sigma Aldrich ) . For metabolite extraction , amastigotes were rapidly cooled to 4°C in a dry-ice ethanol bath with gentle agitation as described [62] , then centrifuged at 3200 g for 10 minutes at 4°C and resuspended in 80% ( v/v ) methanol:water to extract metabolites from the pellet as described [63] . Samples were run in technical triplicate and metabolites detected by the Beth Israel Deaconess Medical Center Mass Spectrometry Facility as described [64–66] . The percent of label incorporation was calculated for each replicate as the peak area of all 13C-labeled variants of a metabolite divided by the sum of both the labeled- and unlabeled-metabolite peak areas . Background was subtracted by averaging the percent label incorporation of unlabeled-replicates and subtracting that value from each labeled replicate . Amastigotes were isolated in KHB and incubated at 4 x 105 amastigotes/mL in 5 mM glucose , 5mM glutamine , or 1 mM pyruvate as indicated at 37°C . Total ATP content was measured in isolated T . cruzi amastigotes under each condition at 0 hr and 24 hr using the ATPlite assay ( PerkinElmer ) following the manufacturer’s protocol . RNA was extracted from either trypomastigotes or purified amastigotes ( 48 hpi ) using the RNeasy purification kit ( Qiagen ) . Using 500ng RNA , cDNA was generated through the iScript cDNA Synthesis Kit ( Bio-Rad ) . Primer sets used for amplification of TcHT were TcHT-F: 5’-TGATGTACCATGTGTCCTCGGCAACG-3’ and TcHT-R 5’-ATGGCACTGCGCTGGACCCGA-3’ [15] or TcHT-F2: 5’-TCCTTCGTGCTCCTGACGAATT-3’ and TcHT-R2: 5’-AAAAGATGAACGCGACTGCCTG-3’ . The primer set for amplification of a parasite housekeeping , ribosomal RNA large subunit gamma M1 was Ribo-F: 5’ -TGTGGAAATGCGAAACAC-3’ and Ribo-R: 5’-CCCAGGTTTTTGCTTTAATG-3’ [12] . Thermal cycling proceeded at 95°C for 10 minutes followed by forty cycles of 95°C 15 seconds and 60°C 1 minute using a StepOnePlus Real-Time PCR System ( Applied Biosystems ) . Relative TcHT abundance was measured using SYBR green iTaq Universal Mix ( Bio-Rad ) and calculated using the ribosomal control and ΔΔCt method . NHDF were seeded at 5 x 104 per well in 24 well plates on 12 mm round German glass coverslips ( Electron Microscopy Services ) and infected with MOI of 10 for 1 hour on glass coverslips . At 48 and 66 hpi , coverslips were fixed in 4% paraformaldehyde in PBS overnight at 4°C , then stained with 2 . 5 μg/mL DAPI and mounted on slides in Mowiol mounting medium . Slides were imaged using a Nikon TE300 and the number of amastigotes per cell was counted for 100 cells per condition . Figures presented show mean values with standard deviation of biological replicates or medians ( non-parametric data ) . Independent experiments were compared where indicated . Comparison of more than two groups was performed using a One-way ANOVA for single factor experiments and Two-way ANOVA for comparisons with two independent variables . The non-parametric Kruskal-Wallis test was used for comparisons of more than two groups that did not have normal distributions . If significant , post hoc tests were used ( p values indicated ) to compare specific groups and correct for multiple comparisons between groups . Statistical analysis was performed using Prism 7 ( GraphPad ) . | The kinetoplastid protozoan , Trypanosoma cruzi , is a highly successful parasite with a broad mammalian host range and the capacity to colonize a variety of tissues within a given host to establish life-long infection . T . cruzi infection causes Chagas disease in humans , characterized by severe cardiomyopathy and gastrointestinal motility disorders , with limited treatment options . Despite the critical role of T . cruzi amastigotes in sustaining mammalian infection , little is known about their metabolic requirements or the range of nutrients available to these parasites in the host cell cytoplasm . Here , we demonstrate that T . cruzi infection triggers a host response in infected cells that includes increased mitochondrial respiration and biogenesis and increased glucose uptake into infected cells . We show that exogenous glucose supports optimal intracellular parasite replication and that cytosolic T . cruzi amastigotes access glucose in situ , potentially via a facilitated transport process characterized here . These findings expand our view of the range of carbons available to the intracellular parasite and suggest even greater metabolic flexibility of the tissue-infective stages of T . cruzi than previously appreciated , a capability predicted to contribute to successful host colonization . | [
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] | 2017 | Modulation of host central carbon metabolism and in situ glucose uptake by intracellular Trypanosoma cruzi amastigotes |
Non-typhoidal Salmonella ( NTS ) bacteremia is a significant cause of morbidity and mortality worldwide . It is considered to be an emerging and neglected tropical disease in Africa . We studied this in two tertiary hospitals–Al Farwaniya and Al Amiri–in Kuwait , a subtropical country , from April 2013-May 2016 . NTS bacteremia was present in 30 of 53 , 860 ( 0 . 75% ) and 31 of 290 , 36 ( 1 . 33% ) blood cultures in the two hospitals respectively . In Al Farwaniya hospital , one-third of the patients were from some tropical developing countries of Asia . About 66% of all patients ( 40/61 ) had diarrhea , and of these , 65% had the corresponding blood serovar isolated from stool culture . A few patients had Salmonella cultured from urine . Patients were either young or old . Most of the patients had co-morbidities affecting the immune system . Two patients each died in both hospitals . The number of different serovars cultured in each hospital was 13 , and most infections were due to S . Enteritidis ( all sequence type [ST] ) 11 ) and S . Typhimurium ( all ST19 ) except in a subgroup of expatriate patients from tropical developing countries in Al Farwaniya hospital . About a quarter of the isolates were multidrug-resistant . Most patients were treated with a cephalosporin with or without other antibiotics . S . Enteritidis and S . Typhimurium isolates were typed by pulsed field-gel electrophoresis ( PFGE ) and a selected number of isolates were whole-genome sequenced . Up to four different clades were present by PFGE in either species . Whole-genome sequenced isolates showed antibiotic-resistance genes that showed phenotypic correlation , and in some cases , phenotypes showed absence of specific genes . Whole-genome sequenced isolates showed presence of genes that contributed to blood-stream infection . Phylogeny by core genome analysis showed a close relationship with S . Typhimurium and S . Enteritidis from other parts of the world . The uniqueness of our study included the finding of a low prevalence of infection , mortality and multidrug-resistance , a relatively high prevalence of gastrointestinal infection in patients , and the characterization of selected isolates of S . Typhimurium and S . Enteritidis serovars by whole-genome sequencing that shed light on phylogeny , virulence and resistance . Similarities with studies from developing countries especially Africa included infection in patients with co-morbidities affecting the immune system , predominance of S . Typhimurium and S . Enteritidis serovars and presence of drug-resistance in isolates .
Salmonella enterica subspecies enterica serovar Typhi and Salmonella Paratyphi A , B and C cause enteric fever , a systemic febrile illness that occurs only in humans . There are more than 2500 serovars of non-typhoidal Salmonella ( NTS ) . NTS infects a variety of hosts and are frequently zoonotic in origin [1] . It mainly causes self-limiting gastroenteritis in humans . NTS has also been recognized as a major cause of extra-intestinal invasive bacterial infection in young children and immunocompromised patients worldwide [2 , 3] . It is a cause of severe bacteremia [1] . It has been estimated that globally about 3 . 4 million cases of bacteremia due to NTS occur every year [4] . The estimated worldwide mortality from NTS infection is 155 , 000 per year [1] . Invasive NTS disease is considered to be an emerging and neglected tropical disease in Africa [1] . Kuwait is a high-income subtropical country situated in the Middle East . It is neither a developing nor a developed country , but is a country in transition . There are no systemic studies of bacteremia due to NTS in Kuwait even though NTS is a major cause of diarrhea in Kuwait [5 , 6] . Antimicrobial resistance is also a problem among diarrheal stool isolates of NTS in Kuwait [7] . Also , there are no data on serovars of NTS causing infection in Kuwait . Therefore , the primary objective of the study was to assess the case-fraction of NTS bacteremia among bacteremia cases in two tertiary hospitals in Kuwait . The secondary objective was to determine the serovars of NTS isolates and their antibiotic susceptibilities . A study was conducted in two tertiary hospitals in Kuwait . We determined the serovars of NTS isolates and their antibiotic susceptibilities . Moreover , all isolates of S . Enteritidis and S . Typhimurium serovars were typed by pulsed-field gel electrophoresis ( PFGE ) to study their relatedness . In addition , selected isolates were subjected to whole genome sequencing for further characterization . We also compared our results with those from Africa . The results are presented in this communication .
E test . Isolates suspected of producing ESBL were tested for clavulanic-inhibitable ESBL production with E test ESBL strips–ESBL CT/CTL 16/1 , ESBL TZ/TZL 32/4 , ESBL PM/PML- as per manufacturer’s instructions ( BioMerieux ) . Vitek 2 test . The Vitek 2 ESBL test ( bioMérieux ) is based on the simultaneous assessment of the antibacterial activity of cefepime , cefotaxime and ceftazidime , measured either alone or in the presence of clavulanate . This test relies on card wells containing 1 . 0 mg/L of cefepime , or 0 . 5 mg/L of cefotaxime or ceftazidime , either alone or associated with 10 or 4 mg/L of clavulanate , respectively . After inoculation , cards were introduced into the Vitek 2 machine , and for each antibiotic tested , turbidity was measured at regular intervals . The proportional reduction of growth in wells containing a cephalosporin combined with clavulanate was then compared with that achieved by the cephalosporin alone and was interpreted as ESBL- positive or–negative through a computerized expert system ( Advanced Expert System ) [14] . Detection of genes encoding ESBL . PCR assays were performed to detect genes encoding blaCTX-M , blaTEM and blaSHV [7] and blaPSE-1 [15] . AmpC disk test and modified three dimensional test ( M3DT ) for detection of AmpC β-latcamases . This test was performed as described by Coskun et al [16] . Enhanced growth of bacteria around blank disks and AmpC disk or intersected growth in the zone of inhibition was considered positive . Pulsed-field gel electrophoresis ( PFGE ) . PFGE was performed according to the CDC protocol ( http://www . cdc . gov/pulsenet/protocols . htm ) using XbaI restriction enzyme ( Roche , Mannheim , Germany ) and a CHEF-DR III PFGE system ( Bio-Rad , Munich , Germany ) . Salmonella Braenderup strain H9812 ( ATCC BAA 664 ) was used as a reference strain . The bands were visualized under UV light with Gel DOC ( Bio-Rad ) and analyzed with Bionumerics software ( Applied Maths NV , Belgium ) . Pairwise similarities between patterns were calculated by DICE’s similarity coefficient . Clustering was based on unweighted pair-wise group method with averages ( UPGMA ) setting tolerance and optimization each at 1 . 5% . Whole genome sequencing ( WGS ) . Based on differences in banding patterns , S . Enteritidis and S . Typhimurium isolates were selected for whole genome sequencing . Sequencing libraries were prepared using the Nextera XT DNA sample preparation kit ( Illumina , San Diego , CA , USA ) and the sequence read data were produced on the Illumina NextSeq instrument ( paired end , 150 base reads ) . Read data were submitted to the sequence read archive under project number PRJNA363099 ( between 70x and 140x read depth coverage for each isolate ) . De novo assembly of the read data from each isolate was performed using MegaHit [17] . The resulting draft genome sequences were used to derive MLST ( MLST:https://github . com/tseemann/mlst PubMLST: https://pubmlst . org/ ) . Abricate ( https://github . com/tseemann/abricate ) was used to detect virulence genes ( [VFDB]: [18] ) . Antibiotic resistance gene profile was determined using Abricate and the Resfinder database [19] . Data management and analysis . Data on NTS bacteremia were obtained from laboratory records . Patient charts were traced , and relevant information was extracted . Results of bacterial analysis including serovars , antibiogram and WGS data were linked to patient data and tabulated in the reference laboratory . This dataset was used for analysis as appropriate . Statistics . The difference in the prevalence of resistance to antibiotics in the two hospitals was calculated by Chi square test . A P value of ≤0 . 05 was considered significant . Ethical approval . Bacterial isolates studied were a part of the routine collection at the Enteric Microbiology Reference Laboratory , Kuwait University , for further studies and archiving . It was not possible to get informed consent of patients as the clinical data were retrospectively collected . No additional specimens were collected for this study and patient identity was kept anonymous . Therefore , a waiver for informed consent , and approval for the study were granted by the Ethics Committee of Ministry of Health , State of Kuwait ( permit number 898/2018 ) .
NTS from blood culture . The isolation of NTS from blood cultures of both hospitals is shown in Fig 1 . In Al Farwaniya hospital , 53 , 860 blood cultures were done and 3981 were positive for microorganisms . Of the positive cultures , 30 were positive for a non-typhoidal Salmonella serovar ( 0 . 75% ) . There were 13 different Salmonella serovars , but 50% of them belonged to S . Enteritidis ( all sequence type [ST]11 ) and S . Typhimurium ( all ST19 ) . In Al Amiri hospital , 29 , 036 blood cultures were done and 2 , 331 were positive for microorganisms . Of the positive cultures , 31 were positive for a non-typhoidal serovar ( 1 . 33% ) . There were 13 different serovars of Salmonella infecting the patients , but 19 isolates ( 61 . 3% ) belonged to S . Enteritidis ( all ST11 ) and S . Typhimurium ( all ST19 ) . Patient characteristics , Salmonella serovars isolated , response to therapy and patient outcome in Al Farwaniya and Al Amiri hospitals are presented in Table 1 . Isolate numbers with suffix F are from Al Farwaniya hospital and those with suffix A are from Al Amiri hospital . Patients were both Kuwaitis and expatriates of many nationalities in both hospitals . Median age for all 61 patients was 58 y and 62 . 3% of patients were >50 y old . Eight patients ( 13 . 1% ) were children <5 y old . Approximately 67% of patients had chronic diseases such as diabetes mellitus , cancer , blood disorder , kidney disease or lung disease . Forty patients ( 65 . 6% ) had diarrhea and 26 of them ( 65% ) had a Salmonella ( identical to the corresponding blood isolate ) cultured from the stool . Four of these 26 patients had also the corresponding Salmonella serovar cultured from the urine . One patient who had diarrhea did not have Salmonella cultured from the stool , but urine culture was positive for the corresponding blood isolate . None of the non-diarrheal patients had Salmonella cultured from the stool . Most patients responded to antibiotic therapy and were discharged . Many classes of antibiotics were used , but many received a cephalosporin . In addition , six patients ( 22A , 36A , 51A , 59A , 60A , 70A ) received steroids . Only four patients died . Antibiogram . The prevalence of resistance to all antibiotics was similar in both hospitals ( P>0 . 05 for all comparisons ) ( S1A & S1B Table ) . Therefore , resistance for isolates from both hospitals was combined and is presented in Table 2 . The prevalence of resistance to ampicillin , ciprofloxacin and tetracycline ranged between 36 . 1 to 50 . 8% . The prevalence of resistance to other antibiotics was either negligible or absent . The resistance phenotypes in both hospitals are shown in Table 3 . A total of 52 of 61 isolates ( 85 . 2% ) were resistant to one or more antibiotics tested . Of the resistant isolates , 30 ( 60% ) were either S . Typhimurium or S . Enteritidis . Among the 16 multi-resistant ( resistant to three or more classes of antibiotics ) isolates , 8 ( 50% ) were either S . Typhimurium or S . Enteritidis . Fourteen isolates ( 23% ) were resistant to a cephalosporin . ESBL production . One isolate ( 69F ) from Al Farwaniya hospital was resistant to all three cephalosporins tested . Two isolates ( 45A and 49A ) from Al Amiri hospital were resistant to all three cephalosporins . All three isolates were negative for clavulanic acid-inhibitable ESBL production by E test and for specific genes encoding ESBL , but were positive for ESBL production by both Vitek 2 test and E test . Two isolates ( 69F and 49A ) were positive for AmpC test . PFGE . The dendrogram of S . Enteritidis isolates from both Al Amiri and Al Farwaniya hospitals is shown in Fig 2 . There were three clusters–cluster 1 comprised isolates 12F , 53F , and 13F; cluster 2 comprised isolates 64F , 10F , 72F , 71F , 22A , 44A , 7F , 23A , 33A , 27A , 30A , 40A , and 38A; and cluster 3 comprised isolates 32A , 3F and 26A . Isolates 48F and 60A were outliers . The dendrogram of S . Typhimurium isolates from both Al Amiri and Al Farwaniya hospitals is shown in Fig 3 . There were three clusters–cluster 1 comprised of isolates 20F and 19F; cluster 2 comprised isolate 36A; cluster 3 comprised isolates 51A , 11F , 54F , 52F , 62F , and 46F; cluster 4 comprised isolates 34A , 21A , 70A , 39A , 42A and 29A . Isolate 36A was an outlier . WGS . Four S . Enteritidis isolates from Al Amiri hospital ( 23A , 32A , 38A , 60A ) and two S . Enteritidis isolates from Al Farwaniya hospital ( 12F , 48F ) were sequenced . Five S . Typhimurium isolates from Al Amiri hospital ( 21A , 51A , 34A , 29A , 36A ) and four S . Typhimurium isolates from Al Farwaniya hospital ( 46F , 52F , 11F , 19F ) were sequenced . These isolates belonged to different clusters by PFGE . The phylogenetic relationship among the six S . Enteritidis isolates based on core genome sequence is shown in Fig 4 . The genome of S . Enteritidis strain P125109 was used as the reference genome sequence . More than 97% of the reference genome was present in the genomes of the six clinical S . Enteritidis isolates . The core genome contained 1999 sites that varied in the six clinical isolates . The pairwise distance was greatest ( 1670 SNPs [single nucleotide polymorphisms] ) between isolates 60A and12F . There were three clusters formed by 12F , 23A & 38A; 32A; and 48F & 60A . The phylogenetic relationship based on the core genomes of the six clinical S . Enteritidis isolates in relation to core genomes of 59 S . Enteritidis strains from different parts of the world for which there are closed genome sequences , is shown in S1 Fig . The greatest pairwise distance of 1833 SNPs was found between isolate 48F and OLF-SE9-10012 , a clam isolate of 2010 from Canada . There were a total of seven clusters and Kuwaiti isolates exhibited three clusters: 12F , 23A & 38A; 32A; and 48F & 60A . The details of genomes of 60 S . Enteritidis strains which were used for comparative analysis are given in S2 Table . The phylogenetic relationship of the nine S . Typhimurium isolates based on their core genome sequence is shown in Fig 5 . The genome of S . Typhimurium strain LT2 was used as the reference genome . More than 95% of the genome of the reference strain was contained in the genomes of the nine clinical S . Typhimurium isolates . The core genome contained 1979 sites that varied among the isolates . The greatest pairwise distance of 1017 SNPs was found between isolates 36A and 34A . Kuwaiti isolates formed five clusters: 11F , 51A &52F; 46F; 34A & 19F; 21A; 29A & 36A . The phylogenetic relationship of the nine S . Typhimurium isolates based on their core genomes in relation to 21 S . Typhimurium strains from different parts of the world whose closed genome sequences are known , is shown in S2 Fig . The greatest pairwise distance of 1333 SNPs was found between isolates 22792 ( a cormorant isolate of 2008 from Canada ) and RM10961 ( an isolate from an agricultural produce in the USA , whose isolation details are not known ) . There were a total of 10 clusters and Kuwait isolates formed four clusters: 19F; 34A; 21A , 29A & 36A; 46F , 11F , 51A & 52F . The details of genomes of 21 S . Typhimurium strains which were used for comparative analysis are shown in S2 Table . Virulence genes . The catalog of virulence genes identified in the whole genome-sequenced S . Enteritidis and S . Typhimurium is shown in S3 Table . The organisms possessed genes encoding a variety of type III secretion system proteins that manipulate host cell transduction pathways and cellular processes to pathogen’s advantage . Other genes included were those for chemotaxis and different fimbriae; anti- inflammatory effector genes for enhancing colonization; genes for resistance to antimicrobial peptides , mouse cecal colonization and prolonged shedding , cellular invasion , enteritis , fluid secretion etc . Of note are: ste gene ( for spread and survival in host tissue ) , sse gene ( for proliferation inside the macrophage and systemic infection in mice ) , sodC1 gene ( for resistance to phagocytosis ) , rck gene ( for serum resistance ) and spv genes ( for proliferation inside the macrophages and late apoptosis and spread of infection ) . Antibiotic resistance genes . Antimicrobial resistance genes were found in some of the whole genome- sequenced S . Typhimurium and S . Enteritidis isolates . These are shown in Table 4 . Based on the presence of an antimicrobial resistance gene , resistance to the corresponding antibiotic was found in the isolate as indicated in the footnote to the Table . Even though S . Typhimurium isolate 34 A possessed blaCARB-2 gene , it was susceptible to imipenem and meropenem . S . Typhimurium isolate 34A resistant to ampicillin , chloramphenicol and tetracycline had corresponding resistance genes . The same isolate was ciprofloxacin- intermediate resistant and had a substitution of asparagine ( N ) for aspartic acid ( D ) at position 87 in the gyrA gene . Multi-resistant S . Typhimurium isolate 51A was ciprofloxacin-intermediate resistant and had a substitution of tyrosine ( Y ) for serine ( S ) at position 83 in the gyrA gene [20] . S . Enteritidis isolates , 23A and 38A carried the ESBL gene , blaTEM-1B and were resistant to ampicillin [21] . Cephalosporin-intermediate-resistant isolates were not among the isolates that were subjected to whole genome sequencing .
A small fraction ( 0 . 75–1 . 33% ) of blood culture-positive isolates only accounted for NTS bacteremia in Kuwait . The prevalence of NTS bacteremia as a proportion of community-acquired bacteremia varies widely according to geographic areas . It was 8% in Southern Africa , 25% in Central Africa , 27% in East Africa , and 18% in Western Africa [22] . In a multicenter study covering Indonesia , Thailand and Vietnam , the prevalence of NTS in individuals positive for any blood-stream bacterial infection was 27 . 5% among children , and 11 . 7% among adults [23] . In a study in Bangladesh , the prevalence of NTS in blood culture was 0 . 16% [24] . A Malaysian study found a prevalence rate of 16 . 2% with most of the cases occurring in children below 1 y of age [25] . The risk factors contributing to NTS bacteremia are extremes of age , immunosuppressive therapy , and underlying comorbidities such as diabetes mellitus , cancer , cardiovascular diseases etc . that affect the immune system [26] . In Africa , the risk factors for children are: sickle cell disease and malnutrition , and the risk factors not associated with age are malaria , anemia and human immunodeficiency virus infection [22 , 26–32] . In Kuwait too , the affected patients were either old people or children . The patients also suffered from comorbidities such as diabetes mellitus , cancer , blood disorders , lung diseases and kidney diseases . In our case series , 4 out of 61 patients died with a case- fatality rate ( CFR ) of 6 . 6% . This low CFR in our study may be attributed to prompt and appropriate therapy of our patients including better management of underlying diseases . CFR was 20 . 6% in Sub-Saharan Africa [22]; 25% in Bangladesh [24]; 33% in Israel [33]; and 8 . 7% in Taiwan [34] . A higher CFR of 40 . 5% was seen among NTS bacteremic patients with malignancy compared to 17 . 7% among NTS bacteremic patients without malignancy in Taiwan [35] . In Al Farwaniya hospital , 16 patients ( 53 . 3% ) had diarrhea , and of these , 5 patients ( 31 . 3% ) had the same serovar of Salmonella as the one in blood culture isolated from the stool . There was an interval of 1–2 days between blood culture and stool culture . If blood culture of a patient was positive for a NTS , the patient was empirically treated with antibiotics during this interval . This antibiotic treatment would have affected the recovery of Salmonella in the stool culture of the patient . Approximately 5% of individuals with gastrointestinal illness caused by NTS will develop bacteremia [36] . However , in primary NTS bacteremia , most of the patients do not develop diarrhea and NTS is also not cultured from stool [37] . This suggested that a higher proportion of Kuwaiti patients with NTS bacteremia had gastrointestinal illness with the isolation of corresponding blood isolates from stools . Many different NTS serovars caused blood stream infection in Kuwaiti patients . However , 50–60% of the infections were due to S . Enteritidis and S . Typhimurium species in the two hospitals . Nonetheless , in Al Farwaniya hospital , most of the expatriate patients from South Asia and Southeast Asia had infection with neither of these serovars . Although many serovars of NTS can cause blood stream infection , S . Enteritidis and S . Typhimurium serovars are the predominant serovars causing blood stream infection in many parts of the world [22 , 29 , 38–40] . NTS infection is usually zoonotic in origin contracted by contact with animals or consumption of contaminated water or food of animal origin [41] . However , there is also evidence of person- to- person transmission [42 , 43] . In the largely urban environment , coupled with the cultural context of Kuwait where pet animals such as dogs are a religious taboo , contact with animals is less likely , and the most likely routes are consumption of contaminated food and person- to- person contact . Patients were treated with many antibiotic classes , but most were treated with a third-generation cephalosporin–ceftriaxone , cefotaxime or ceftazidime . Most patients responded to these antibiotics . The antibiotic response concurs with the susceptibility data ( Tables 2 and 3 ) . ESBL production has been reported in NTS worldwide including in Kuwait [7 , 44 , 45] . In the current study , three out of 13 cephalosporin- resistant isolates showed ESBL production . In two isolates , ESBL production may be mediated by AmpC . All the 13 resistant isolates were negative for specific genes . Resistance may also be due to other mechanisms such as an altered porin with decreased entry of the antibiotic into bacterial cell , with the bacteria showing intermediate susceptibility [46 , 47] . A combination of tests needs to be done for the detection of ESBL production [48] . Isolate 34A possessed the carbapenemase resistant gene , blaCARB-2_1 , yet , it was susceptible to carbapenem . This concurs with a previous report [15] . Most of the isolates were resistant to one or more antibiotics , and 26 . 2% were multidrug-resistant . Half of the multidrug-resistant isolates belonged to S . Typhimurium or S . Enteritidis . Drug resistance is a problem among NTS isolates in many parts of the world [39 , 27 , 28 , 49 , 50 , 51 , 52] . Studies in African countries showed a higher prevalence of multidrug-resistant bacteria ( 40 to 100% ) [28 , 49–52] than in our study . We typed all NTS isolates by PFGE and some selected isolates by WGS . There were discrepancies between the two typing methods . This is not unexpected as approaches to typing are different in the two methods . Nevertheless , studies have shown that WGS is more discriminatory than traditional typing methods including PFGE [53 , 54] . Moreover , WGS gives a near thorough sequence information of all genes present in the bacteria . In our series , the MLST of S . Typhimurium was ST19 and that of S . Enteritidis was ST11 . In Sub- Saharan Africa , the predominant ST of S . Typhimurium was ST313 [55 , 56] . ST 19 was the predominant type found in both Europe and North America [57] . ST11 and ST19 were the predominant types causing gastroenteritis in Qatar , another Middle Eastern country [58] . ST19 was the predominant type in Iran being isolated from blood , urine and stool specimens [59] . It was also the predominant ST found in China [60] . S . Enteritidis ST11 causes diarrhea and blood stream infection world-wide . It caused blood stream infections from Mozambique [61] , Kenya [55] , and Vietnam [62] . Sequencing of our NTS isolates showed that they carried a full complement of virulence genes . Of note are the presence of genes that contribute to systemic spread and survival in the blood stream—ste gene for spread and survival in the host tissue through multiplication in a membrane-bound compartment , SCV [63] , sse gene for survival and replication inside the macrophages via a type III secretory system[64] , sodC1 gene for protection of bacteria against superoxide generated within phagocytes [65] , rck gene for serum resistance [66] , and spv genes for virulence of NTS to cause extra-intestinal disease by cytotoxicity and apoptosis of macrophages [67] . Thus , our data showed that unlike in sub-Saharan Africa and some parts of Asia , there was only a low-case fraction of NTS isolated from blood cultures done at these two hospitals in Kuwait . From a public health point of view , these patients need to be protected from contracting NTS infection by provision of thoroughly cooked foods of animal origin , and a high standard of personal hygiene of caregivers . A significant proportion of our patients had gastrointestinal illness , and mortality was negligible . Similarities with other studies included the following: the patients affected were young or old; most patients had immunocompromising co- morbidities; an array of serovars of Salmonella caused blood stream infection , but most of the isolates were S . Typhimurium and S . Enteritidis; and drug resistance was a problem in the isolates , but most infections were treated with a third-generation cephalosporin with or without other antibiotics . Unlike other studies , we performed phylogenetic analysis of all S . Typhimurium or S . Enteritidis isolates by PFGE and some selected PFGE typed isolates by WGS . These two typing methods showed that the isolates showed closely related clusters . Phylogeny by core genome analysis showed a close relationship with S . Typhimurium and S . Enteritidis from other parts of the world . WGS showed that S . Typhimurium and S . Enteritidis had a complement of virulence genes mediating extra-intestinal infection and antibiotic resistance genes mostly corresponding to the resistance phenotypes . Our study has several limitations since it is a hospital-based study and cases were diagnosed by blood culture . For hospitalization , there is a selection bias towards more severe cases and associated conditions . Hospitalized cases are not truly representative of community cases where a spectrum of severity of cases can occur , and because of this , community population at risk cannot be properly defined . Therefore , our findings cannot be extrapolated to a general population . Hospital records are not primarily designed for research purposes because of incomplete and unstandardized information and diagnostic variability between hospitals . Retrospective studies may have inferior level of evidence compared with prospective studies , and may be subject to confounding variables that may be present , but not measured . Moreover , temporal relationships are difficult to assess in retrospective studies . The concentration of NTS in blood is about 1 cfu per ml [68] . Therefore , conventional blood culture and even polymerase chain reaction ( PCR ) method may not be sensitive enough to detect all true positive cases [69] . This is prompting investigators to design more sensitive methods [70] . | Salmonella organisms are classified into typhoidal Salmonella ( causing enteric fever ) and non-typhoidal Salmonella ( NTS ) ( causing infections other than enteric fever ) . Apart from causing other infections , NTS causes blood-stream infection ( bacteremia and septicemia ) . NTS blood stream infection ( NTS-BI ) is considered to be an emerging and neglected tropical disease in Africa . It causes a very high morbidity and mortality in Africa . The individuals affected in Africa are children , malnourished people , patients with malaria or HIV etc . These conditions affect the immune system and make them vulnerable to infection with NTS . In these patients , diarrheal disease due to NTS is rare . The majority of infections are due to two types of NTS: Typhimurium and Enteritidis . There is a very high prevalence of multidrug-resistance in NTS making the infection difficult to treat . NTS-BI is also present in other parts of the world including developed countries albeit at a lower prevalence . Kuwait is a high-income , subtropical country in transition ( from a developing to developed country ) , located in the Middle East . We studied NTS-BI in Al Farwaniya and Al Amiri hospitals in Kuwait during April 2013 to May 2016 . Out of nearly 30 , 000 to more than 50 , 000 blood cultures done in these hospitals , NTS was present in 0 . 75 to 1 . 33% of blood cultures , representing a very small proportion of blood cultures , unlike in Africa . This showed that 31 patients in Al Farwaniya hospital and 30 patients in Al Amari hospital had NTS-BI . Most of these patients had underlying illnesses such as diabetes , lung infection , cancer etc . that affect the immune system , as in Africa . Many patients also had diarrheal disease caused by the same NTS that caused blood stream infection , unlike in Africa . Only two patients in each hospital died , a low mortality , unlike in Africa . The majority of the isolates belonged to Typhimurium and Enteritidis as in Africa . Even though resistance to drugs was a problem , about quarter of the isolates only were multidrug-resistant , a lower prevalence compared to in Africa . In Kuwait , we performed a detailed genetic study of a selected number of Typhimurium and Enteritidis isolates by a modern technique called whole genome sequencing . This revealed genetic determinants encoding drug-resistance and virulence causing blood-stream infection . This type of study was not performed in African isolates . Thus , our study revealed similarities and differences with studies of NTS-BI in Africa . | [
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] | 2019 | Non-typhoidal Salmonella blood stream infection in Kuwait: Clinical and microbiological characteristics |
Normal development of the respiratory system is essential for survival and is regulated by multiple genes and signaling pathways . Both Tbx4 and Tbx5 are expressed throughout the mesenchyme of the developing lung and trachea; and , although multiple genes are known to be required in the epithelium , only Fgfs have been well studied in the mesenchyme . In this study , we investigated the roles of Tbx4 and Tbx5 in lung and trachea development using conditional mutant alleles and two different Cre recombinase transgenic lines . Loss of Tbx5 leads to a unilateral loss of lung bud specification and absence of tracheal specification in organ culture . Mutants deficient in Tbx4 and Tbx5 show severely reduced lung branching at mid-gestation . Concordant with this defect , the expression of mesenchymal markers Wnt2 and Fgf10 , as well as Fgf10 target genes Bmp4 and Spry2 , in the epithelium is downregulated . Lung branching undergoes arrest ex vivo when Tbx4 and Tbx5 are both completely lacking . Lung-specific Tbx4 heterozygous;Tbx5 conditional null mice die soon after birth due to respiratory distress . These pups have small lungs and show severe disruptions in tracheal/bronchial cartilage rings . Sox9 , a master regulator of cartilage formation , is expressed in the trachea; but mesenchymal cells fail to condense and consequently do not develop cartilage normally at birth . Tbx4;Tbx5 double heterozygous mutants show decreased lung branching and fewer tracheal cartilage rings , suggesting a genetic interaction . Finally , we show that Tbx4 and Tbx5 interact with Fgf10 during the process of lung growth and branching but not during tracheal/bronchial cartilage development .
The development of the respiratory system represents an evolutionary hallmark that allowed vertebrates to survive on land utilizing air as a source of oxygen . Because the respiratory system is dispensable for embryonic survival in mammals , defects in development of the respiratory system manifest at or after birth . Indeed , abnormal development of the respiratory system in humans is associated with multiple disorders such as tracheal/bronchial atresia , tracheoesophageal fistula , bronchogenic cysts , pulmonary/lobar atresia and pulmonary hypoplasia [1] . Thus , it is important to understand the genetic basis of development of the respiratory system . In the mouse embryo , the endodermal foregut tube is patterned by signals from the lateral plate mesoderm leading to specification of the lung and trachea at embryonic day ( E ) 9 . 0 ( 19–24 somites ) . Nkx2 . 1 has been identified as the earliest marker of lung endoderm specification . At E9 . 25 ( 25–28 somites ) , the primary lung buds appear as ventro-lateral outpouchings of the foregut connected ventrally by the tracheal primordium . The lung buds grow in a ventro-posterior direction and continue to elongate until E11 . 5 . The point of connection of the lung buds is thought to be the origin of the tracheal tube , which separates from the esophagus in a caudal to cranial direction by E11 . 5 [2] , [3] . In the mouse , the left lung bud remains a single lobe and the right lung bud forms 4 lobes - cranial , medial , caudal and accessory [2] . The airways undergo a stereotypic pattern of branching beginning at E11 . 5 [4]; development and maturation of the alveoli occurs later . Genes involved in different signaling pathways , including Wnt2 , Fgf10 , Bmp4 , Shh and retinoic acid synthesis genes , have been shown to play important roles in lung specification and branch formation . Complete absence of both Wnt2 and Wnt2b in mesenchyme surrounding the anterior foregut or absence of β-catenin in the foregut epithelium leads to a loss of specification of lung primordia as seen by the absence of Nkx2 . 1 expression [5]–[7] . Embryos lacking Fgf10 , which is normally expressed in mesenchyme surrounding the epithelial branching tips , form a short trachea but have no lungs [8] , [9] . Inhibition of epithelial Bmp4 signaling by overexpression of Xnoggin leads to a decrease in lung size and irregularly shaped lung lobes [10] . Shh null mutant mice have only a rudimentary lung sac due to branching severe branching defect [11] . Additionally , conditional inactivation of Shh in lung epithelial cells leads to the formation of hypoplastic lungs with reduced branching of the peripheral tubules [12] . Retinoic acid receptor ( RAR ) α and RAR β2 double null mutants show left lung agenesis and a hypoplastic right lung at E18 . 5 [13] . Thus , genes expressed in both the mesenchyme and the epithelium are essential for correct lung bud specification and branching . After E11 . 5 , mesenchyme surrounding the dorsal aspect of the trachea differentiates into the trachealis smooth muscle . Mesenchyme surrounding the ventral aspect of the trachea and lateral aspect of the main stem bronchi segments and differentiates into C shaped rings composed of chondrocytes . Ventral tracheal cartilage is formed by migration of cells that undergo mesenchymal condensation [14] . Sox9 has been implicated as an important regulator of mesenchymal condensation and chondrocyte differentiation [15] , [16] . In chondrocyte cultures it has been shown that in addition to Sox9 , FGF2 , Igf1 , Tgfβ2 and Bmp2 enhance chondrocyte formation [14] , [17] . Mutations in a number of genes including Shh , Sox2 , retinoic acid synthesis genes and Fgf signaling pathway genes have been shown to affect cartilage ring formation [11]–[13] , [18]–[21] . Fgf10 mutants form a partial tracheal tube in spite of the failure of lung formation [8] , [9] . Recent evidence shows that loss of Fgf10 leads to defects in tracheal ring formation and that overexpression of Fgf10 between E11 . 5 and E13 . 5 disrupts tracheal rings by altering the periodic expression of Shh in the trachea [22] . The T-box transcription factor genes are important during embryonic development . All members of this gene family contain a conserved DNA-binding T-box domain , which binds to a conserved sequence , the T-box binding element , to activate or repress transcription of specific target genes [23] . All Tbx2 subfamily genes , Tbx2 , Tbx3 , Tbx4 and Tbx5 are expressed in the developing chick lung buds and trachea between stages 15–21 [24] . In the mouse , Tbx1 is expressed in lung epithelium at E12 . 5 , Tbx2 and Tbx3 are expressed in lung mesenchyme at E11 . 5 , and Tbx4 and Tbx5 are expressed in both lung and trachea mesenchyme at E12 . 5 and later [25] . Tbx1 homozygous null mutants die at birth due to severe heart defects; the lungs are never fully inflated [26] but lung development has not been further investigated . In Tbx4 homozygous mutants , lung buds form but the embryos die at E10 . 5 due to failure of allantois development and the subsequent lack of chorio-allantoic fusion leading to placental insufficiency [27] . Tbx5 mutants die around E10 due to defects in heart development [28]; lung development has not been previously investigated . Antisense oligonucleotide depletion of both Tbx4 and Tbx5 , but not Tbx2 and Tbx3 , in lung organ cultures results in inhibition of branching and loss of Fgf10 expression [29] suggesting a role for these factors in lung branching . In the chick embryo , interference with Tbx4 function leads to a reduction in Fgf10 expression in lung mesenchyme and inhibits lung bud formation . Ectopic expression of Tbx4 leads to ectopic expression of Fgf10 and Nkx2 . 1 and lung bud formation in the esophagus . Additionally , ectopic expression of Tbx4 at the boundary between the trachea and the esophagus can lead to lack of separation of these two structures , resulting in a tracheoesophageal fistula [30] . In humans , Tbx5 mutations cause Holt Oram syndrome characterized by heart and forelimb abnormalities . A single de-novo mutation in TBX5 has been linked to right lung agenesis [31] . To study the roles of Tbx4 and Tbx5 in lung and trachea development in the mouse , we made use of conditional alleles to bypass early embryonic lethality . We studied three distinct processes , namely 1 ) lung bud and trachea specification , 2 ) lung branching morphogenesis , and 3 ) tracheal/bronchial cartilage formation . We show that during early stages of development , Tbx5 is important for specification of the lung buds and the trachea . After specification , Tbx4 and Tbx5 interact during lung growth and branching and the regulation of branching is dependent on Fgf10 signaling . Additionally , Tbx4 and Tbx5 interact in the formation of mesenchymal condensations , which ultimately form the tracheal/bronchial cartilage rings independent of Fgf10 signaling .
Tbx5 expression is first detected using in situ hybridization ( ISH ) at E9 . 0 ( 24 somites ) in the mesenchyme of the lung and trachea primordia , concurrent with Nkx2 . 1 expression in the ventral foregut epithelium ( Figure 1A–1D ) . The anterior extent of expression of both genes coincides with the posterior extent of the third pharyngeal pouch ( red arrow in Figure 1A , 1B ) . Tbx4 expression is detected in the lung buds when they first appear a few hours later at E9 . 25 ( 28 somites ) in a pattern similar to Tbx5 ( Figure 1E , 1F ) . Tbx4 and Tbx5 are expressed at E11 . 5 , E13 . 5 and E15 . 5 throughout the lung mesenchyme but not in the epithelium ( Figure 1G–1L ) [25] . Both Tbx4 and Tbx5 show a dynamic expression pattern in the developing trachea . At E11 . 5 and E13 . 5 both genes are expressed throughout the tracheal mesenchyme surrounding the epithelium but are excluded from the epithelium and the outermost layer , the mesothelium ( Figure 1G , 1H , 1M , 1M′ , 1N , 1N′ ) . At these stages , Tbx4 and Tbx5 are expressed in ventral tracheal cells that also express Sox9 ( Figure 1O , 1O′ ) [32] and in dorsal tracheal cells that also express SM22α ( Figure 1P , 1P′ ) [33] . Within the ventral mesenchyme at E15 . 5 , Tbx4 and Tbx5 expression is restricted to mesenchyme between and surrounding cartilage condensations ( Figure 1Q , 1R ) in a pattern complementary to that of Sox9 , which is restricted to condensing mesenchyme of the cartilage rings at this stage ( Figure 1S ) . The genotypes of embryos used in this study and the corresponding descriptive shorthand nomenclature are shown in Table 1 . PCR genotyping was used to determine the efficiency of recombination of the conditional alleles . For embryos carrying the tamoxifen-inducible CreER transgene , a dose of 8 mg tamoxifen was injected into pregnant females at E9 . 0 and embryos were dissected at E12 . 5 . Yolk sacs were analyzed as an estimate of recombination in the whole embryo . Both alleles of Tbx4fl/fl embryos were completely recombined at E12 . 5 to produce the mutant allele at this dose of tamoxifen ( Figure S1A ) , but the single floxed allele of Tbx5fl/+ embryos was only partially recombined to the mutant form ( Figure S1E ) . Doses of tamoxifen higher than 8 mg at E9 . 0 lead to a loss of pregnancy . When 7 mg tamoxifen was injected at E8 . 5 , complete recombination of the Tbx4fl alleles ( Figure S1B ) and the single Tbx5fl allele ( Figure S1F ) was obtained at E13 . 5 . In lung bud cultures a concentration of 1 µM 4-OH tamoxifen produced near-complete recombination of the Tbx4fl allele after 24 hours ( Figure S1C ) whereas the Tbx5fl allele was only partially recombined ( Figure S1G ) . Virtually complete recombination of all floxed alleles was achieved after 4 days of culture ( Figure S1D , S1H ) . These data suggest that the Tbx5fl allele has a lower efficiency of Cre-mediated recombination than the Tbx4fl allele . Thus we assume that there may be some residual Tbx5 activity from the Tbx5fl allele in the in vivo experiments , even in the presence of the CreER and Tbx4cre alleles . To explore the role of Tbx4 and Tbx5 in the earliest stages of lung and trachea specification , foregut culture [34] was used with Nkx2 . 1 as a marker of specification . This ex vivo technique allows for analysis of mutants in culture , circumventing early embryonic lethality of the Tbx4 homozygous mutants due to allantois defects and Tbx5 homozygous mutants due to heart defects . When foreguts and surrounding tissue are isolated at E8 . 75 ( 8–16 somites ) , the foregut tube is devoid of Nkx2 . 1 expression and the lung buds are not present [6] . At the end of 3 or 4 days of culture lung buds and trachea have formed as seen by Nkx2 . 1 expression ( Figure 2A , 2D , 2G ) . Nkx2 . 1 is also expressed in the thyroid primordia at this stage ( Figure 2D–2I and [35] ) . Expression of Tbx4 and Tbx5 was confirmed in control foreguts that were cultured for 4 days ( Figure 2B , 2C ) . Foreguts from E8 . 5 embryos were cultured in the presence of 4-hydroxy ( OH ) tamoxifen and analyzed for Nkx2 . 1 expression . Reduction of Tbx5 alone lead to a lack of Nkx2 . 1 expression in one of the lung buds after 3 or 4 days of culture ( Figure 2E and 2H , respectively ) suggesting a unilateral loss of lung bud specification . Removal of Tbx4 alone did not affect Nkx2 . 1 expression after 3 days of culture ( data not shown ) and removal of Tbx4 in addition to Tbx5 did not exacerbate the Tbx5 phenotype ( Figure 2F , 2I ) . Therefore , Tbx5 but not Tbx4 is important for the bilateral specification of lung buds ex vivo . Wnt2 and Wnt2b , genes essential for specification of respiratory primordia [5] , were analyzed in the conditional Tbx5 null foreguts . Wnt2 expression was reduced ( Figure 2J , 2K ) and Wnt2b expression was absent ( Figure 2L , 2M ) suggesting that Tbx5 lies upstream of these genes in regulating the process of specification . With respect to tracheal specification , Nkx2 . 1 expression was not observed in the foregut tube after 3 or 4 days of culture in embryos lacking Tbx5 ( arrowheads in Figure 2E , 2H ) suggesting a lack of tracheal specification in the absence of Tbx5 . Additional loss of Tbx4 alleles did not alter the phenotype ( Figure 2F , 2I ) , supporting a role for Tbx5 in the specification of the trachea , independent of Tbx4 . To analyze the effect of loss of Tbx4 and Tbx5 on lung branching in vivo we made use of the tamoxifen-inducible CreER transgene . Tamoxifen was injected at E8 . 75 ( 8–16 somites ) , late enough to bypass lethality but well before lung branching begins . Embryos with CreER and different combinations of the Tbx4fl and Tbx5fl alleles were examined at E12 . 5 and E13 . 5 . Conditional Tbx4 null lungs were similar to controls at E12 . 5 but had fewer branching tips at E13 . 5 ( Figure 3A , 3B , 3E , 3F ) . Conditional Tbx4;Tbx5 double heterozygous lungs ( Figure 3C ) were smaller in size and had fewer branching tips than conditional Tbx4 null lungs at E13 . 5 ( Figure 3B ) but were more advanced developmentally ( Figure 3E , 3F ) than conditional Tbx4 null;Tbx5 heterozygous lungs ( Figure 3D ) , which were severely retarded . Lobation in the right lung was disrupted in conditional Tbx4 null;Tbx5 heterozygous lungs: the accessory lobe was missing and only rudimentary cranial and medial lobes were present ( Figure 3H ) . The lobes had a fused appearance ( Figure 3H′ ) suggesting a failure of separation . Histologically , there were no obvious structural defects other than an overall reduction in size ( Figure 3G , 3G′ , 3H , 3H′ ) . Conditional Tbx4 null;Tbx5 heterozygous mutants could not be analyzed later than E13 . 5 , due to hematopoietic defects caused by Cre-induced apoptosis [36] and it was not possible to analyze conditional Tbx5 null embryos using the inducible CreER in vivo due to lethal heart defects . To circumvent these limitations , we used the Tbx4cre allele , which is expressed in the lung and trachea but not in the heart [37] . From this allele , Cre is expressed in the majority of cells of the developing lung and trachea mesenchyme , as seen with lacZ reporter expression at E13 . 5 ( Figure 3I , 3J ) [38] . Lung-specific Tbx5 null mutants , carrying a single copy of Tbx4cre , showed a range of phenotypes from an apparently normal lung to a severe decrease in lung size ( data not shown ) . We hypothesize that the variability in phenotype is due to variable extent of recombination of the Tbx5fl allele ( Figure S1 ) . All lung-specific Tbx4 heterozygous;Tbx5 null pups ( n = 13/13 from 5 litters ) became cyanotic at birth and died shortly thereafter due to respiratory distress . Unlike the variable lung size in the lung-specific Tbx5 null mutants , the lungs of these mutants were consistently smaller than controls at E13 . 5 ( Figure 3K , 3L ) and at birth ( Figure 3N , 3O ) and had significantly fewer branching tips at E13 . 5 ( Figure 3M ) . At P0 histology of the lung-specific Tbx4 heterozygous;Tbx5 null lungs is comparable to controls although the mutant tracheas show accumulation of a mucus like substance ( Figure 3P , 3Q ) . Epithelia of mutant lungs show expression of T1α and Pro-surfactant protein C ( Pro-SPC ) ( Figure S2A–S2D ) , markers for alveolar cell differentiation [10] , [19] , suggesting that appropriate differentiation of the lung epithelium occurs in the lung-specific Tbx4 heterozygous;Tbx5 null lungs . These lungs show lobation defects very similar to the conditional Tbx4 null;Tbx5 heterozygous mutant lungs . The accessory lobe was absent in most embryos but when present showed less branching; the cranial and caudal lobes also showed decreased branching . The cranial , medial and caudal lobes were not separated ( Figure S3A , S3B ) . In addition , although tertiary dorsal branches were present in these lungs , they were crowded together and the secondary lateral branches had outgrown a shorter distance compared to controls ( Figure S3C , S3D ) Using Tbx4cre , it is not possible to study conditional double nulls as Tbx4 is also expressed from this allele . Thus , to further explore branching morphogenesis in conditions where both alleles of Tbx4 and Tbx5 could be deleted , a lung bud culture system was used in which lung buds from embryos with or without the CreER transgene were cultured in the presence of 4-OH tamoxifen . Conditional Tbx4 null;Tbx5 heterozygous , or conditional Tbx4 heterozygous;Tbx5 null lung buds showed reduced branching ( Figure 4A , 4B , 4C ) consistent with the reduced number of branching tips observed in vivo . The conditional double null lungs showed a complete branching arrest by 3 days of culture ( Figure 4D , 4E , 4F ) . The existing branches continued to elongate as seen at 4 days of culture ( arrow in Figure 4E ) . Therefore , Tbx4 and Tbx5 are essential for continuing branching morphogenesis ex vivo . Expression of the lung mesenchymal marker Fgf10 as well as epithelial targets of the Fgf10 signaling pathway , Bmp4 , Spry2 and Etv5 [39] , [40] , [41] , was analyzed in lungs with reduced Tbx4 and Tbx5 expression . Fgf10 is expressed in mesenchyme surrounding the distal epithelial tips that mark the site of future bud formation ( Figure 5A ) [42] . Consistent with the smaller overall lung size , there were fewer foci of Fgf10 expression in the conditional Tbx4 null;Tbx5 heterozygous lungs ( Figure 5B ) and in the lung-specific Tbx4 heterozygous;Tbx5 nulls ( Figure 5C ) . The primary receptor for this pathway , Fgfr2 [43] , is expressed normally in the epithelium of lung-specific Tbx4 heterozygous;Tbx5 null lungs ( Figure 5D , 5E ) . Bmp4 ( Figure 5F , 5G , 5H ) and Spry2 ( Figure 5I , 5J ) were downregulated in Tbx4 and Tbx5-deficient lungs but Etv5 ( Figure 5N , 5O ) expression was not affected , although it was drastically reduced in conditional double nulls cultured ex vivo ( see Figure 6J , 6K ) . Wnt2 , which is normally expressed in the developing lung mesenchyme ( Figure 5K ) [5] , was greatly reduced at E13 . 5 in conditional Tbx4 null;Tbx5 heterozygous ( Figure 5L ) and lung-specific Tbx4 heterozygous;Tbx5 null lungs ( Figure 5M ) . In addition to Fgf10 and Wnt2 signaling pathways , Shh signaling has also been implicated in branching morphogenesis in the lung [11] . Epithelial Shh ( Figure 5S , 5T ) and its mesenchymal receptor Ptc ( Figure 5U , 5V ) showed normal expression in lung-specific Tbx4 heterozygous;Tbx5 null lungs . The epithelial marker Nkx2 . 1 , which is necessary for lung branching [44] and showed unilateral expression in the conditional Tbx5 null foreguts at the time of specification ( Figure 2 ) , showed expression similar to controls at E13 . 5 in the lung-specific Tbx4 heterozygous;Tbx5 null lungs ( Figure 5W , 5X ) . At E13 . 5 , expression of the vascular marker Pecam indicated normal development of vessels around individual bronchioles of Tbx4 and Tbx5-deficient mutants ( Figure 5P , 5Q , 5R ) . Since mesenchymal Fgf10 expression and expression of epithelial targets was affected in mutants with reduced Tbx4 and Tbx5 , we investigated the interactions between Tbx4 , Tbx5 and Fgf10 during branching morhphogenesis using double and triple heterozygotus mutants . Tbx4;Tbx5 double heterozygous lungs were smaller than control lungs at E18 . 5 ( Figure 6A , 6B ) . Tbx4;Tbx5;Fgf10 triple heterozygous lungs were smaller than Tbx4;Tbx5 double heterozygous lungs ( Figure 6B , 6C ) , suggesting that Tbx4 and Tbx5 genetically interact with the Fgf10 signaling pathway during lung development . Removing one copy of Fgf10 from lung-specific Tbx4 heterozygous;Tbx5 nulls did not reduce lung size further ( Figure 6D , 6E ) . Despite the genetic interaction , the addition of exogenous Fgf10 to lung bud cultures of conditional double nulls did not rescue branching ( Figure 6F–6I ) . The lack of change in Etv5 expression , an Fgf10 target , indicated that the Fgf10 signaling pathway was not activated in the presence of exogenous Fgf10 in the conditional double null lungs ( Figure 6J–6M ) . To ensure that the Fgf10 used for the rescue experiments was active , Fgf10 coated heparin beads were placed near the branching tips of lung explants . The tips of both control and conditional double null lungs swelled up in response to Fgf10 beads but not BSA-coated heparin beads ( Figure 6N , 6O ) [45] . Since Fgfr2 is expressed normally in the Tbx4 and Tbx5-deficient lungs ( Figure 5E ) and the conditional double null lungs ( data not shown ) , it is not surprising that the conditional double null lung tips can respond to Fgf10 . However the conditional double null lungs fail to undergo branching in the presence of Fgf10 . Thus , in addition to Fgf10 there must be other factors under the control of Tbx4 and Tbx5 important for activation of the Fgf10 signaling pathway leading to branching morphogenesis . To analyze the development of the tracheal/bronchial cartilage in embryos with reduced Tbx4 and Tbx5 , cartilage rings were visualized at birth using alcian blue staining . Lung-specific Tbx5 nulls and lung-specific Tbx4 heterozygous;Tbx5 null embryos had defective cartilage ring development ( Figure 7A–7C ) with some normal rings ( arrows in Figure 7B , 7C ) and isolated foci of cartilage ( black arrowheads in Figure 7B , 7C ) . The tracheal and bronchial lumen of newborn pups was expanded in the controls ( Figure 7D , 7E ) but collapsed in lung-specific Tbx4 heterozygous;Tbx5 nulls , and contained a mucus-like substance ( Figure 7F , 7G ) . The tracheal epithelium of these mutants showed an increase in the number of mucus-producing cells , as seen by alcian blue staining ( Figure 7D′ , 7D″ , 7F′ , 7F″ ) [19] . To assess the development of cartilage rings at earlier stages , Sox9 expression was analyzed at E12 . 5 and E13 . 5 in lung-specific Tbx4 heterozygous;Tbx5 nulls . At E12 . 5 , these mutant tracheas have Sox9 expression on the ventral aspect of the trachea similar to controls ( Figure 7H , 7I ) but fail to form mesenchymal condensations at E13 . 5 ( Figure 7J , 7K ) . Expression of two genes genetically downstream of Sox9 , Sox6 ( Figure 7L , 7M ) and Sox5 ( Figure 7N , 7O ) , was downregulated at E13 . 5 whereas Col2α1 , a Sox9 target , was expressed at apparently normal levels in those rings that were present ( Figure 7P , 7Q ) . The smooth muscle marker SM22α was analyzed to assess the development of the dorsal trachealis muscle . SM22α was expressed in an expanded domain and there was a loss of the characteristic banding pattern in the mutant tracheas ( Figure 7R , 7S ) indicating a disruption in smooth muscle formation due to loss of Tbx4 and Tbx5 . Tracheas of controls ( Figure 8A ) , Tbx4;Fgf10 double heterozygotes ( Figure 8B ) and Tbx5;Fgf10 double heterozygotes ( Figure 8C ) showed a normal pattern of 10–11 cartilage rings , suggesting a lack of genetic interactions between Tbx4 or Tbx5 and Fgf10 in trachea formation . Fgf10 null mutants do not form lungs but have a truncated trachea with 6–8 cartilage rings , some of which are aberrantly formed [22] ( Figure 8D ) . Tbx4;Tbx5 double heterozygous mice also have tracheas with 6–8 cartilage rings but in addition have main stem bronchial cartilage rings ( Figure 8E ) . Removing a copy of Fgf10 in these double heterozygotes did not alter the phenotype ( Figure 8F ) showing an Fgf10-independent role for Tbx4 and Tbx5 in the formation of tracheal/bronchial cartilage rings . Also , removing a copy of Fgf10 in the lung-specific Tbx4 heterozygous;Tbx5 nulls did not alter the phenotype of the tracheal/bronchial cartilage rings ( Figure 8G , 8H ) . Thus , Tbx4 and Tbx5 affect lung development via control of Fgf10 expression but affect tracheal/bronchial cartilage development independently of the Fgf10 signaling pathway .
Tbx5 is expressed around the lung/trachea primordia at the same time that Nkx2 . 1 , a marker of specification , is first expressed in the primordia of the foregut endoderm . Tbx4 is expressed slightly later at the time of lung bud formation . In our study , loss of Tbx5 , but not Tbx4 , leads to unilateral loss of lung bud specification , indicating that Tbx5 has a distinct function in lung bud formation . In contrast , in the chick , although ectopic expression of Tbx4 in the esophagus can specify lung fate , expression of a dominant negative form of Tbx4 leads to a lack of primary budding in only a third of the mutants analyzed [30] . However , the dominant negative Tbx4 could also be affecting the expression of Tbx5 targets as the repressor construct utilized the complete T-box domain and Tbx4 and Tbx5 have 94% amino acid identity in their T-box domains [46] . This interpretation is compatible with our hypothesis that Tbx5 plays a distinct role in vertebrate lung primordia specification ( Figure 9A ) . Further our results suggest that Tbx5 regulates specification by regulating the activity of Wnt2 and Wnt2b . Loss of Tbx5 alone leads to a lack of tracheal specification ex vivo , a phenotype similar to that of mice mutant for Bmpr1 and Bmpr2 . In these mutants , although lung bud specification occurs , the ventral foregut fails to acquire tracheal identity [47] . Therefore , Tbx5 either acts in parallel with or in the BMP signaling pathway in specification of the foregut into a trachea ( Figure 9A ) . In lung branching morphogenesis , Tbx4 and Tbx5 genetically interact with one another ( Figure 9B ) . Although , neither Tbx5 ( data not shown ) nor Tbx4 single heterozygous lungs show a branching defect , double heterozygous lungs are smaller at E13 . 5 and E18 . 5 with a reduced number of branching tips at E13 . 5 . Because these genes are closely related [46] , they could potentially regulate the same target genes by binding to a similar T-box binding element , independently of one another . Alternatively , Tbx4 and Tbx5 could physically interact with each other as heterodimers to activate or repress transcription of downstream targets , as has been suggested for other T-box genes [48] . Even though Tbx4 and Tbx5 have a shared role in regulating lung branching , their relative contributions may not be equal , as Tbx4;Tbx5 double heterozygous lungs are smaller than conditional Tbx4 nulls . Unequal and distinct functions could be explained by domains outside of the T-box . For instance , the C terminal domain of both Tbx4 and Tbx5 has transcription activating capability which has been correlated to the shared limb outgrowth promoting activity of these genes [49] but Tbx4 also has a C terminal repressor domain which is proposed to be responsible for its distinct hind limb specific patterning activity [50] . In another example Tbx5 and Tbx4 both bind to a distinct LIM domain repeat of the LMP4 protein in the chick [51] , illustrating distinct protein-protein interactions which could have functional implications . In conditional Tbx4 nulls;Tbx5 heterozygous and lung-specific Tbx4 heterozygous;Tbx5 null lungs , reduction of Tbx4 or Tbx5 leads to a severe decrease in branching , a defect in formation of the lobes and failure of lobe separation , while absence of Tbx4 and Tbx5 leads to branching arrest . These results are in line with the published observations that antisense RNAs used against both Tbx4 and Tbx5 inhibit lung branching in culture and that this affect is explained by loss of Fgf10 expression [29] . Tbx5 binds and activates the Fgf10 promoter in vitro suggesting that Fgf10 is a direct downstream target [52] . The drastic effect on lung branching in our study is explained by loss of expression of the important regulatory genes Fgf10 and Wnt2 in the lung mesenchyme . The Fgf10 targets Bmp4 and Spry2 show very low levels of expression in the epithelium of Tbx4 and Tbx5-deficient mutants and Etv5 , another Fgf10 target , shows greatly reduced expression in the conditional double null lungs , leading to the hypothesis that the Fgf10 signaling pathway is activated downstream of Tbx4 and Tbx5 in the developing lung and that Fgf10 genetically interacts with Tbx4 and Tbx5 . Lungs that are triple heterozygous for Tbx4 , Tbx5 and Fgf10 are reduced in size compared to Tbx4;Tbx5 double heterozygous lungs , which supports this hypothesis . Lung-specific Tbx4 heterozygote;Tbx5 null lungs are severely retarded but lung size is not affected by further loss of Fgf10 , demonstrating epistasis and supporting the hypothesis that Fgf10 lies downstream of Tbx4 and Tbx5 . Lack of rescue of branch formation and lack of activation of the Fgf10 signaling pathway by exogenously supplied Fgf10 in culture suggests that there are other factors downstream of Tbx4 and Tbx5 that affect Fgf10 signaling . Although , lungs deficient in Tbx4 and Tbx5 retain the ability to respond to Fgf10 coated beads ( our study and [29] ) , they fail to activate additional factors necessary for branching morphogenesis ( X in Figure 9B ) . One possibility for such a factor is a mesenchymal signaling molecule that communicates with the epithelium and activates downstream target ( s ) required for the activation of the Fgf10 pathway . We examined expression of Ptc and Shh to determine whether the Shh pathway was involved , but Shh signaling is not affected in the lung-specific Tbx4 heterozygous;Tbx5 nulls or in the conditional double null cultures ( data not shown ) . The extracellular matrix ( ECM ) molecules , heparan sulfate ( HS ) proteoglycans aid in Fgf10-Fgfr2 interactions during lung development [45] and hence are good candidates for factors missing in the Tbx4 and Tbx5-deficient mutants . Inhibition of heparanase decreases submandibular gland morphogenesis in culture due to deficiency of Fgf signaling [53] . Additionally , HS-deficient null mouse embryos fail to respond to Fgf signaling and the spatiotemporal expression of cell surface-tethered HS chains regulates the local reception of Fgf-signaling activity during embryonic development [54] . Tbx4 regulates ECM molecules in the developing allantois , specifically the chondroitin sulfate proteoglycan versican ( R . Arora and V . E . Papaioannou unpublished observations ) , suggesting that ECM might be one of the targets for T-box genes in regulating development of other organs as well . Appropriate dorsal smooth muscle development and ventral tracheal/bronchial cartilage development is important for the normal functioning of the trachea . Smooth muscle provides tracheal flexibility and the cartilaginous rings prevent tracheal collapse . Defects in trachea formation may result in tracheomalacia or tracheal stenosis . Reduction of Tbx4 and Tbx5 causes defects in both formation of the tracheal cartilage rings and development of the trachealis smooth muscle indicating interactions between Tbx4 and Tbx5 . Tbx4 and Tbx5 heterozygous tracheas have 10–11 cartilage rings but Tbx4;Tbx5 double heterozygous tracheas have 6–8 cartilage rings , some of which are incomplete . Additional reduction of Tbx4 and Tbx5 in the lung-specific Tbx4 heterozygous;Tbx5 null lungs leads to a complete disruption of cartilage ring formation supporting a genetic interaction between Tbx4 and Tbx5 in trachea formation . Sox9 , a master regulator of the process of chondrogenesis , is expressed normally at E12 . 5 throughout the ventral mesenchyme in lung-specific Tbx4 heterozygous;Tbx5 null tracheas but at E13 . 5 these tracheas show a lack of characteristic mesenchymal condensations and reduced Sox9 expression . Either Tbx4 and Tbx5 control of Sox9 expression becomes more sensitive to dosage at E13 . 5 or Tbx4 and Tbx5 control another factor , possibly an ECM molecule , which is important for formation of mesenchymal condensations and , in the absence of these condensations , there is a down regulation of Sox9 expression ( Figure 9C ) . Expression of Sox5 and Sox6 , genes genetically downstream of Sox9 and important for condensation and cartilage formation , is downregulated in lung-specific Tbx4 heterozygous;Tbx5 null tracheas , concordant with an aberration in the process of chondrogenesis . While Tbx4 and Tbx5 regulate lung branching by controlling Fgf10 signaling , their control of tracheal/bronchial cartilage formation is independent of Fgf10 signaling . Fgf10 homozygous mutants have 6–8 tracheal cartilage rings , although the spacing between them is reduced and the rings do not always form the characteristic C shape [8] , [9] , [22] . Neither Tbx4;Fgf10 double heterozygous tracheas nor Tbx5;Fgf10 double heterozygous tracheas show cartilage condensation defects or a reduction in the number of cartilage rings . In contrast , Tbx4;Tbx5 double heterozygotes show a shorter trachea with 6–8 tracheal cartilage rings suggesting Tbx4 and Tbx5 interact with each other during trachea formation but do not interact with Fgf10 . The triple heterozygous tracheas do not show an exacerbation of the Tbx4;Tbx5 double heterozygous trachea phenotype . Additionally , aberrant Fgf10 signaling has been shown to affect tracheal cartilage formation and loss of Fgf10 affects Shh expression but not Sox9 expression [22] . In contrast , in the Tbx4 and Tbx5-deficient mutants Shh expression appears to be unaffected whereas Sox9 expression is reduced at E13 . 5 . Hence , Tbx4 and Tbx5 control tracheal/bronchial cartilage formation via Sox9 , independent of the Fgf10 signaling pathway .
Mice carrying the following alleles were genotyped as previously described: a Tbx4 conditional ‘floxed’ allele , Tbx4tm1 . 2Pa [55] , hereafter referred to as Tbx4fl; a Tbx5 conditional floxed allele , Tbx5tm1 . 2Jse [56] , hereafter referred to as Tbx5fl; an Fgf10 null allele [8]; ROSA26CRE-ERT2 , a ubiquitous tamoxifen-inducible cre transgene [57] , hereafter referred to as CreER; Tbx4-cre , an insertion into the endogenous Tbx4 allele resulting in a bicistronic allele that expresses both cre and Tbx4 in all areas of Tbx4 expression including lung and trachea [37] , [38] , hereafter referred to as Tbx4cre; and a R26RlacZ reporter [58] . All lines of mice were kept on mixed genetic backgrounds . Embryos were dissected from timed matings and yolk sacs were removed for PCR genotyping . The dark period was 19 . 00 to 05 . 00 h and noon on the day of a mating plug was identified as E0 . 5 . All mouse work was carried out under Columbia University Medical Center Institutional Animal Care and Use Committee guidelines . Tamoxifen ( Sigma ) at a concentration of 20 mg/ml in sunflower oil ( Sigma ) was administered to pregnant females by intraperitoneal injection between 15 . 30 and 19 . 30 hours on E8 . 5 or between 23 . 00 and 24 . 00 hours on E9 . 0 . Whole-mount ISH , immunohistochemistry ( IHC ) , immunofluorescence ( IF ) and ISH on cryosections was performed as described previously [59] , [60] , [61] . Primary antibodies used were anti-PECAM ( Pharmingen , catalog number 01951D ) , anti-E-cadherin ( Takara clone ECCD-2 ) , anti T1α ( Developmental Studies Hybridoma Bank antibody 8 . 1 . 1 ) and anti Prosurfactant Protein C ( Millipore catalog number AB3786 ) . All secondary antibodies were either peroxidase-conjugated donkey IgG from Jackson Immunochemicals or Alexa Fluor 488 from Invitrogen . For histology embryos were removed from the uterine horns , dissected out of the decidua and fixed in Bouin's fixative ( Sigma ) . After dehydration in ethanol , embryos were embedded in paraffin wax , sectioned at 8 µm thickness and stained with hematoxylin and eosin ( H & E ) . Alcian blue staining was performed according to standard protocols [62] . Lungs and trachea were dissected out at different stages , fixed in Bouin's fixative and washed with 70% ethanol . The tissue was then equilibrated in 5% acetic acid and stained with 0 . 05% alcian blue in 5% acetic acid for 2 hours . The tissue was washed in 5% acetic acid to remove excess stain and dehydrated in 100% methanol , cleared in BABB ( benzyl alcohol , benzyl benzoate ) and photographed . For alcian blue staining on sections , 8 µm paraffin sections were rehydrated , treated with 0 . 05% alcian blue in 5% acetic acid and then counterstained with nuclear fast red . Foregut culture was carried out as described previously [34] . Foreguts were isolated from 8–16 somite stage embryos using tungsten needles and cultured at 37°C in the presence of 95% air and 5% CO2 on Transwell-Col filters ( Fisher Scientific ) containing 1 . 5 ml BGJb media ( Invitrogen ) supplemented with 10% fetal bovine serum ( FBS ) , 0 . 2 mg/ml vitamin C ( Sigma ) and 2 µM 4-OH tamoxifen ( Sigma ) . For lung bud culture , lung buds were dissected at E11 . 5 in phosphate buffered saline ( PBS ) with 0 . 1% bovine serum albumin ( Sigma ) and cultured in media containing DMEM ( Invitrogen ) with 10% fetal bovine serum , 1% penicillin/streptomycin ( Invitrogen ) and 1 µM 4-OH tamoxifen on 3 . 0 µm filters ( Millipore ) or 0 . 4 µm Transwell filters ( Fisher Scientific ) . Similar results were obtained using both types of filter; results reported are for experiments with Millipore filters . Where specified , Fgf10 ( R&D ) was added after 1 day of culture at a concentration of 500 ng/ml . In some experiments heparin beads coated with Fgf10 ( 100 µg/ml ) or BSA ( 100 µg/ml ) were placed near the branching tips of the explants after 1 day of culture . Transwell filters were used for the bead experiments . Lungs were stained with either E-cadherin antibody using IHC or E-cadherin RNA probe using ISH . In case of whole mount lungs , lobes were separated and photographed to count the number of branching tips . The cultured lungs were photographed and the branching tips were counted . For some experiments , after E-cadherin ISH , lungs were post fixed in 4% PFA , washed with PBT , dehydrated in 100% methanol , cleared in BABB and then photographed . | Defective development of the mammalian respiratory system can lead to tracheal , bronchial , or pulmonary malformations causing severe consequences at birth or during postnatal life . Studies using mouse genetics have begun to reveal complex regulatory mechanisms that guide the development of the respiratory system , but understanding how disruption of these mechanisms leads to malformations is far from complete . In this study , we analyze the role of two T-box transcription factors , Tbx4 and Tbx5 , in three processes essential to the development of the respiratory system: the specification of the lung and trachea primordia , the growth and branching of the airways to form the lung , and formation of cartilage rings of the trachea and bronchi . In the absence of Tbx5 , only one lung is specified , and no trachea . Both Tbx4 and Tbx5 regulate the process of lung branching by controlling the expression of the secreted growth factor Fgf10 and activation of Fgf10 signaling . In the trachea , both Tbx4 and Tbx5 are important for condensation of cells to form cartilage rings , although this is regulated by a distinct pathway that does not involve Fgf10 . | [
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] | 2012 | Multiple Roles and Interactions of Tbx4 and Tbx5 in Development of the Respiratory System |
The dynamics of circadian rhythms needs to be adapted to day length changes between summer and winter . It has been observed experimentally , however , that the dynamics of individual neurons of the suprachiasmatic nucleus ( SCN ) does not change as the seasons change . Rather , the seasonal adaptation of the circadian clock is hypothesized to be a consequence of changes in the intercellular dynamics , which leads to a phase distribution of electrical activity of SCN neurons that is narrower in winter and broader during summer . Yet to understand this complex intercellular dynamics , a more thorough understanding of the impact of the network structure formed by the SCN neurons is needed . To that effect , we propose a mathematical model for the dynamics of the SCN neuronal architecture in which the structure of the network plays a pivotal role . Using our model we show that the fraction of long-range cell-to-cell connections and the seasonal changes in the daily rhythms may be tightly related . In particular , simulations of the proposed mathematical model indicate that the fraction of long-range connections between the cells adjusts the phase distribution and consequently the length of the behavioral activity as follows: dense long-range connections during winter lead to a narrow activity phase , while rare long-range connections during summer lead to a broad activity phase . Our model is also able to account for the experimental observations indicating a larger light-induced phase-shift of the circadian clock during winter , which we show to be a consequence of higher synchronization between neurons . Our model thus provides evidence that the variations in the seasonal dynamics of circadian clocks can in part also be understood and regulated by the plasticity of the SCN network structure .
The circadian rhythm is a 24 h rhythm which can be found in many organisms ranging from cyanobacteria and fungi to mammals [1] , [2] , [3] . There is a huge interest in studying the circadian rhythm because of the well-known effects of jet-lag after traveling and diseases related to shift-work [4] , [5] . In mammals the major pacemaker is the suprachiasmatic nucleus ( SCN ) , which synchronizes all peripheral clocks in the body and controls the overall behavior [6] , [7] , [8] . It is a small region in the hypothalamus located below the third ventricle and directly above the optic chiasm . Light is the major entrainment factor of the SCN . An important dynamical property of the SCN is that it adapts to different photoperiods in summer and in winter [9] , [10] . This means that the behavioral activity should be longer in summer days than in winter days , which is advantageous for the organism [11] , [12] . The SCN is a symmetric structure , consisting of approximately 20 000 neurons , where each part is usually classified into a ventrolateral ( VL ) and dorsomedial ( DM ) region [13] , [14] , [15] . Cells in the VL region mainly express the neuropeptide , vasoactive intestinal peptide ( VIP ) , comprising around 24% of the SCN neurons in rats [15] , and receive the light information from photosensitive retinal ganglion cells [7] , [16] . However , also other cell types have been identified that show light induced gene expression . For example in hamsters , cells expressing calbindin ( CalB ) are responsible for the mediation of light information [17] . Importantly , it has been found that these cells are nonrhythmic and uncoupled from each other [17] , 18 , 19 , 20 . In contrast to this cells in the DM region , expressing mainly vasopressin ( AVP ) are rhythmic but do not receive light input [17] . It has been shown that separating a part of the DM region leads to nonsynchronous rhythms in the individual cells [21] , indicating that the coupling in the DM region is not sufficient to synchronize these cells . Indeed it has been shown that interactions between cells in this region are restricted to short-range connections [22] . On the other hand long range connections of neurons in the VL region to neurons in the DM region have been identified [13] , [23] , which seem to be important to synchronize cells in the DM and VL region to each other and ensure entrainment of DM cells to an external light-cycle . We are aware that the distinction into a VL and DM region is an oversimplification and not that clear in other species than rats since the distribution of neurotransmitters and retinal inputs shows a more complex spatial organization [17] , [24] , [25] . Nevertheless , since our study is a first attempt to model the influence of long-range couplings we aim at a simple and manageable model . Moreover , the separation into a “core" part , represented by the VL cells , and a “shell" part , represented by DM cells , is commonly accepted also for other rodents [17] . The importance of the neuronal network circuitry mediated by chemical synaptic interactions as opposed to simple global coupling has been demonstrated in several experimental studies [13] , [21] . All these studies show that the functioning of the SCN and the regulation of the circadian rhythm in general is based on a very complex neuronal network consisting of short- and long-range connections . The question arises how the structural properties of this complex neuronal network are linked with the electrical activity of the SCN , which is known to crucially affect the circadian gene expression [26] . It is known that the seasonal adaption of the SCN is closely related to its electrical activity [9] , [10] . In particular , single cells in the SCN have a peak in electrical activity ( measured as firing activity ) from 4–5 h , regardless of monitoring in winter or summer conditions [9] , [27] , [28] , [29] , [30] , [31] . Therefore , it has been suggested that the phase distribution of electrical activity inside the SCN neurons , which is narrower in winter than in summer , leads to a shortened behavioral activity in winter [10] . Different phase distributions in winter and summer have been previously modeled by introducing delay times in the synaptic connections [32] . Here we hypothesize that the adaptation to seasonal changes in the phase distributions can also be related to changes in the structural properties , i . e . , the topology and coupling of the complex neuronal network of the SCN . This is a reasonable hypothesis since , as already mentioned in the previous paragraph , the individual neurons do not change their electrical activity in summer and in winter . Many studies have already examined the network topology of the SCN in various manners [32] , [33] , [34] , [35] , . These studies use different single cell models that range from a generic van-der-Pol oscillator [32] , [34] to more detailed biochemical models [36] , [37] , [38] . Moreover , they model coupling between the cells in different ways . Whereas some studies considered homogenous coupling between all cells [33] , [35] others take into account the heterogeneity in the SCN cellular network [34] , [36] , [37] , [38] . Furthermore , Vasalou et al . [37] have reported that a small-world network architecture of the SCN can firmly mimic the dynamical behavior of mean-field coupled models , but is on the other hand much more efficient in terms of connectivity cost . Also Hafner et al . [38] in a recent study analyze different network topologies with respect to rhythm output and jet-lag adaptation and find that coupling different network topologies leads to robustness of the overall rhythm with respect to perturbations . Nevertheless , all of the abovementioned studies were mainly focused on the synchronization and amplitude properties , entrainment and robustness of the SCN . In our study , we focus on the role of long-range connections between the neurons , as they are known to lead to networks characterized by small-world properties [39] , [40] . Moreover , in jet-lag experiments evidence was found for a connection between the ventral and dorsal part of the SCN [10] , indicating a role for long-range connections between both parts . Using our model , defined by coupled ordinary differential equations , we show that the number of long-range connections between the cells in the VL and DM region is a fine-tunable parameter to adjust the phase distribution and consequently the length of behavioral activity . Our results thus indicate that the seasonal summer/winter dynamics of the circadian clocks can effectively be regulated by the plasticity of the SCN network structure .
It has been shown in mathematical models that the electrical activity measured as the firing rate of neurons is directly related by a threshold mechanism to the underlying molecular clockwork composed of transcriptional and translational feedback loops [41] , [42] . The firing rate is then encoded into the release of neurotransmitter via synapses that in turn affect the underlying molecular clockwork by a cascade involving Ca2+ , cAMP and CRE elements in the promotor region of Per and Cry [13] . Here , we are using a generic amplitude-phase oscillator model that was used in recent studies on the entrainment and the importance of coupling in circadian rhythms and is commonly referred to as the Poincaré oscillator [43] , [44] , [45] . The advantage of this model is its few independent parameters namely the radial relaxation rate and the relative amplitude A . It has been shown that many high-dimensional oscillator models can be reduced to simple two-dimensional amplitude-phase oscillator models [46] . The parameters for this model have been taken from model fits to measurements from single dissociated SCN cells showing a log-normal distribution , with an average value of / and standard deviation 0 . 5/0 . 4 of the underlying normal distribution for A and , respectively [47] . Non-rhythmic cells were modeled with the amplitude set to zero A = 0 , resulting in a damped oscillator [47] . To mimic the spike-like electrical activity of 4–5 h , we adjusted the model as suggested in [43] , with the parameter controlling the phase velocity change set to 2 . The oscillator in the amplitude-phase ( ) representation is given by: ( 1 ) ( 2 ) Here the offset parameter is chosen in such a way that the period of the individual oscillators is Gaussian distributed around 24 h with a standard deviation of 3 h . This ensures that most of the intrinsic periods are in a range of 18–30 h , as experimentally observed [48] . To reduce computational costs our main SCN model consists of cells . However , to check whether our results are independent of the system size , we later also consider a network of cells . To reflect the light-receiving and non-rhythmic cells ( A = 0 ) in the VL region we distributed 1/3 of the neurons in the lower region randomly without any connections between them . The other 2/3 of the rhythmic cells was distributed randomly above . These cells were connected as a random geometric graph [49] , [50] . In particular , for simplicity an identical radius range for all cells was chosen , where signifies the average degree of the random geometric network and is the density of the cells in the region . If two cells fall within each other's range then they are connected . To analyze the importance of the long-range connections between the VL and the DM region they were added with an adjustable probability , similar as in a previous study on small network properties in the SCN [37] . Moreover , since we cannot exclude long-range connections between cells within the same region ( DM , VL ) , these were also added but with a ten times smaller probability ( ) . For simplicity we only considered bidirectional coupling in all cell-to-cell connections . A characteristic network structure is shown in Figure 1 . We use a linear , local mean field coupling model that averages the inputs into each cell and adds these to the oscillator in Cartesian coordinates . It has been used in previous SCN coupling studies [36] , [37] . The terms: ( 3 ) are added to Eqs . 1–2 after transformation into Cartesian coordinates . Here defines the coupling strength , is the degree of node i and is the ij-th element of the adjacency matrix , whose value is 1 if the oscillators are coupled , whilst otherwise the value is 0 . The oscillators are not coupled to themselves ( ) . The light input into our model was simulated by adding the light signal to the x-coordinate of each oscillator cell in the VL region . The light signal was modeled as a square shaped pulse with a period of 24 h and adjustable width , which enables us to simulate different photoperiod lengths . In Section 6 in Text S1 we analyzed the entrainment capacities of a single model oscillator and found that only forcing in the x-coordinate is suitable for entrainment , since forcing in y leads to a shift of the intrinsic period to lower periods ( see Eq . 44 in Text S1 ) . In all calculations we used a fixed amplitude for the periodic light input . Phase response curves ( PRCs ) are a very useful tool to characterize circadian rhythms [51] . They measure the advance or delay of the clocks phase to a perturbation applied at different times of the day . The PRC is measured in our model by applying a light-pulse of 4 h duration and amplitude to the free-running rhythm . The PRC is then scaled to circadian time ( CT0 to CT24 ) by entraining the organism to an external rhythm and taking the maximum of the variable as a phase reference point [51] . The infinitesimal or instantaneous PRCs ( iPRCs ) describe the phase response to an infinitesimally short and small light pulse [52] of each individual oscillator and can be calculated from adjoint equations [53] , [54] , [55] , [56] ( cf . Section 1 in Text S1 ) . Importantly , they allow disassembling the overall PRC , which can be used to deduce factors affecting its magnitude or shape . Due to the long-range connections interaction networks with small world properties can emerge [37] , [40] . Their existence is characterized by a relatively high efficiency E , which serves as an indicator of the traffic capacity of the network and is defined as follows: ( 4 ) where is the length of the shortest path from unit i to unit j . It should be noted that E is inversely related to the average shortest path length , but is numerically easier to use for the estimation of topological distances between elements of disconnected graphs . On the other hand , the cliquishness of a typical neighborhood in small-world networks is large . This characteristic is usually quantified using the clustering coefficient C , which is defined as follows: If the node degree ( the number of neighbors ) of a vertex i is denoted by , there are possible links between these neighbors . One commonly denotes as the fraction of those links that are present in the graph and C is defined as the average of over all the vertices . Furthermore , it has been shown [57] , [58] that the product is a suitable indicator for the optimal small-world network structure , because has its maximum in the region of , where a proper ratio between the clustering and the efficiency is achieved . It should also be noted that it makes sense to argue about small-world characteristics only for low enough values of ( i . e . ) [58] . Above this value the interconnectivity becomes too large and the coupling behaves more as a mean-field type . The synchronization behavior of coupled oscillators for small coupling strengths can be deduced from the eigenvalues of the Laplacian matrix of the network [45] , [59] , [60] ( see Section 5 in Text S1 for further explanation ) . In Sections 2 , 3 and 4 in Text S1 we establish a theory for the phase synchronization of weakly coupled heterogeneous oscillators in an arbitrary network by using the phase-reduction method introduced by Kuramoto [61] . We assume that the heterogeneity in the oscillators and their coupling is small and Gaussian distributed . This is a simplification of our considered network structure because it contains a mixture of damped and self-sustained oscillators and thus a rather large heterogeneity between these two groups . Nevertheless , the analytic results lead to deeper insights into the system and can still be helpful to understand the systems dynamics . For the most general case we find that if the in-phase locking of oscillators is stable , the variance of the stable phase distribution is mainly determined by near-zero singular values of the coupling matrix M , which determines the dynamics of deviations from the synchronized state . If the coupling between the oscillators is additive and similar , the coupling matrix is identical to the Laplacian matrix L of the network up to multiplication by a constant factor . The Laplacian matrix is defined as: ( 5 ) where is the weighted adjacency matrix from Eq . ( 3 ) and is the Dirac delta function . Furthermore , if the Laplacian L is symmetric due to a bidirectional coupling of oscillators the singular values can be replaced by the eigenvalues of L . However , in our case the matrix L in Eq . ( 5 ) is not symmetric because of the local mean field coupling . Thus , we calculated the singular values after removing all completely disconnected oscillators from the network because each disconnected oscillator leads to a trivial zero singular/eigenvalue in the spectrum . In order to generalize our findings we additionally verify how the SCN network behaves when a more complex model for circadian oscillations that , to a certain extent , takes into account molecular aspects of the circadian clock describes the dynamics of the individual cells . The mathematical formalism used to describe the dynamics of individual oscillators is based on the theoretical framework of Goodwin [62] and its extended version proposed by Gonze et al . [35] . The dynamics of the i-th cell is governed by the following set of differential equations: ( 6 ) ( 7 ) ( 8 ) ( 9 ) In Eqs . ( 6–9 ) denotes the clock gene mRNA which produces a clock protein which , in turn , activates a transcriptional inhibitor . Moreover , signifies the neuropeptide serving as a means for intercellular communication . In particular , the neurotransmitter level affects the clock gene transcription ( see Eq . ( 6 ) ) , whereby the neurotransmitter interactions are determined by the network structure: ( 10 ) The term in Eq . ( 6 ) represents the light signal , which is applied only to cells in the VL region and is modeled as a square shaped pulse with a period of 24 h and adjustable width and amplitude 0 . 01 . To mimic the nonrhythmic behavior of cells in the VL region we set for 1/3 of the neurons , whereas in the upper DM region the Hill coefficient for all the cells is set to , so that they exhibit self-sustained oscillations . However , a reduced Hill coefficient results in a decrease of the inherent oscillator frequency . For that reason we adjusted the values for the degradation rates of the clock gene , in order to achieve an inherent period around 24 h for both self-sustained and damped oscillators . In particular , in the VL region we set , whereas in the DM region we chose . Other parameters used in our calculations , except for the coupling strength , were chosen according to Gonze et al . [35]: , , , , , , , , , , , , , . Furthermore , we introduce cell-to-cell variability in terms of different individual periods between cells in the same way as it was proposed by Gonze et al . [35] . The production and degradation rates , , , , , , and are divided by a scaling factor , whose values are chosen randomly from a normal distribution of mean 1 . 0 and standard deviation 0 . 05 .
We entrained our network model over 40 cycles with a 24 h rhythm and variable photoperiod lengths . In all numerical calculations the initial conditions were randomly distributed around x = 1 and y = 0 , according to a normal distribution with a standard deviation 0 . 2 . In all simulations , the final dynamical state was attained after just a few periods ( see Figure S1 in Text S1 ) . Therefore , we chose to discard an initial transient of 15 cycles . In Figure 2 we show the time courses of activity in the x-coordinate of 10 randomly chosen cells for and . This parameter affects the number of long-range connections in the SCN network . It should be emphasized that the coupling of the cells in the network leads to an approximately 10-fold increase in the amplitude of the activity of the single cells , as observed also experimentally [21] . Moreover , it can be seen that decreasing and thus the number of long-range connections leads to a broadening of the peak-phase distribution ( see also Figure S2 in Text S1 ) , whereas the activity peak of the individual cells always has a width of only about 4–5 h as observed experimentally [10] . To capture the effect of the broader phase distribution on the overall electrical activity of the SCN we calculated the mean activity over the entire SCN and shifted this activity into the positive quadrant ( Figure 3A ) . It can be seen that the mean electrical activity is broader for small values of , which we therefore refer to as “summer-topology" and is shorter for large values of , which we therefore refer to as “winter-topology" . Therefore , our model is able to explain the different experimentally observed phase-distributions in summer and in winter [10] , by changes in the network topology . It has also been observed that after entrainment to different photoperiods the width of the phase distribution is preserved under free-running conditions as well . In Figure 3B we show the network's dynamical response without an entraining stimulus for the winter- and summer-topology . We can observe that even without entraining the phase distribution is also preserved under free-running conditions . Thus we can conclude that the width of the activity is mainly related to SCN plasticity [9] . Moreover , even in the absence of light input the non-rhythmic cells in the VL region become rhythmic due to the coupling to the oscillators with a self-sustained rhythm in the DM region as already observed in a previous study on synchronization induced rhythmicity in the SCN [36] . Another surprising observation is that a coupling strength of is sufficient to synchronize the oscillators , despite the significant heterogeneity in the oscillator periods . We attribute this to the weakness ( small radial relaxation rate ) of the oscillators . The sensitivity of the amplitude and width w ( defined as the width of the peak at half the amplitude ) of the mean electrical activity with respect to the parameter is shown in Figure 4 . It can be seen that by changing the number of long-range connections the width of the mean electrical activity can be adjusted to winter and summer photoperiods . When moving from winter- to summer-topology also the mean activity amplitude decreases . To ensure that the width and amplitude of the mean activity is mainly determined by the network topology we also calculated them for short and long photoperiods . It can be seen that both only slightly depend on the length of the photoperiod ( see Figure 4 ) . Remarkably , the same conclusions can be drawn from results presented in Figure S3 in Text S1 , where the mean-field of the summer ( ) and winter topology ( ) for different durations of the light input is shown . To quantify the synchronization between the neuronal cells we calculated the correlation matrices for the summer- and winter-topology under the corresponding entrainment cycle ( Figure 5A–B ) . The ij-th element of the matrix is defined as follows: ( 11 ) where is one of the Cartesian coordinates which signifies the neuronal activity and and are the mean value and the standard deviation of the time series of the i-th oscillator , respectively . The sum in Eq . ( 11 ) runs over the whole temporal series , whereby every 25th point of resulting from numerical integration is recorded . It can be seen that the cells receiving light input in the VL region are more synchronous to each other for both topologies . On the other hand , cells in the DM region in the summer-topology are asynchronous to each other reflecting the broader peak phase distribution , but are more synchronous to the cells in the VL region probably to the ones they have long-range connections to . For the winter-topology also the cells in the DM region show good synchrony to each other , which reflects the narrow phase distribution observed in short photoperiods [10] . The overall better synchrony with more long-range connections in the winter-topology also reflects itself in the average correlation coefficient ( Figure 5C ) . Besides , the level of correlation between cells only slightly depends on the duration of the photoperiod , hence once again confirming that the intercellular network structure is the key agent governing the characteristics of the collective neuronal activity . Furthermore , it has been experimentally observed that the PRC of the SCN shows a larger magnitude in winter than in summer [63] . We have numerically calculated the PRC for the winter- and summer-topology ( Figure 6 ) . In accordance with the experimental observations , the PRC for the winter-topology has a higher magnitude then the summer-topology . Notably , the extent of the phase shift under both conditions is regulated by the amplitude of the perturbation signal ( see Figure S4 in Text S1 ) . To further analyze the underlying reasons for the differences , we calculated the individual instantaneous PRCs of all light-receiving oscillators ( Figure 7A–B ) , which when summed up represent the overall instantaneous PRC ( Figure 7C–D ) . It can be seen that the individual iPRCs are also diminished in magnitude in the summer topology compared to the winter topology . Moreover , the individual iPRCs are more shifted to each other in the summer topology ( correlation coefficient compared to ) , most likely due to the lesser synchronization between the individual light-receiving cells . Both effects lead to a diminished overall PRC . The effect due to shifting of the curves is enhanced for even smaller values of : for ( Figure S5 in Text S1 ) . To characterize the different network topologies in winter and in summer we calculated the network efficiency and clustering coefficient , two properties that allow the characterization of small-world networks ( Figure 8 ) . It has been shown that the product of the two measures is the largest when the network possesses small-world properties 57 , 64 . Using this measure characterizes the winter-topology as a small-world network , which is efficient in terms of costs for the neuronal connections and the achieved synchrony between the individual cells . On the other hand the summer-topology is not able to achieve synchrony due to the reduced number of long-range connections , which lessens the small-world properties of the network . To additionally characterize the synchronization behavior of the network we calculated the singular value spectrum of the network's Laplacian matrix L for 25 replicates of our network structure ( see Section 5 and Figure S6 in Text S1 ) . For a symmetric coupling it has been shown that large eigenvalues indicate groups of oscillator that synchronize fast , whereas near zero eigenvalues indicate a community structure inside the network , with groups of oscillators that do not synchronize their phases to each other [59] , [60] . In Sections 2 , 3 and 4 in Text S1 we generalize these numerical findings to a network of weakly coupled heterogeneous oscillators and show analytically that for a non-symmetric coupling small near-zero singular values lead to a large variance in the synchronized phase distribution . Although , the theory is established for a network of only self-sustained oscillators the numerical calculations indicate that its predicted effects are still applicable for the here considered network of damped and self-sustained oscillators . The singular value spectra for the summer topology possesses many near-zero eigenvalues indicating that indeed a community structure is present , where oscillators within a community are more connected to each other than to members from other communities ( see Section 5 in Text S1 ) . Our theoretical results now also explain how this effects the stable distribution of phases . In particular the near-zero eigenvalues present for the summer topology lead to a dramatic increase in the width of the phase distribution . To illustrate this even more we have also calculated the mean number of near zero singular values over different values of ( Figure 9 ) . It can be seen that for only the trivial zero singularvalue exists , whereas at lower values of small non-zero singularvalues indicate groups of oscillators that do not fully synchronize their phases to each other and therefore lead to a broader SCN activity . To further characterize our model we also analyzed the entrainment properties of our model . In Section 5 in Text S1 we analyze the entrainment dynamics of a single , uncoupled oscillator in an analytic way and derive , several explicit formulae for the entrainment borders ( see also Figure S7 in Text S1 ) . These results shows that the individual weak , spiking single oscillator we consider shows an entrainment behavior that markedly differs from the entrainment of a rigid amplitude-phase oscillator . Especially , after a certain threshold of the entrainment amplitude b the external forcing overrides the internal dynamics and the oscillator is practically entrainable to every period . We therefore compared the entrainment region of the single uncoupled oscillator ( Figure S7 in Text S1 , dotted lines ) with the entrainment region of the oscillator network in summer and winter ( Figure 10A–B ) . Note that due to the high computational costs , we considered only one network realization per condition . Therefore , we expect that the borders of the entrainment region slightly change with each realization . Nevertheless , even the calculations for just one realization give valuable insights . We find that due to the coupling the oscillator network behaves more like a rigid oscillator as found also in a previous study [44] . Moreover , the entrainment amplitude b needs to be significantly higher compared to the single oscillator to achieve entrainment . This effect is related to the amplitude expansion of the coupled oscillators . This is confirmed by calculations of the entrainment region for an oscillator network with increased coupling strength ( Figure 10C–D ) . The entrainment region becomes even smaller due to increased rigidity and amplitude of the oscillator network in line with the previous results [44] . Nevertheless , we observe that the entrainment region of the winter topology is larger than that of the summer topology . This result is in line with the observed reduced phase response curve of the summer topology ( Figure 6 ) . Another interesting observation is that for a small coupling strength the entrainment region is not symmetric for large entrainment amplitudes and especially , entrainment to smaller periods is not possible . In order to verify that our findings are indeed qualitatively independent of the system size we calculated how the amplitude and the width of the mean electrical activity change with respect to the parameter delta for a three times larger system size ( 1800 neurons , 600 of them being located in the VL region ) . First , we computed at which values of delta small-world network properties are obtained . Results shown in Figures S8A–B in Text S1 reveal that those characteristics , which reflect winter conditions , are found around . Accordingly , the SCN network structure reflecting summer conditions was chosen to be at . Remarkably , the results in Figures S8C–D in Text S1 , showing the changes of w and h as a function of delta , clearly indicate that qualitative identical results are obtained for larger system sizes as well ( compare to Figure 4 ) . Finally , we examine how the SCN network behaves in the case that a more complex and biologically relevant model – the Goodwin oscillator – drives the dynamics of individual cells . Results presented in Figures 11A–B show the time evolution of the concentration of the clock gene u in summer ( ) and winter ( ) conditions . Obviously , the phase distribution is much broader in summer conditions than in winter conditions . This observation is additionally confirmed with the results presented in Figure S9 in Text S1 , where the time evolution of the mean SCN activity over several entraining cycles is shown . Similar as in the case of the simple amplitude-phase oscillator ( see Figure 3 ) , the mean field signal of the Goodwin oscillator network is higher and narrower in winter . Furthermore , Figure 11C shows the average correlation coefficient as a function of the network parameter , whereby a greater extent of synchronization can be observed as is increased . Thus , the results obtained with the more complex model for circadian oscillations are qualitatively very similar to those obtained with the simple amplitude-phase oscillator .
The importance of network plasticity for the adaptation of the circadian rhythm to different photoperiods was put forth in several experimental studies [9] , [10] . Using our model , we identified one possible mechanism able to explain the adaptation to different photoperiods: the introduction of long-range connections between cells in the VL ( ventrolateral ) and DM ( dorsomedial ) region of the SCN . In particular , the length of the behavioral activity can be regulated as follows: dense long-range connections during winter lead to a narrow activity phase , while rare long-range connections during summer lead to a broad activity phase . We found that this result is independent of the number of neurons . Moreover , similar results were obtained when replacing the model governing the dynamics of the individual neurons , namely the simple amplitude-phase oscillator , with the more biologically relevant Goodwin oscillator . To go from a wide phase distribution in summer to a more narrow and synchronized phase distribution in winter roughly the doubled number of long-range connections had to be introduced . In our model of 600 neurons , this amounts to around 450 connections in the summer topology and 900 connections in the winter topology . Taking into account that the fraction of added long range connections leading to a network with small world properties is inversely proportional to the number of oscillators [57] , [58] , we can extrapolate the number of required long-range connections in the real SCN network consisting of 20000 neurons . In this case , roughly 15000 connections would need to be introduced to change the network from the summer-topology to the winter-topology . It should be emphasized that the photoperiod gradually changes from summer to winter and hence the introduction of the additional neuronal connections would occur in a time span over roughly half a year . In fact the physical connections between neurons do not need to be introduced but synaptic plasticity changing the responsiveness of individual neurons could lead to a weakening or strengthening of connections . To support those ideas we also calculated how the dynamics of the SCN network changes if the coupling strength of the long-range connections in a winter topology network with is gradually reduced . The simulation results indicate that reducing the coupling strength by at least 75% for half of the long-range connections ( reflecting the summer topology with ) leads to very similar effects as a complete deletion of links ( Figure S10 in Text S1 ) . Moreover , a simulation with a rapid transition from winter to summer topology shows that the entrainment transient is very short and therefore negligible for gradual changes over the year ( Figure S11 in Text S1 ) . Our model is also able to simulate the increased magnitude of the overall phase response curve ( PRC ) in winter compared to summer . This can be explained by the reduced magnitude of the individual instantaneous PRCs and their shifting relatively to each other in the summer topology , leading to an overall smaller phase response . Both effects are due to the reduced synchrony in the summer-topology shown by the correlation matrices in Figure 5 . Since the cells are at different phases of their inner clock when not completely synchronized this leads to a smaller overall response , much like is the case for coupled but unsynchronized pendulums that are perturbed by a pulse . Of course , the individual PRCs do not contain a “dead" zone with no phase shift . The reason for this is the simple model for the individual oscillators , which cannot account for the complicated phase response behavior of more realistic models . However , the focus of this work was not on modeling the individual oscillators but analyzing the impact of network changes on the synchrony of the overall SCN and how photoperiod adaptation is related to this . Along with the phase response , we also analyzed the dependence of the entrainment region of the oscillator network on the network topology and the coupling strength between the individual neurons . Our results agree with previous studies that found a decreased entrainment region for increased coupling between the oscillators , due to an increased in rigidity and amplitude of the oscillators [44] . Moreover , in line with the observed reduced PRC of the summer topology we also observe that the entrainment region of the summer topology is smaller . This result is counterintuitive , since intuitively one would expect that the oscillators become more rigid in a network with more long-range connections . In addition , the amplitude of the oscillator networks mean field is larger in the winter topology as compared to the summer topology . A common statement in the analysis of oscillator synchronization is that large amplitude oscillators are harder to entrain . Our results show that this statement cannot simply be carried over to oscillator networks . It seems that the effect of adding specific connections between cells is different from an increase in the overall coupling strength . Whereas , the latter leads to a decrease in the entrainment region , due to amplitude expansion and increase in rigidity , the former leads to a larger entrainment region due to a more synchronized phase response . These findings underline the importance of the network structure connecting the oscillators . Another possibility to achieve different phase distributions in summer and winter are changes in the intrinsic oscillator characteristics or in the transmission of signals between the cells . For example the introduction of a delay distribution in the synaptic transmission also leads to a distribution of peak phases [32] . Changing the delay distribution could also allow the adaptation to different photoperiods . However , it is difficult to explain a delay of several hours physically since it has been observed that the transcriptional induction of Per upon a light pulse is within 5–15 minutes during subjective night [13] . Since the same pathway induces transcription in cells not sensing light but neuropeptide release , we can assume that the delay times are on the same order of magnitude also for intercellular coupling via neuropeptides . Our results support the ideas pointed out by Meijer et al . [10] in that the SCN neuronal network plasticity crucially affects the activity phase distribution among SCN neurons and therefore contributes to the adaptation to changes in day length . Interestingly , our results indicate that the intercellular communication network in the SCN has features of a small-world network . Such complex topologies have been identified in numerous biological systems including functional as well as anatomical connections in the nervous system [65] . They indeed seem to be advantageous for various living organisms . A closer inspection of Figures 4A–B and 5 reveals , that below the region where the small-world properties of the network are well expressed ( , see Figure 8 ) the curves are very steep . For the SCN it would thus be advantageous operating in the proximity of the optimal small-world configuration . Namely , rather small modifications in the extent of long-range connections enable the regulation of synchronization behavior and phase distributions of electrical activity in the neuronal population . In this manner the plasticity as well as the realization of different neuronal coupling mechanisms between the ventral and dorsal SCN have a large impact during the adjustment to seasonal changes . The effect of the broad phase-distribution in the summer topology is explained by the introduction of well-connected communities separated by bottlenecks into the network . Local clusters of SCN oscillator synchronize fast and well to each other . However , the synchronization between these clusters is hindered due to the few connections between them . The community structure of a network can be quantified by the so called algebraic connectivity given by the second smallest eigenvalue of the networks Laplacian matrix ( see Eq . 5 ) [66] , [67] . If this eigenvalue is near zero the network can be easily separated into groups . It was shown numerically that near-zero eigenvalues lead to a more unsynchronized state of the oscillators [60] , [68] . Our analytical results ( Sections 2 , 3 and 4 in Text S1 ) support these findings and show that the distribution variance of oscillator phases is strongly influenced by near-zero eigenvalues . Remarkably , these theoretical results do not depend on the underlying model governing the dynamics of individual cells and therefore generalize the proposed mechanism of photoperiod adaptation by controlling the number of long-range connections and consequently the community structure in the SCN network . Another advantage of the theoretical results is the insight into the relation between synchronized phase and period distribution on the one hand and network properties on the other hand ( see for example Eq . 18 in Text S1 ) . Future studies could use this relation to extend our model to account for other features of the phase distribution in the SCN , for example the observed bimodal phase distribution in long photoperiods [30] , [31] . | Circadian clocks drive the temporal coordination of internal biological processes , which in turn determine daily rhythms in physiology and behavior in the most diverse organisms . In mammals , the 24-hour timing clock resides in the suprachiasmatic nucleus ( SCN ) of the hypothalamus . The SCN is a network of interconnected neurons that serves as a robust self-sustained circadian pacemaker . The electrical activity of these neurons and their synchronization with the 24-hour cycle is established via the environmental day and night cycles . Apart from daily luminance changes , mammals are exposed to seasonal day length changes as well . Remarkably , it has been shown experimentally that the seasonal adaptations to different photoperiods are related to the modifications of the neuronal activity of the SCN due to the plasticity of the network . In our paper , by developing a mathematical model of the SCN architecture , we explore in depth the role of the structure of this important neuronal network . We show that the redistribution of the neuronal activity during winter and summer can in part be explained by structural changes of the network . Interestingly , the alterations of the electrical activity patterns can be related with small-world properties of our proposed SCN network . | [
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"biology",
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] | 2012 | Modeling the Seasonal Adaptation of Circadian Clocks by Changes in the Network Structure of the Suprachiasmatic Nucleus |
Molecular-based surveys have indicated that Ancylostoma ceylanicum , a zoonotic hookworm , is likely the second most prevalent hookworm species infecting humans in Asia . Most current PCR-based diagnostic options for the detection of Ancylostoma species target the Internal Transcribed Spacer ( ITS ) regions of the ribosomal gene cluster . These regions possess a considerable degree of conservation among the species of this genus and this conservation can lead to the misidentification of infecting species or require additional labor for accurate species-level determination . We have developed a novel , real-time PCR-based assay for the sensitive and species-specific detection of A . ceylanicum that targets a non-coding , highly repetitive genomic DNA element . Comparative testing of this PCR assay with an assay that targets ITS sequences was conducted on field-collected samples from Argentina and Timor-Leste to provide further evidence of the sensitivity and species-specificity of this assay . A previously described platform for the design of primers/probe targeting non-coding highly repetitive regions was used for the development of this novel assay . The assay’s limits of detection ( sensitivity ) and cross-reactivity with other soil-transmitted helminth species ( specificity ) were assessed with real-time PCR experiments . The assay was successfully used to identify infections caused by A . ceylanicum that were previously only identified to the genus level as Ancylostoma spp . when analyzed using other published primer-probe pairings . Further proof of sensitive , species-specific detection was provided using a published , semi-nested restriction fragment length polymorphism-PCR assay that differentiates between Ancylostoma species . Due to the close proximity of people and domestic/wild animals in many regions of the world , the potential for zoonotic infections is substantial . Sensitive tools enabling the screening for different soil-transmitted helminth infections are essential to the success of mass deworming efforts and facilitate the appropriate interpretation of data . This study describes a novel , species-specific , real-time PCR-based assay for the detection of A . ceylanicum that will help to address the need for such tools in integrated STH deworming programs . ANZCTR . org . au ACTRN12614000680662
Ancylostoma ceylanicum is the only predominantly animal-infecting hookworm species known to successfully develop into adults within the human intestine [1] . This zoonotic species has been reported to infect animals and humans in Southeast Asia , South America , South Africa , Melanesia and Australia [2–13] , and prevalence studies have demonstrated A . ceylanicum to be the second most common hookworm species causing human infection in many parts of Asia [5 , 14–16] . Equally troubling , a recent study investigating A . ceylanicum transmission conducted in Cambodia demonstrated the ability of this worm to result in burdens of infection comparable to levels caused by Necator americanus [6] . Furthermore , it has been demonstrated that Ancylostoma duodenale can ingest up to eight times the volume of blood consumed by N . americanus , resulting in increased iron depletion [17] . Accordingly , frequent cases of Ancylostoma-associated anemia have been reported , even among properly nourished individuals [12 , 18] . Although only speculative , similar pathology might exist for human infections with A . ceylanicum , underscoring the potential importance of this zoonotic parasite for both child and maternal health [19] . Despite these findings , methodologies for the reliable , species-specific , and sensitive molecular detection of A . ceylanicum are lacking , resulting in a need for improved diagnostic tools . Historically , diagnosis and detection of all hookworm species has relied heavily on the use of coprological methods [5] . Widespread use of these methods stems from their low cost , simplicity , and minimal infrastructure requirements . By default , microscopy has served as the standard method for gastrointestinal helminth detection [5] . However , at the egg level , and in some cases even at the larval level , certain hookworm species are morphologically indistinguishable [20] and further culturing of worms is required for differentiation . Conducting such coprological techniques is time-consuming and requires highly skilled microscopists for accurate assessment of results [21] . Accordingly , PCR-based molecular diagnostic tests for hookworm provide an attractive alternative to microscopy with more modest training requirements . However , such assays frequently target the internal transcribed spacer regions ( ITS ) of the rDNA gene clusters , which demonstrate considerable homology between closely related Ancylostoma species [5] . This similarity can lead to species misidentification in hookworm positive samples . Several PCR variations such as nested PCR and PCR coupled with restriction fragment length polymorphism ( PCR-RFLP ) have been developed to differentiate between the hookworm species . Unfortunately , these methods tend to be laborious , requiring many steps and/or restriction enzyme digestions coupled with agarose gel electrophoresis [4 , 21 , 23 , 24] . In addition , nested PCR is far more likely to result in false positivity due to sample contamination and increased numbers of PCR cycles [22] . Thus , simplified PCR assays , not reliant on nested PCR or restriction enzyme digestions , would be of great benefit to programmatic efforts . Furthermore , while drug intervention and hygiene programs have made substantial gains and continue to show great promise , they frequently fail to account for the control of zoonotic infections [15] . Such concerns are particularly important when proper sanitation is lacking as the potential for zoonotic transmission from wild or domestic animals to humans is dramatically increased [5] . Accordingly , species-level identification provides insight into possible infection reservoirs , which might not be considered utilizing genus-level diagnostics . Herein we report the development and validation of a novel , real-time PCR-based diagnostic assay for the species-specific detection of A . ceylanicum . The development of this test is based on the identification of a highly repetitive , non-coding DNA element using advanced bioinformatics to analyze whole genome sequence data for target discovery and assay design [25] . We posit that the incorporation of this new assay into our previously established diagnostic platform [25] will aid programmatic efforts , as accurate species-level detection and differentiation of Ancylostoma ssp . is now possible .
Genome sequences for A . ceylanicum were obtained from NCBI ( Sequence Read Archive ID: SRR2037027 and SRR2037046 ) . Analysis of repeat DNA content was performed using the publicly available platform , RepeatExplorer [26] , following our previously established bioinformatics workflow [25] , and a primer-probe set was designed using the PrimerQuest online tool ( Integrated DNA Technologies , Coralville , IA ) ( Table 1 ) . This candidate primer-probe pairing then underwent further bioinformatics analysis using the Primer-BLAST tool available from the National Center for Biotechnology Information ( NCBI ) website . Utilizing default parameters , the candidate primer pair did not return any matches to off-target templates within NCBI’s Nucleotide Collection database , indicating target specificity and ensuring that the occurrence of off-target amplification would be extremely unlikely . Default parameters for probe-based PCR were utilized for the assay design and the probe was labeled with 6FAM fluorophore at the 5’ end and double-quenched with ZEN ( internal ) and 3IABkFQ at the 3’ end . A concentration matrix was created to determine the optimal forward and reverse primer concentration as previously described [25] . All reactions were performed in 7 μl total volumes utilizing 2 μl of template , 3 . 5 μl of TaqMan Fast Universal PCR Master Mix ( ThermoFisher Scientific , Waltham , MA ) , and the appropriate concentrations of primers and probe . Cycling conditions consisted of an initial 2-minute incubation step at 50°C , followed by a 10-minute incubation at 95°C . These incubations were followed by 40 cycles of 95°C for 15 seconds for denaturation and 1 minute at 59°C for primer annealing and polymerase extension . All reactions were conducted using the StepOne Plus Real-Time PCR System ( Life Technologies , Carlsbad , CA ) . Following the completion of primer optimization reactions , the sensitivity and specificity of our assay was evaluated . For sensitivity testing , the optimized assay was performed utilizing a titration of genomic DNA concentrations as template . Reactions were conducted using 2 μl of A . ceylanicum genomic template at concentrations of 1 ng/μl , 100 pg/μl , 10 pg/μl , 1 pg/μl , 100 fg/μl , 10 fg/μl , 1 fg/μl , 100 ag/μl 10 ag/μl , and 1 ag/μl . In order to verify assay specificity , optimized conditions were employed to evaluate the potential for non-specific amplification of control genomic DNA from six other species of soil-transmitted helminths ( A . duodenale , N . americanus , Trichuris trichiura , Strongyloides stercoralis , Ascaris lumbricoides and Ancylostoma caninum ) , along with human genomic DNA and DNA of the common gastrointestinal tract commensal bacterium Escherichia coli ( strain K-12 ) . All of these control samples were tested utilizing 2 μl of DNA template at concentrations of 1 ng/μl . A panel of six 10-fold serial dilutions of an initial 1 ng/μl stock of A . ceylanicum genomic DNA was created . To each dilution in this panel , genomic DNA was added from both A . caninum and A . duodenale such that the concentrations of DNA from both of these other species were at a final concentration of 1 ng/μl in each panel dilution . The aforementioned panel was tested with the optimized real-time PCR based assay for A . ceylanicum to prove that the presence of other Ancylostoma spp . , simulating mixed infections , would not affect our assay’s specificity . Furthermore , testing of this panel enabled us to determine the assay’s limits of detection when increasingly limited concentrations of target DNA ( A . ceylanicum ) were intermixed with genomic DNA from the two other Ancylostoma spp . ( S1 Table ) . Sixty-one human stool samples previously collected as part of the “Wash for Worms” intervention trial in Timor-Leste ( Trial registration: ACTRN12614000680662 ) were selected for inclusion in this study . DNA extractions for these samples were previously performed at QIMR Berghofer , and real-time PCR analysis demonstrated the presence of Ancylostoma spp . DNA in 22 of these samples [27] . Eight additional samples , collected in Orán , Argentina , as part of a larger collection effort ( IRB00008019 ) , were also selected for inclusion in this study . For Argentinian samples , DNA was previously extracted at Baylor College of Medicine utilizing a published methodology [28] . Prior real-time PCR analysis of these eight samples had demonstrated the presence of Ancylostoma ssp . DNA [28] . A previously published semi-nested conventional PCR assay coupled with restriction fragment length polymorphism analysis [23] was employed to distinguish between the different hookworm species in the samples obtained from both Timor-Leste and Orán , Argentina . All amplification reactions and digestions were conducted in accordance with the previously described methodologies [23] . DNA aliquots of all samples from both Timor-Leste and Orán , Argentina were sent to Smith College for further analysis . Samples were blindly coded and assayed for the presence of both A . duodenale and A . ceylanicum . Testing for the presence of A . duodenale occurred in accordance with the previously described protocol [25] , while A . ceylanicum testing was conducted using the novel assay described here .
Utilization of the previously published workflow for repeat analysis led to the identification of a novel target for the sensitive and species-specific real-time PCR-based detection of A . ceylanicum [25] . Employing the PrimerQuest online tool , a primer/probe pairing was identified and oligonucleotides were synthesized . Through the analysis of a titration matrix , primer optimization reactions were performed , and the optimal forward primer concentration was determined to be 125 nM , while the reverse primer was demonstrated to have an optimal concentration of 1000 nM . This combination of concentrations resulted in the lowest Ct values when amplifying 2 μl of A . ceylanicum genomic DNA at a concentration of 1 ng/μl . Assay specificity testing failed to amplify purified genomic DNA from the STH parasites A . duodenale , N . americanus , T . trichiura , S . stercoralis , A . lumbricoides and A . caninum . Testing also failed to amplify human DNA or DNA from the common gastrointestinal bacteria , E . coli ( strain K-12 ) , thus demonstrating the assay’s species-specific detection properties . Furthermore , assay sensitivity testing demonstrated consistent detection of purified A . ceylanicum genomic DNA template at all quantities above 200 ag . However , when the A . ceylanicum assay was utilized to analyze samples containing simulated mixed infections of both A . ceylanicum and other Ancylostoma species , the limit of detection was determined to be 13 . 3 fg of A . ceylanicum DNA ( S1 Table ) . Importantly , based upon the genome size of A . ceylanicum [29] , and assuming that a single egg contains approximately 8 cells , this quantity of template DNA is less than the quantity expected to be found within a single A . ceylanicum egg ( theoretically 5520 fg of DNA based on A . ceylanicum’s genome size ) . To further validate this novel assay , 61 DNA extracts from stool samples collected in Timor-Leste were analyzed . Utilizing a genus-specific real-time PCR assay , previous testing conducted at QIMR Berghofer demonstrated the presence of Ancylostoma ssp . DNA in 22 of these samples [27] . However , follow-up testing of these samples utilizing our previously described A . duodenale-specific primer/probe set excluded the presence of A . duodenale [25] , and semi-nested PCR-RFLP analysis demonstrated the presence of A . ceylanicum in 21 of the 22 samples analyzed [25] . The identity of the 22nd sample could not be determined as this sample failed to amplify using both our A . duodenale-specific assays and the semi-nested PCR-RFLP analysis . Two independent sequencing trials were also performed , but meaningful results could not be obtained . These experiments exhausted the sample stock and so no further experiments could be done to identify this sample . This subset of 22 Ancylostoma ssp . -positive samples was then employed to demonstrate the comparative sensitivity and species-specificity of our newly designed A . ceylanicum assay . Utilizing this assay , the same 21 samples which were determined to be A . ceylanicum-positive by RFLP-PCR again tested positive for the presence of A . ceylanicum DNA , while the 22nd sample ( PCR-RFLP-negative for A . ceylanicum ) was also negative by real-time PCR , likely indicating that the Ancylostoma genus-specific assay ( QIMR ) had amplified a non-duodenale , non-ceylanicum Ancylostoma target ( S2 Table ) . Equally important , the remaining 39 Timor-Leste-derived samples that tested negative for Ancylostoma spp . using the previously described genus-specific PCR assay [27] also produced a negative result when tested using the newly described A . ceylanicum-specific diagnostic ( S2 Table ) . Thus , negative results agreed across all samples , in all replicates , with all utilized assays . While testing of field-collected samples from Timor-Leste demonstrated the ability of our new assay to detect DNA from A . ceylanicum , we next sought to demonstrate that this A . ceylanicum real-time PCR assay would not amplify DNA from A . duodenale-containing field samples . For this purpose , eight additional DNA extracts from stool samples , obtained from an A . duodenale endemic region near Orán , Argentina , were tested in duplicate . Utilizing a combination of the previously described A . duodenale-specific PCR [25] and the semi-nested PCR-RFLP assays [23] , we verified the presence of A . duodenale and the absence of A . ceylanicum within each sample ( S1 Fig , S3 Table ) . Individual samples were then tested using the newly described A . ceylanicum-specific PCR assay , and for all samples , in all replicates , amplification failed to occur . Thus , by selectively amplifying A . ceylanicum-containing samples from Timor-Leste , but failing to amplify Argentinian samples positive for A . duodenale , we successfully demonstrated the species-specific nature of the A . ceylanicum PCR assay on human samples collected in the field . Table 2 summarizes the results of the various molecular approaches used to determine the species of Ancylostoma within samples from both field studies .
Highlighting the under-recognized importance of A . ceylanicum as a public health concern , a study in northern Cambodia recently implicated A . ceylanicum as the causative agent responsible for as many as half of all human hookworm infections among the young adult population [6] . However , despite such prevalence , zoonotic parasites are typically given little consideration during the implementation of mass deworming efforts . Accordingly , increased awareness of the potential impact of A . ceylanicum infection on the human population is required , and more sensitive tools are needed for monitoring this important zoonotic STH . Such tools will provide a more complete picture of the role such zoonotic parasites play in global health . While current treatments for human hookworm infection are not species-dependent , it has been demonstrated that responses to drug treatments can vary in a species-dependent manner [30] . Because species-specific diagnostics are lacking , misidentification of the infecting species may lead to a lack of clarity in understanding drug-responses in infected populations . While benzimidazole-based interventions have not yet resulted in the reporting of any such species-specific responses , such responses may develop , or they may simply be underreported . As A . duodenale and A . ceylanicum are both capable of causing significant human infection , and A . caninum has the capacity to infect humans in an “unsuccessful” manner [23 , 31] , species-specific diagnostic assays are needed to fully evaluate the progress of drug intervention programs . Furthermore , given the increasing concerns surrounding the possible development of drug resistance [32–34] , species-level knowledge of infection is critical , as it is likely that different hookworm species may evolve resistance via different mechanisms and at different rates . The importance of species-level identification is further supported by the phenomenon of refugia , as the presence of an untreated animal reservoir may slow the evolutionary pressures on a pathogen , in turn slowing the development of drug resistance mechanisms [32 , 34] . While the A . ceylanicum-specific assay described here has potential usefulness as a tool for monitoring veterinary infections , its specificity and performance in such settings has not yet been evaluated . In order to evaluate such applications , additional testing and validation of this assay against common animal hookworm species such as Ancylostoma braziliense , Ancylostoma tubaeforme , and Uncinaria stenocephala , as well as other common veterinary intestinal helminths would be required . Similarly , employment of this assay for the purposes of environmental monitoring would require , at a minimum , testing against common free-living nematode species . Given the importance of the accurate , species-level identification of STH infections , we believe that the real-time PCR-based assay for the detection of A . ceylanicum described here , in combination with our previously described real-time PCR assays , will aid future hookworm monitoring efforts . With the demonstrated capacity to detect DNA isolated from a single egg , this assay provides a sensitive and species-specific diagnostic tool capable of more fully informing program managers , enabling more appropriate decision-making and allowing for improved programmatic outcomes . Finally , it should be noted that this A . ceylanicum PCR assay is easily integrated into our current multi-parallel PCR system [25] to provide a more complete picture of the STH infection status in studied populations . | Historically , Ancylostoma ceylanicum has been viewed as an uncommon cause of human hookworm infection , with minimal public health importance . However , recent reports have indicated that this zoonotic hookworm causes a much greater incidence of infection within certain human populations than was previously believed . Current methods for the species-level detection of A . ceylanicum rely on techniques that involve conventional PCR accompanied by restriction enzyme digestions . These PCR-based assays are not only laborious but they lack sensitivity as they target suboptimal regions on the DNA . As efforts aimed at the eradication of hookworm disease have grown substantially over the last decade , the need for sensitive and specific tools to monitor and evaluate programmatic successes has correspondingly escalated . Since a growing body of evidence suggests that patient responses to drug treatment can vary based upon the species of hookworm that is causing infection , accurate species-level diagnostics are advantageous . Accordingly , the novel real-time PCR-based assay described here provides a sensitive , species-specific diagnostic tool that will facilitate the accurate mapping of disease endemicity and will aid in the evaluation of progress of programmatic deworming efforts . | [
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] | 2017 | A novel, species-specific, real-time PCR assay for the detection of the emerging zoonotic parasite Ancylostoma ceylanicum in human stool |
Schistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries . Biomphalaria straminea , an intermediate host for Schistosoma mansoni , is native to the southeastern part of South America and has established in other regions of South America , Central America and southern China during the last decades . S . mansoni is endemic in Africa , the Middle East , South America and the Caribbean . Knowledge of the potential global distribution of this snail is essential for risk assessment , monitoring , disease prevention and control . A comprehensive database of cross-continental occurrence for B . straminea was compiled to construct ecological models . We used several approaches to investigate the distribution of B . straminea , including direct comparison of climatic conditions , principal component analysis and niche overlap analyses to detect niche shifts . We also investigated the impacts of bioclimatic and human factors , and then used the bioclimatic and footprint layers to predict the potential distribution of B . straminea at global scale . We detected niche shifts accompanying the invasions of B . straminea in the Americas and China . The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low . Annual mean temperature , isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B . straminea . Additionally , human factors improved the model prediction ( P<0 . 001 ) . Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions , including South America , Central America , Sub-Saharan Africa and Southeast Asia . Our results confirmed that niche shifts took place in the invasions of B . straminea , in which bioclimatic and human factors played an important role . Our model predicted the global distribution of B . straminea based on habitat suitability , which would help for prioritizing monitoring and management efforts for B . straminea control in the context of ongoing climate change and human disturbances .
Invasive species can often pose threats to the ecosystem functioning and biodiversity at the global scale , especially when they spread diseases[1] . There are a growing number of studies conducted for risk assessment , monitoring and management of invasive species and reduction of negative impacts . For many invasive species , however , once they are established over large areas , their eradication or removal can be an impossible task[2] . The prevention of introduction and establishment is therefore thought to be the most cost-effective way of mitigating future negative consequences[3 , 4] . An important approach to prevention is predicting species with invasive tendency and areas vulnerable to their invasion , which then can guide early detection and rapid response efforts against invasive species[4–7] . B . straminea ( Dunker , 1848 ) is a freshwater snail in the family Planorbidae , originated from the southeastern part of South America[8] . It is a highly invasive and competitive species given its capacity to survive during the periods of drought and its great fertility[8–10] . During the last decades , free-ranging populations of B . straminea have been reported in peripheral countries including Paraguay[11] , Argentina[12] , Uruguay in 1987[13] , Colombia in 1966[14] and Costa Rica in 1976[15] . In the Caribbean area , its introduction has been documented in several islands of the Lesser Antilles , namely Martinique around 1950[16] , Grenada in 1970[17] , Guadeloupe in 1985[18] and St Lucia in 1992[19] . Over the same period , the snail has invaded several new states in Brazil and replaced other Biomphalaria species following its introduction[20] . In addition , B . straminea is noted for its long-distance dispersion and establishment in Hong Kong in 1974 , on the Pearl River Delta of China[21] . The snail subsequently dispersed to different water habitats in adjacent cities in Guangdong Province of southern China , including Shenzhen , Dongguan and Huizhou[22] . B . straminea is an intermediate host of Schistosoma mansoni[9 , 23 , 24] , and is one of the three species found to be naturally infected with S . mansoni in Brazil[8] . S . mansoni is a snail-bone parasitic disease , prevalent predominantly in Africa , the Middle East , the Caribbean , Brazil , Venezuela and Suriname[25] . China started several aid programs in African countries in the 1970s . Since then , imported cases of S . mansoni from Africa have been increasing , which has captured much attention from public health officials[25] . The existence of imported patients and its intermediate host is the prerequisite of transmission of S . mansoni in China . Moreover , the increasing amount of logistics and human flows induced by the Belt and Road Initiative would put China at a greater risk of the disease[26] . Furthermore , global warming is thought to change the current habitats of B . straminea , thereby affecting the original landscape of schistosomiasis[25] . B . straminea was also found to precede the common snails as a carrier of Angiostrongylus cantonensis , an important neurotropic pathogen of human angiostrongyliasis , under laboratory conditions . [27] The study of potential distribution and suitable habitats of this snail are therefore of particular importance for global health . Ecological niche modeling is increasingly used to predict the distributions of species and vector-borne diseases[28–32] . This modeling method can not only predict distributional ranges , but also identify what particular combination of environmental variables shapes a species’ distribution[4 , 29] . Previous studies have utilized ecological models to predict the spatial distribution of B . straminea at state and national scales in Brazil[9 , 24] . There is only one study that built the prediction map in China based on the occurrence data in Shenzhen city[33] . However , species distribution models , which do not incorporate data from both the native and introduced ranges , likely misrepresent the potential distribution of invasive species , especially under projected climate change scenarios[34] . The aim of this study was threefold . First , we compared the niches of the native and introduced populations to assess whether niche shifts occurred in the invasion of B . straminea . Second , we investigated the impacts of bioclimatic and human factors in the process of invasion . Third , we used pooled data from both the native and introduced ranges to predict the potential distribution of B . straminea at a global scale .
We obtained occurrence records from a comprehensive literature review ( S1 Table ) , the Global Biodiversity Information Facility database ( GBIF , http://www . gbif . org/ , last accessed December 2016 ) , and results from our own malacological surveys . All available location information was extracted for each occurrence . We georeferenced records from literature and GBIF that had only the administrative region using Google Maps ( http://www . google . cn/maps ) , Google Earth ( https://www . google . com/earth/ ) , or simple Google searches . We overlaid the geolocated occurrence points with a raster layer that distinguished land from water . Any records that were positioned outside the land area were subsequently removed from the database . Malacological surveys were conducted during the period from 2012 to 2016 in the river systems of cities ( Guangzhou , Shenzhen , Dongguan and Huizhou ) adjoining Hong Kong , in which the first introduction of B . straminea in China was reported . Sampling was carried out by two trained field investigators using standard snail scoops . At each site , the investigators collected any Biomphalaria snails found in a radius of approximately 2 m over a permitted search time of 30min . The coordinates of each sampling sites were recorded with the help of a handheld geographical positioning systems ( GPS ) device ( Trimble Navigation Co . , Ltd . ) . Collected snails were appropriately labeled , transported to the laboratory and identified using the morphological approach . Key characters were shape of the shells and number of the prostate diverticula as previously described[8] . To minimize the effect of sampling bias , we retained only one occurrence point per 2 . 5 arc-min resolution grid ( a 5×5 km area ) [30 , 35 , 36] . The final data set included 1312 occurrence points ( 1262 from the native range , 19 from introduced ranges in the Americas and 31 from China ) ( Fig 1 ) . We obtained 19 bioclimatic variables with a spatial resolution of 2 . 5 arc-min from WorldClim version 1 . 4[37] . The bioclimatic variables represent annual trends , seasonality and extreme environmental factors . They have been widely applied to model the ecological niche and potential distribution of species[30 , 35 , 38 , 39] . We also included the global human footprint layer version 2 ( Wildlife Conservation Society ( WCS ) , & Center for International Earth Science Information Network ( CIESIN ) ) in our model to evaluate the correlation between anthropogenic influences and the distribution of the introduced occurrences . The human footprint layer measures the human influence on global surface , combining data sets representing human population density , land transformation , human access , and presence of infrastructures[38 , 40] . We used the same resolution for this layer as for the bioclimatic variables . To reduce the effects of overparameterization and multicollinearity of predictors , we calculated the Pearson’s correlation coefficient for each pairwise comparison for all 19 bioclimatic variables and the human footprint , and excluded variables with a high intercorrelation ( r > 0 . 90 ) ( S1 Fig ) . The final environmental data set included 13 variables ( S2 Table ) . We applied three different methods to test the differences in the variables of bioclimatic environments at the occurrences between the native and introduced regions . First , we extracted the values of predictors for each occurrence and used the Kruskal-Wallis test to compare the pairwise differences in the distribution of each variable between the three distributional records . P-values were Bonferroni-corrected to avoid false significant differences . Second , we employed principal component analysis ( PCA ) to compare the position of occurrences from the native and invaded ranges in the bioclimatic space[30 , 41] . Third , we calculated the Schoener’s index for niche overlap ( D ) for each pair of occurrences of B . straminea snails . D ranges from 0 ( no overlap ) to 1 ( identical ) . We then used niche equivalence and similarity tests that rely on the metric D to detect niche shifts[42 , 43] . We used a buffer of 500 km around each known occurrence , which would provide better model predictions[44] . Computations of D , niche similarity and equivalence were performed using the ENMTools package in R . Modeling was conducted using Maxent ( 3 . 3 . 3k , http://biodiversityinformatics . amnh . org/open_source/maxent/ ) , which is a widely used machine learning algorithm that estimates the species’ probability distribution of maximum entropy , constrained by incomplete information about the species’ distribution and the environmental factors[29] . Maxent generally has a better performance for presence-only data sets and is particularly robust at small sample sizes in comparison with other species distribution models[28 , 45 , 46] . To test the contribution of human impacts on the distribution of B . straminea , we built two models for all occurrences using twelve bioclimatic layers with and without the human footprint layer . Default settings were used when not otherwise stated . Each model was replicated ten times so that results were summarized as an average of the ten models . Occurrence data were randomly split into a training subset ( 75% ) and a test one ( 25% ) . Subsequently , model averages were projected on a global scale under current climatic conditions . Prediction maps were generated using the tmap package in R . We used two metrics to evaluate the model prediction performances: the area under the curve of the receiver operating characteristic ( AUC ) and the sample size corrected Akaike’s Information Criterion ( AICc ) . The AUC uses presence and absence records to assess model predictive performance across a range of thresholds . The AUC ranges from 0 to 1 , where a score of 0 . 7–0 . 8 is thought to be an acceptable prediction , 0 . 8–0 . 9 is good and >0 . 9 is excellent[28] . The AICc can outperform AUC as a model selection criteria , particularly when sample sizes are small[47] . A lower AICc value indicates better model fits . We then compared the means of the AUC and AICc values using t-test to assess whether the human footprint could increase the predictive ability significantly . We also evaluated the relative contribution of each variable to the model .
Fig 2 summarizes the pairwise comparisons of bioclimatic conditions for B . straminea . For the native and introduced occurrences in the Americas , only annual mean temperature ( bio1 ) was lower in the introduced range than in the native range whereas differences of the other temperature-related variables were not significant . The introduced range had higher annual precipitation ( bio12 ) , precipitation of driest period ( bio14 ) , precipitation of warmest quarter ( bio18 ) and human influence than the native range but lower precipitation seasonality ( bio15 ) . Annual mean temperature ( bio1 ) was lower in the introduced range in China than in the native range with lower isothermality ( bio3 ) and mean diurnal temperature range ( bio2 ) . The introduced range in China had higher temperature seasonality ( bio4 ) and higher temperatures in the warmest period ( bio5 ) and in the wettest quarter ( bio8 ) than the native range . Anthropogenic impacts on the environment were higher in the introduced region in China with higher annual precipitation ( bio12 ) and precipitations in all seasons except the coldest quarter ( bio13 , bio14 , bio18 ) . Annual mean temperature ( bio1 ) and isothermality were lower in the introduced range in China than in the introduced range in the Americas . The introduced range in China had higher temperature seasonality ( bio4 ) and higher temperatures in the warmest period ( bio5 ) and in the wettest quarter ( bio8 ) than the introduced range in the Americas . The introduced ranges in China and the Americas also differed in all the precipitation requirements ( bio12-15 , bio18 and bio19 ) , but seemed similar in human impacts on the environment . Principal component analysis of the predictors revealed two significant axes of climatic variation , which explained 54 . 32% of the total variance . The first principal component ( PCA1 ) was closely related to both temperature and precipitation while the second one was mostly associated with precipitation ( Fig 3 , Table 1 ) . The environmental space occupied by the introduced population of B . straminea in the Americas ( red triangles ) largely overlapped with that of the native occurrences ( blue dots ) ( Fig 3 ) . The niche shift of the introduced American population ( red triangles ) occurred principally along axis 1 , indicating different component weights of each variable as the underlying gradient of niche differentiation . The pattern of the introduced population in China was more obvious , as they ( green squares ) clustered together and appeared isolated from both the native and nonnative American occurrences ( Fig 3 ) . Human impacts appeared to be an important factor for the occurrences in China , as the China cluster shifted roughly along the direction of human footprint variable ( Fig 3 ) . All occurrence pairs showed very limited levels of niche overlap ( Schoener’s D between 0 . 03–0 . 17 ) , with the highest overlap found between the native range and the introduced range in the Americas ( Table 2 ) . The null hypotheses of niche equivalency and similarity were rejected for all occurrence pairs ( P<0 . 05 ) . The ecological models performed reasonably well , with all AUC values >0 . 9 . The predictive capability was significantly higher for models with the human footprint layer ( full model ) than those without ( P < 0 . 001 ) . The AICc of the full model was significantly lower than the model with bioclimatic variables only ( P < 0 . 001 ) , indicating that the human footprint layer improved the model performance significantly ( Table 3 ) . The prediction map of the full model also showed less medium suitable areas ( light blue ) than the model without the human footprint layer ( Fig 4 ) . In China , the two models differed most in the southern regions , including Guangxi and Guangdong Provinces . In addition to southern China , the most suitable regions for the establishment of B . straminea were identified in the eastern regions of South America , Central America , western and eastern regions of Africa and Southeast Asia ( Fig 4 ) . The inclusion of the human footprint layer influenced the relative contributions and permutation importance of the bioclimatic variables ( Table 4 ) . The human footprint , isothermality ( bio3 ) and temperature seasonality ( bio4 ) had higher relative contribution in predicting the distribution of B . straminea . Annual mean temperature ( bio1 ) , isothermality ( bio3 ) and the human footprint were the most important judging by the metric of permutation importance .
Considering the distribution of B . straminea in its native range and invaded peripheral regions in the Americas , it has a broad geographical and climatic range ( Figs 1 , 2 and 3 ) . Like other invasive species , B . straminea snails have developed a variety of survival strategies to adapt to variable environments[9] . Our study has demonstrated that the B . straminea occupies significantly different ecological niches in the native region and invaded regions in the Americas and China , providing empirical evidence of the ability of this type of invasive snails to repeatedly acclimate to new environmental conditions . Such capability may be a fundamental basis for a successful invasion of exotic species[30] . The shifts were significant for the two groups of invaded occurrences in the Americas and China . Although for these two ranges , there were lack of evidence for niche identity and similarity , the climatic niche shifted towards a similar direction ( Fig 2 ) . There are potential mechanisms for the observed niche shifts in our study , which include: 1 ) snail breeding conditions in the invaded range that are not accessible in the native range due to geographical dispersal limitations; 2 ) a biotic release from native predators , competitors and pathogens or diseases in the novel habitats; and 3 ) a rapid evolution accompanied the invasion , which may allow them to colonize new environments[48–50] . Unfortunately , disentangling the relative roles of these mechanisms is not possible based on our data . Further field and experimental studies are required to clarify the mechanisms behind the climatic niche shifts for B . straminea invasions . Our results indicated that the annual mean temperature , isothermality and temperature seasonality were the most important climatic features for the occurrence of B . straminea . The snail can be found in a wide variety of shallow aquatic habitats with little water velocity , such as small pools , lakes , streams , irrigation channels and flooded areas[8] . Fluctuations of water temperature may have a direct effect on the growth of juvenile snails as well as the reproduction and survival of adults[8 , 51] . They keep active at a temperature of 18–32 °C while the optimum temperature for their development is 20–26 °C[51] . In some permanent habitats with compatible temperature , B . straminea reproduces throughout the year; in others with short window of suitable temperature , only a single generation is produced each year[51] . In addition , temperature is associated with the production and release of schistosome cercariae[8] . The climate of the native and the invaded Caribbean area and Colombia is tropical but the other invaded regions , including Paraguay , Argentina , Uruguay , Hong Kong and coastal mainland China , have a subtropical climate characterized by humid , hot summers and relatively dry and mild winters . Evidently , B . straminea has expanded its domain to regions with lower annual temperature partially facilitated by its ability to survive adverse environmental conditions , e . g . lower or higher temperature and desiccation , in the mud[51] . Annual precipitation also played a role in the occurring of B . straminea , which was consistent with a recent survey that found a positive correlation between precipitation and B . straminea abundance[52] . Inclusion of the human footprint layer enhanced the accuracy of the ecological niche model , indicating that human activities can contribute to the spreading of introduced B . straminea . Our finding supported the association between human-intervened environmental changes and distribution of schistosomiasis and its intermediate hosts[53 , 54] . The development and management of water resources is thought to be an important risk factor for schistosomiasis transmission[55] . Human activities can affect the biological invasions by changing the spreading routes of the species and creating suitable microclimates in regions with suboptimal climates[56] . Non-native species tend to colonize man-made suitable habitats first and move on to natural environments[57] . The first introduction of B . straminea into Hong Kong was thought to be associated with imported tropical aquarium plants or fish from South America[22 , 58] , which was reconfirmed by a recent phylogenetic analysis[59] . Subsequent colonization occurred in large outdoor concrete breeding ponds for tropical aquarium fish near the border with mainland China[60] . This snail was first found in several ponds , ditches and rivers in Shenzhen of China , and spread further in the Shenzhen River system[58] . Shenzhen was one of the fastest-growing cities in the world and has undergone magnificent landscape reconstructions , which may lead to dispersion of the snails to surrounding cities[61] . A survey conducted in 2012–2013 noted that this snail had established in several waterways in Dongguan and Huizhou[61] . Therefore , environmental assessment is advised for infrastructure projects , especially water conservancy schemes , that might transport B . straminea to novel habitats . According to our model , B . straminea showed a variable range of suitable areas in the tropical and subtropical regions including continental and coastal areas and islands . The most suitable regions for invasion covered Central America , Sub-Saharan Africa and Southeast Asia ( Fig 4 ) . B . straminea is present in several countries in Central America , but its role as the intermediate host of S . mansoni has never been confirmed in these countries[11] . Africa is home to other intermediate hosts of S . mansoni , including B . pfeifferi , B . alexandrina and B . sudanica[62] . Although this continent has no evidence so far for the presence of B . straminea snails , our predicted suitable habitats largely overlap with the distribution of S . mansoni in Africa[63] . Once the B . straminea was introduced and successfully established in these regions , it might complicate the life cycle of S . mansoni and increase the burden of schistosomiasis control for African countries . Therefore , in African regions efforts should be mainly directed to monitoring water bodies for early signs of an invasion by global trade and transport . Identified suitable areas in China were constrained to tropical and subtropical regions . However , the low suitability of some areas does not mean that B . straminea can be introduced without any risk of invasion since these snails could breed in suitable artificial microhabitats . Moreover , the ongoing global climate change , in particular global warming , can modify the suitability and cause an expansion of suitable areas towards higher latitude[25] . The outbreak of urogenital schistosomiasis in Corsica , France , sounded the alarm and suggested that the transmission cycle of schistosomiasis can be completed upon the encounter between the intermediate host snails and imported individuals infected the parasite[64] . Since the 1970s , imported cases of S . mansoni or S . haematobium has been repeatedly reported among migrant workers , who are at high risk of African schistosome infections because of frequent contact with infested water . Few of the individuals infected with S . mansoni , usually characterized by only mild or no symptoms , seek medical help and they may be misdiagnosed for the reason that Chinese clinicians lack knowledge of the diagnosis of this disease . The priorities of China’s health authorities are to monitor and block further spread of B . straminea as well as to develop strategies to reduce the imported cases of S . mansoni from endemic areas . In conclusion , our study showed that the environmental spaces of the introduced B . straminea populations in the Americas and China shifted compared to that of their native counterparts . This snail has acclimated to parts of the subtropics with lower annual mean temperature . Incorporation of anthropogenic factors improved niche model prediction in areas of high human disturbance . Our final model predicted large suitable areas in the tropics and subtropics , indicating that B . straminea snail has a significant potential to spread further as nonnative species . Therefore , it is important to impose strict monitoring and surveillance of new invasion of B . straminea in areas at high risk . | Biomphalaria straminea is an intermediate host of Schistosoma mansoni . This snail has not only established in peripheral countries but also survived in different water habitats in Hong Kong and adjacent cities of mainland China . Ecological niche models were used to predict the potential global distribution of B . straminea . Our results showed that there were niche shifts in the process of invasion for B . straminea in the Americas and China . This snail has expanded its habitats to subtropical regions with lower annual mean temperature . Annual mean temperature , isothermality , temperature seasonality and human influence were identified as most important climatic features for the occurrence of B . straminea . Under current climate conditions the distribution of this snail should be mostly confined to the tropic and subtropic regions , including South America , Central America , Sub-Saharan Africa and Southeast Asia . With the rapid globalization and the continued burden of imported cases of S . mansoni to non-endemic countries , predicting the potential distribution of the intermediate host and its drivers is increasing in importance for designing control strategies and optimizing use of limited public health resources . Prioritizing surveillance and control efforts to high-traffic regions with high habitat suitability may be the most effective approach . | [
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] | 2018 | Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni |
Food webs , networks of feeding relationships in an ecosystem , provide fundamental insights into mechanisms that determine ecosystem stability and persistence . A standard approach in food-web analysis , and network analysis in general , has been to identify compartments , or modules , defined by many links within compartments and few links between them . This approach can identify large habitat boundaries in the network but may fail to identify other important structures . Empirical analyses of food webs have been further limited by low-resolution data for primary producers . In this paper , we present a Bayesian computational method for identifying group structure using a flexible definition that can describe both functional trophic roles and standard compartments . We apply this method to a newly compiled plant-mammal food web from the Serengeti ecosystem that includes high taxonomic resolution at the plant level , allowing a simultaneous examination of the signature of both habitat and trophic roles in network structure . We find that groups at the plant level reflect habitat structure , coupled at higher trophic levels by groups of herbivores , which are in turn coupled by carnivore groups . Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial pattern , in contrast to the standard compartments typically identified . The network topology supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence . Furthermore , our Bayesian approach provides a powerful , flexible framework for the study of network structure , and we believe it will prove instrumental in a variety of biological contexts .
Food webs , networks of feeding relationships in ecosystems , connect the biotic interactions among organisms with energy flows , thus linking together population dynamics , ecosystem function , and network topology . Ecologists have been using this powerful conceptual tool for more than a century [1]–[3] . One particularly relevant aspect of food webs is the subdivision of species into compartments or modules , a feature that has been proposed to contribute to food web stability by constraining the propagation of disturbances through a network [4] . In this definition , compartments are alternately referred to as modules , clusters , or “communities” [5] , and are defined by high link density within groups and low link density between them . A large literature has considered the presence of compartments of food webs , with early work concluding that compartmentalization results primarily from habitat boundaries , not from dynamical effects [6] , although continuing theoretical work has shown that compartmentalization can affect stability [7] , [8] . One recent study shows that niche structure can result in compartmentalization [9] , but the relationship between compartments and spatial habitat structure remains the strongest empirical pattern identified [10] , [11] . Although compartmental structure may be significant at one scale of analysis , compartments alone do not account for much of the topological structure in food webs . Recent work with a probabilistic model considers a more flexible notion of groups , allowing link density to be high or low within any group or between any pair of groups [12] . Groups can thus represent compartments in the previous sense , but can also represent trophic guilds or roles [13] , [14] , sets of species that feed on , and are fed on , by similar sets of species . By fitting models of this type to data , the dominant topological pattern in the network can be found , which may include compartments , trophic guilds , or some combination of the two . The initial application of this model to empirical food webs from different ecosystems has revealed a predominance of trophic guilds rather than compartments [12] . Two major challenges limit the application of this model in resolving the group structure of food webs and interpreting its biological basis . First , most food webs have poor resolution of primary producers; plants in terrestrial systems and phytoplankton in aquatic ones are typically represented by a few nodes that are highly aggregated taxonomically . Some are aggregated at multiple trophic levels , e . g . , the Coachella Valley web [15]; others aggregate only the primary producers , e . g . , the El Verde rainforest [16] , which identifies basal taxa as categories of plant parts . Another recently published Serengeti food web includes highly aggregated primary producers and varying levels of aggregation at other trophic levels [17] . Some webs that do include high resolution of plants include plant-herbivore bipartite networks , notably one lowland food-web from Papua New Guinea [18] , and plant-insect-parasitoid “source webs” [19] , [20] . Because primary producers form the base of the food web , high resolution in those groups can facilitate a much better understanding of how spatial organization and habitat type percolate up the web , and how higher trophic levels cut across the habitat structure at lower levels . Second , some technical problems have hindered the use of probabilistic models in analyzing group structure . Early food web models served as null models for food web structure and were tested by generating model webs and comparing summary statistics against data from real webs [21] , [22] . More recently , a more rigorous approach for measuring the goodness of fit of a model has been provided by maximum likelihood and model selection [12] , [23] . Two problems still remain within this framework . One is technical: standard model-selection criteria are not applicable to “discrete parameters” such as group membership . The second problem is more fundamental: there are many almost equally good arrangements , and it is desirable to extract information not just from a single best arrangement , but also from the rest of the ensemble . The Bayesian approach is gaining popularity in ecological modeling due to the philosophical and conceptual appeal of explicitly considering uncertainty in parameter estimation as well as its methodological flexibility [24] . This approach is especially well-suited for handling uncertainty in complex food web models , and allows us to overcome the limitations of the previous implementation of the group model . In network inference , there are only a few examples of complete Bayesian models [25] , [26] and a few examples of MCMC for maximum-likelihood inference [27] , [28] , but Bayesian inference in phylogenetics has been long established [29] , [30] , and provides a clear methodological analogue . In this paper , we address the group structure of a newly assembled food web for the large mammals and plants of the Serengeti grassland ecosystem of Tanzania by applying a computational approach to the identification of groups based on Bayesian inference . We specifically ask whether the structure that emerges reflects the underlying spatial dimension , as delineated by the different plant communities that characterize different sub-habitats within the ecosystem , or whether it is determined by trophic dimensions in the form of species guilds that share functional roles . The Serengeti has been studied as an integrated ecosystem for almost five decades [31]–[33] , and because of widespread popular familiarity with the consumer-resource dynamics of lions , hyenas , wildebeest , zebra and grasses , it provides a strong intuitive test for probabilistic food web models . Furthermore , all the primary producers in this Serengeti web are identified to the genus or species level . The plant diversity encompasses a number of distinct grass , herb , and woody plant communities on different soils and across a rainfall gradient [34] . This well-documented structure allows us to examine the extent to which habitat structure defines network topology at multiple trophic levels . Although not yet a comprehensive community web , with the addition of more taxa , such as those in another recently published Serengeti web [17] , this data set can become the most highly-resolved terrestrial web available .
We compiled the Serengeti food web from published accounts of feeding links in the literature [34]–[47] along with some links known from personal observation . With a few exceptions , the taxa included are large mammalian carnivores and herbivores and the plant diets of the herbivores . In its current form it is not a comprehensive community web , nor does such a terrestrial web yet exist . Another recently published Serengeti food web is largely complementary , containing many bird , mammal , and invertebrate species not included here , but without high resolution of plants [17] . We have not included invertebrates ( insects and parasitic helminths ) or birds , but are adding data for these groups for future studies . The compiled food web ( Tables S1 and S2 ) consists of 592 feeding links among 161 species ( 129 plants , 23 herbivores , and 9 carnivores ) . 507 of the links are herbivorous , and 85 are predatory . The fraction of all possible links ( connectance , ) , ignoring all biological constraints , is equal to 0 . 023 . We attribute the low connectance , as compared to other existing food-web data sets , to the high taxonomic resolution of the plant community . We compared marginal likelihood estimates of different model variants to determine which one best describes the Serengeti food web ( see Methods ) . First , we find unequivocal support for the use of group-based models in describing the Serengeti food web , as compared with simple null models that ignore group structure , either by treating each species as its own group or by combining all species into a single group ( Table 1 ) . We also find that a flexible group model that allows for high or low connectance between and within groups vastly outperforms a compartmental model that restricts between-group connectance to be lower than within-group connectance , with a posterior odds ratio ( Bayes factor ) of against the compartmental model . Additionally , the use of flexible priors vastly improves the fit of the basic model , for both link probability parameters and network partitions . The model variant with beta prior for link probabilities and Dirichlet process prior for partitions performed best . Next , in order , were ( 1 ) the model with beta link probability prior and uniform partition prior , ( 2 ) the model with uniform link probability prior and Dirichlet process partition prior , and ( 3 ) the model with both uniform priors . The strongest variant surpassed its closest competitior by 133 units of ( natural ) log-likelihood , corresponding to a posterior odds ratio of against the worse one , and surpassed the model with both uniform priors by 439 units of log-likelihood , a posterior odds ratio of . In all cases , 95% confidence intervals on the marginal likelihood estimates were less than one unit of log-likelihood , far less than the differences between models . Given this unequivocal support , we consider results only from the best model variant . We used samples from the posterior distribution to summarize model hyperparameters controlling link probabilities and partitions . The posterior mean number of groups is ( 95% credible interval ) , and the mean value of the Dirichlet process parameter is ( ) ( Figure 1 ) . The prior expectation of was 1 . 0 and the prior expectation of was . The finding of posterior values substantially greater than prior values strongly supports the presence of detailed group structure in the Serengeti food web . Mean values for beta distribution parameters are ( ) and ( ) ( Figure S2 ) . The corresponding beta prior has support concentrated near 0 , since most species do not feed on most other species ( Figure S3 ) . The posterior output includes 30 , 000 partitions of the Serengeti food web into groups , nearly all distinct from each other . One partition appears 6 times; two partitions appear 3 times; 14 partitions appear 2 times , and the rest appear only once . For the sake of interpretation , we formed a consensus partition ( Table S3 ) of 14 groups from the affinity matrix ( Figure 2 ) , which represents the fraction of partitions in all posterior samples in which pairs of species appear in the same group . On average , the consensus partition differs from sampled partitions by 5 . 6% , calculated as the fraction of species pairs that are assigned to the same group in one partition but to different groups in the other . By comparison , on average , individual sampled partitions differ from other sampled partitions by 7 . 9% . In addition , every sampled partition differs on average from the others by more than the consensus partition does , indicating the value of the consensus approach . The groups identified in the Serengeti food web in the consensus partition contain trophically similar species , with all groups restricted to a single trophic level ( plants , herbivores , or carnivores ) . The consensus partition , with 14 groups , is shown in Table 2 . The partition includes 2 groups of carnivores ( groups 1–2 ) , 4 groups of herbivores ( groups 3–6 ) , and 8 groups of plants ( groups 7–14 ) . On average , plant groups contain more species than herbivore and carnivore groups ( 16 . 1 , 5 . 8 , and 4 . 5 , respectively ) . As evident in the affinity matrix , the carnivore and herbivore groups are well-defined , including several individual species or pairs of species with distinct diets . Plant groups demonstrate mild overlap , indicating a partially hierarchical relationship between smaller groups and larger groups . Figures 3 , 4 , and S1 show three alternate views of the food web , organized by the 14 -group consensus partition . Except for carnivore group 1 , there are no connections within groups , and partitions are defined by targeted , directed connections between specific pairs of groups . For actual link densities between groups in the consensus partition , see Table S4 . Overall , plants of the same habitat type are significantly more clustered in groups than random according to weighted Shannon entropy . ( Lower values of weighted entropy indicate higher levels of clustering; see Methods . ) Mean weighted entropy across all posterior partitions is 1 . 25 , compared to a randomized mean value of 1 . 39 ( ) . Furthermore , the four largest plant groups reflect significant overrepresentation of four different habitat types , and either significant underrepresentation or no significant signal for other habitat types . In group 13 , kopje plants are significantly overrepresented , comprising 36 . 7% of the group , compared to a random expectation of 18 . 1% ( ) . Group 9 contains 60 . 4% grassland plants compared to a random expectation of 41 . 5% ( ) , and includes 40 . 4% of individual species records in the plot data . All of the identified riparian species occur in group 11 , comprising 31 . 8% of the group , compared with a 6 . 3% random expectation ( ) . Finally , woodland plants comprise 66 . 7% of group 8 , compared with a random expectation of 25 . 6% ( ) . This result holds across all individual sampled partitions in the posterior output; each one includes four different groups with significant overrepresentation of kopje , grassland , riparian , and woodland habitat . Plant groups are coupled by groups of herbivores , which are in turn coupled by groups of carnivores . Large migratory grazers ( group 4 , wildebeest , zebra , and gazelles ) feed plant groups that include the dominant grass species in the ecosystem ( group 9 ) , predominantly riparian species ( group 11 ) , and a mixture of woodland species ( Combretum molle , Digitaria diagonalis , Duosperma kilimandscharica , and others ) and less common species ( group 8 ) . Group 7 represents a specific case where very high trophic similarity brings two spatially separate plants into the same group . Hyparrhenia rufa is found mainly in the north , and is a significant component of zebra and wildebeest diets during the dry season; in contrast , Digitaria scalarum dominates much of the plains and is eaten in large amounts by migrants during the rainy season when their nutritional needs are at a maximum due to calving and lactation . However , they are grouped together because of their mutual inclusion in the diets of the migratory species . Herbivores feeding in the longer grasslands , woodlands and in riparian habitats ( group 3 ) couple groups 9 and 11 . The hyraxes ( group 5 ) and group 6 ( giraffe , elephant , buffalo , and others ) couple group 13 , which bears a strong kopje signature , to groups biased toward other habitats . At the highest trophic level , the large carnivores ( group 1 ) integrate across all the herbivore groups; smaller carnivores ( group 2 ) show more specialized diets , reflecting the more distinct habitats in which they are usually found .
In order to analyze the group structure of the Serengeti food web , we used a flexible Bayesian model of network structure that includes no biological information aside from a set of nodes representing species and links representing their interactions . The groups that emerge from an otherwise blind classification of species make remarkable biological sense , and moreover reveal detailed patterns between habitat structure and network topology that expert intuition alone cannot . Species are divided into trophic guilds that reveal a strong relationship between the habitat structure of plant , herbivore , and carnivore groups and the structure of the network . At the coarsest scale , the groups in the Serengeti food web correspond to carnivores , herbivores , and plants . The further subdivisions that emerge within the trophic levels reveal connections between habitat types and feeding structure . This deeper analysis is made possible by high resolution at the plant level along with information about the habitat occupancy of different plants . Since different habitat types occupy distinct spatial locations in the Serengeti , the group structure thus reflects in part the flow of energy up the food web from different spatial locations , with herbivores integrating spatially separated groups of plants , and carnivores integrating spatially widespread herbivores . A priori , it was not clear precisely what kind of group structure would emerge in the Serengeti web from the use of the group model . In general , the more complex the web , the more useful these methods will be in helping to disentangle the complexity . The food web presented here included only plants and mammals , but we hypothesize that the general conclusions will be largely robust to the addition of more species . Although the addition of birds , reptiles , invertebrates , and pathogens will likely add a significant number of new groups , they should not significantly modify the derived structure for mammals , since the insect-bird links reflect an almost parallel food web . To the extent that insect herbivores further differentiate plants , plant groupings may be affected , but we expect that the larger tendency for groups to reflect habitat structure will remain . Recently , interesting theoretical and empirical work has highlighted the relationship between observed patterns of food-web structure and energy flow that seemingly relates to the trophic guild structure in the Serengeti . Rooney and colleagues [48] give evidence that real ecosystems may be dominated by nested sets of fast and slow “energy channels , ” each of which represents a food chain of trophic guilds . They suggest that this pattern may have a strong stabilizing effect , based on theoretical work by McCann on spatially coupled food webs [49] . The group structure for the Serengeti web that emerges from our analysis supports a pattern of spatial coupling at multiple trophic levels: the grasslands have very high turnover rates compared to those of the kopjes and woodlands . This suggests a similar pattern of fast and slow energy channels to those described by Rooney and colleagues , with fast energy flow up through the highly seasonal but very productive grasses of the short-grass plains . These are almost completely consumed by wildebeest and zebra during their peak calving season , which are then in turn consumed by large predators ( lions and hyenas ) . Although the migratory species of wildebeest and zebra form a crucial and major component of the diet of the predatory species , their high abundance and presence in open habitat places them at a lower per capita risk of predation . In contrast , the resident herbivore species living on kopjes and in the woodlands reproduce at slower rates and are consumed at higher per capita rates by large carnivores during the time when the carnivores are unable to feed on migratory wildebeest and zebra . These patterns emerge directly from the topology of the food web without being explicitly labeled as different habitats upfront as was done in previous empirical work [48] , showing that topological analysis can reveal structures that may be very significant for food-web dynamics . They are subtly different , however , from the proposed pure fast and slow chains , in that they incorporate the migration of the keystone species in the ecosystem , so the fastest energy chain is seasonally ephemeral and may only operate for three to four months in any year . We suspect that even within the sub-habitats of kopjes and woodlands there are similarly nested faster and slower chains that involve species for which we are still collating data ( e . g . , birds , small mammals , and insects ) . More generally , we see the identification of important structures in empirical food webs via probabilistic models as important for grounding future investigations into the relationship between structure and dynamics in empirical pattern . In this paper , we used a probabilistic model to analyze the structure of a single food web , an approach we have seen in only one other study based on a probabilistic version of the niche model [28] ( see supporting text S1 for more discussion of probabilistic modeling of food webs ) . This approach has proved fruitful in Bayesian phylogenetics , where the combinatorial challenges are similar . Moreover , we view the group model as only a starting point for richer modeling efforts to help identify relevant processes that influence the structure of ecological communities . In fact , the Bayesian approach described here provides a powerful general framework for encoding hypotheses about the structure of food webs and comparing models against each other , and we see it as a natural next step in the current trend of representing food-web models in a common way . Simple abstract models such as the niche model and the group model used here act as proxies for the high-dimensional trait space that determines feeding relationships in an ecosystem . The identification of actual traits that correspond to groups ( or niche dimensions ) is another valuable direction , so far followed primarily by finding correlations between compartments/groups [11] or niche values [22] and traits such as body size or phylogenetic relatedness . A more sophisticated , rigorous approach is to directly incorporate such traits into the probabilistic models themselves , either as predictors or as informed Bayesian model priors . Although the current work does not employ that approach , the results from the habitat analysis suggest that including additional information directly in the model would be a valuable approach . The use of flexible , hierarchical priors for model parameters is another useful innovation of the Bayesian framework . The number of groups identified by the model increases dramatically with the use of a flexible beta prior distribution for link probability parameters . In that model variant , we effectively introduce two degrees of freedom to the model ( the beta distribution parameters ) but dramatically reduce the effective degrees of freedom of the link probability parameters . Note that we penalize parameters by using the marginal likelihood for model selection , so that the model selection represents a balance between goodness of fit and model complexity . Moreover , this structure makes intuitive sense: since most link probability parameters are simply zero , they should not be penalized . An alternate approach is to remove and add parameters to the model , but this hierarchical technique is much easier to implement in practice . Advanced Markov-chain Monte Carlo methods make it possible to accurately estimate marginal likelihoods for probabilistic network models . Unlike information criteria such as AIC or BIC , an accurate estimate of the marginal likelihood provides a direct measurement of goodness of fit that takes into account the degrees of freedom in a model without making any asymptotic assumptions about parameter distributions [50] , and can handle discrete parameters such as partitioning into groups that are not properly handled by AIC and BIC . Additionally , the Bayesian approach also serves as a means to avoid fundamental issues inherent in network models with a large parameter space . One recent study has shown that , even in relatively small networks , a large number of good solutions exist for the standard modularity criterion [51] , [52] . A maximization algorithm is thus guaranteed to find a single local maximum of many—possibly even the best one , but certainly not one that captures the full range of good solutions . This problem arises whether the quantity to be maximized is a heuristic such as modularity or a likelihood value . The group model and other parameter-rich models presumably suffer from similar degeneracy problems . In the present case , we find that nearly every partition sampled from the posterior distribution is unique . Although MCMC sampling cannot reproduce the full posterior distribution , it is an important step in the right direction . Philosophical arguments aside , one of the main reasons for maximizing likelihood or modularity is simply that a single solution is far more tractable than a distribution . The consensus partitioning heuristic used here is an attempt to find a single partition that represents the posterior distribution reasonably well for the sake of interpretation and presentation ( see Methods ) . More sophisticated approaches to collapsing partitions will be welcomed . However , since the Bayesian approach provides direct access to uncertainty in the form of the posterior distribution , quantitative analyses should be done across the whole distribution , and we follow that approach here . The group model , based on the simple notion that groups of species may have similar feeding relationships to other groups , reveals that trophic guilds are the topologically dominant type of group in the Serengeti food web . The model also reveals an interesting relationship between habitat structure and network structure that corroborates recent ideas on spatial coupling in food webs . A theoretical study with a dynamical model suggests that this type of structure may contribute to ‘stability’ in the sense of the persistence of species [49] . Now , by using group structures directly inferred from empirical webs , we can better guide investigations into the relationship between structure and various aspects of stability , for example robustness to secondary extinctions [53] , [54] . Although the Bayesian modeling approach is not new to network analysis in general [25] , [26] , it remains relatively rare . The Bayesian group model , and , more importantly , the general framework for modeling and model selection , naturally extend to other kinds of biological networks , such as metabolic and regulatory networks [55] and networks describing other ecological interactions such as pollination [56] . We advocate this framework as a way to build stronger ties between hypothesis formulation , model building , and data analysis .
In this work , we use Bayesian probabilistic models to analyze food webs; for a general introduction to the Bayesian modeling approach and details on the specific models used here , please see supporting text S1 . We employ a generative model based on groups that treats the food-web network , represented as the presence or absence of each possible feeding link , as data . The group model [12] , known as a stochastic block model in the statistical literature [57] , was previously treated in a maximum-likelihood framework . In a Bayesian framework , both data and model parameters are treated probabilistically , making the object of inference a posterior distribution over model parameters rather than a point estimate . For a general overview of Bayesian inference , see section 3 of supporting text S1 . The group model ( supporting text S1 , section 2 ) divides species into some number of groups , thus determining a partition . All possible links between any pair of groups are assigned the same probability of existing , , for consumer group and resource group . If a between-group link probability is close to one , then there are likely to be many links with a species from group feeding on a species from group . A highly compartmental network can be generated by having lower between-group link probabilities ( for ) than within-group link probabilities . In general , priors may incorporate informed knowledge about the system , but in this case we simply use them to encode different variants of the same basic model . We use two distributions for partitions and two distributions for link probabilities , which are combined to form four different model variants . We also consider several null models for comparison . We sample from the posterior distribution of model parameters using a Markov-chain Monte Carlo technique known as Metropolis-coupled MCMC , or [59] , which improves mixing between multiple modes of the posterior distribution , and also allows improved estimation of the marginal likelihood [60] . Software for performing MCMC sampling was implemented in Java , and is available from the corresponding author on request . A full treatment of MCMC is given in supporting text S1 , section 4 , including details on applying the method to the group model . In order to select a good model variant , we employ the marginal likelihood , the probability of data given a model integrated over all model parameters ( partitions and link probability parameters ) . This approach extends the use of Bayes' rule to model selection as well as inference of parameter values . The ratio of the marginal likelihoods for two models is often called the Bayes factor [61]–[63] , and determines the posterior odds ratio of two models given equal prior odds . For details on marginal likelihood-based model selection , see text S1 , section 5 . The output of an MCMC simulation includes a large number of network partitions representing draws from the posterior distribution . As these partitions are potentially all distinct from each other , but represent similar tendencies of species to be grouped together , it is useful to try to summarize the information contained in all the samples in a more compact form . To do this , we construct an affinity matrix with entries equal to the posterior probability that two species are grouped together . We use the affinity matrix to then form a consensus partition , using an average-linkage clustering algorithm ( see supporting text S1 , section 6 ) . The affinity matrix is akin to the co-classification matrix previously used to identify uncertainty in end-points in a simulated annealing algorithm [64] . In order to test the overall presence of habitat signature in plant groups , we assigned plants to habitat types via one of three methods based on data availability . For plants present in 133 plots sampled from around the Serengeti [65] , we assigned them to the habitat type of the plot in which they were most abundant; plot habitat types were assigned via a separately compiled map of habitat boundaries [66] . Some plants were available from a study of kopje forbs [67] . Finally , some were assigned from personal knowledge of the system . We used a randomization test to measure the overall clustering of habitat in groups across sampled partitions . The habitat signature of an individual group was measured as the Shannon entropy—low entropy indicates an uneven distribution—of the assignment of species to habitats , , where is the habitat , is the group size , and there are species assigned to habitat within the group . The overal clustering signature for a partition was the average of the individual group entropies , weighted by the size of the groups , , for total species count . The p-value for the statistic is the probability that a partition drawn from the posterior distribution has overall clustering greater than or equal to a randomized partition with groups of identical size . To test clustering significance of a specific habitat type in a specific grouping of species , we calculated the p-value as the probability that a randomized group of the same size would have as many or more species assigned to the chosen habitat type . | The relationships among organisms in an ecosystem can be described by a food web , a network representing who eats whom . Food web organization has important consequences for how populations change over time , how one species extinction can cause others , and how robustly ecosystems respond to disturbances . We present a computational method to analyze how species are organized into groups based on their interactions . We apply this method to the plant and mammal food web from the Serengeti savanna ecosystem in Tanzania , a pristine ecosystem increasingly threatened by human impacts . This web is unusually detailed , with plants identified down to individual species and corresponding habitats . Our analysis , which differs from the compartmental studies typically done in food webs , reveals that functionally distinct groups of carnivores , herbivores , and plants make up the Serengeti web , and that plant groups reflect distinct habitat types . Furthermore , since herbivore groups feed across multiple plant groups , and carnivore groups feed across multiple herbivore groups , energy represents a wider range of habitats as it flows up the web . This pattern may partly explain how the ecosystem remains in balance . Additionally , our method can be easily applied to other kinds of networks and modified to find other patterns . | [
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] | 2011 | Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model |
Establishing the genetic determinants of niche adaptation by microbial pathogens to specific hosts is important for the management and control of infectious disease . Streptococcus pyogenes is a globally prominent human-specific bacterial pathogen that secretes superantigens ( SAgs ) as ‘trademark’ virulence factors . SAgs function to force the activation of T lymphocytes through direct binding to lateral surfaces of T cell receptors and class II major histocompatibility complex ( MHC-II ) molecules . S . pyogenes invariably encodes multiple SAgs , often within putative mobile genetic elements , and although SAgs are documented virulence factors for diseases such as scarlet fever and the streptococcal toxic shock syndrome ( STSS ) , how these exotoxins contribute to the fitness and evolution of S . pyogenes is unknown . Here we show that acute infection in the nasopharynx is dependent upon both bacterial SAgs and host MHC-II molecules . S . pyogenes was rapidly cleared from the nasal cavity of wild-type C57BL/6 ( B6 ) mice , whereas infection was enhanced up to ∼10 , 000-fold in B6 mice that express human MHC-II . This phenotype required the SpeA superantigen , and vaccination with an MHC –II binding mutant toxoid of SpeA dramatically inhibited infection . Our findings indicate that streptococcal SAgs are critical for the establishment of nasopharyngeal infection , thus providing an explanation as to why S . pyogenes produces these potent toxins . This work also highlights that SAg redundancy exists to avoid host anti-SAg humoral immune responses and to potentially overcome host MHC-II polymorphisms .
S . pyogenes ( commonly known as the β-hemolytic Group A Streptococcus ) is a prominent bacterial pathogen that causes a diverse range of clinical manifestations . Globally , S . pyogenes is responsible for over 600 million cases of pharyngitis , and more than one half million deaths primarily from complications of autoimmune rheumatic heart disease and invasive infections [1] . In addition , approximately 12% of school-aged children are asymptomatic carriers of this organism [2] , and this ‘carriage’ state can last for years without the development of disease [3] . Although humans remain the only known natural reservoir for S . pyogenes , closely related streptococci such as Streptococcus canis and Streptococcus dysgalactiae lack host-specific adaptation . This indicates that the ancestor to S . pyogenes was unlikely to be human specific , and this further suggests that a primary feature in the evolution of S . pyogenes was the stringent adaptation to the human host [4] . Although morbidity associated with S . pyogenes is dependent on the ability to colonize and transmit within human populations , the molecular basis of the human specific tropism by S . pyogenes remains poorly understood . One group of ‘trademark’ virulence factors produced by S . pyogenes are the bacterial superantigens ( SAgs ) , also commonly referred to as the erythrogenic toxins or the streptococcal pyrogenic exotoxins [5] . There are at least 14 genetically distinct streptococcal SAgs [6] often encoded within mobile , or putatively mobile , genetic elements [7]–[9] . Thus , different S . pyogenes strains typically encode for distinct repertoires of multiple SAgs . These toxins function by engaging lateral surfaces of both MHC class II ( MHC-II ) molecules and T cell receptor ( TCR ) β-chains [10]; these unconventional interactions can force the activation of enormous numbers of T cells . Indeed , human MHC-II molecules are known host factors fundamental to the development of severe streptococcal disease [11]–[14] , and the ability of MHC-II to modulate severity of invasive streptococcal disease has been linked directly to SAgs [12] , [15] . Although SAgs are well recognized in the pathogenesis of scarlet fever [16] , [17] and the streptococcal toxic shock syndrome ( TSS ) [18] , [19] , in what context these toxins contribute to the fitness and life cycle of S . pyogenes is unknown . Thus , since a major biological niche for S . pyogenes is the upper respiratory tract , we hypothesized that SAgs have likely evolved to function in the context of asymptomatic nasopharyngeal colonization and/or pharyngitis , rather than in the context of severe invasive disease . Here we show that mice expressing human MHC-II molecules are highly susceptible to acute nasopharyngeal infection by S . pyogenes . Furthermore , we demonstrate that S . pyogenes MGAS8232 requires the streptococcal pyrogenic exotoxin A ( SpeA ) SAg to cause acute nasopharyngeal infection . In addition , immunization with toxoid SpeA is protective for nasopharyngeal infection by wild-type S . pyogenes MGAS8232 . This work indicates that the streptococcal SAgs play an important role in the life cycle of S . pyogenes by promoting the initial stages of infection , and that these toxins should be further considered as potential vaccine targets to prevent S . pyogenes nasopharyngeal carriage .
S . pyogenes infection of the nasopharynx in mice is a model for pharyngeal infection in humans [20] , [21] . We first evaluated the influence of human MHC-II on mouse nasopharyngeal infection by S . pyogenes . Nasal inoculation of C57BL/6 ( B6 ) mice with ∼1×108 CFU of S . pyogenes MGAS8232 resulted in very low bacterial recovery from the nasal mucosa [herein referred to as the complete nasal turbinates ( cNT ) ] at 48 h ( Figure 1A ) . However , ‘humanized’ B6 mice that express HLA-DR4 ( DR4-B6 mice ) contained ∼100-fold more CFUs of S . pyogenes compared to wild-type B6 mice , and mice that expressed HLA-DQ8 ( DQ8-B6 mice ) , or both HLA-DR4 and HLA-DQ8 ( herein referred to as HLA-B6 mice ) , contained ∼10 , 000-fold more CFUs than wild-type B6 mice ( Figure 1A ) . In a time course analysis of this acute infection model , ∼5×103 bacterial CFUs of S . pyogenes MGAS8232 were recovered at 24 h , which increased by ∼100-fold at 48 h , and were subsequently cleared after one week ( Figure 1B ) . Importantly , MGAS8232 did not become invasive as viable S . pyogenes cells were not recovered from the spleen ( Figure 1B ) or blood ( data not shown ) in HLA-B6 mice at any time point . These data reveal that human MHC-II molecules are important host factors for acute nasopharyngeal infection by S . pyogenes . To investigate the potential role of SAgs for nasopharyngeal infection in HLA-mice , we mined the genome of S . pyogenes MGAS8232 and confirmed that this strain encodes for six genetically distinct SAgs ( SpeA , SpeC , SpeG , SpeL , SpeM and SmeZ ) [22] . The mature coding region for each SAg gene was cloned , recombinant SAgs ( rSAg ) were expressed and purified , and each was shown to activate human T cells ( data not shown ) . To assess activity of the rSAgs in HLA-mice , ex vivo splenocyte activation was evaluated . B6 splenocytes demonstrated little capacity for activation by all six rSAgs as measured by mouse IL-2 production and proliferative responses ( Figure 2A ) . However , splenocytes from HLA-B6 mice showed enhanced responses to both the SpeA and SmeZ rSAgs in a dose-dependent manner ( Figure 2B ) . These data indicate that both SpeA and SmeZ function in HLA-B6 mice and suggest that SAgs encoded within S . pyogenes MGAS8232 may contribute to the ability of S . pyogenes to infect the nasopharynx of HLA-mice . In order to determine empirically if SAgs were direct contributors to the enhanced nasopharyngeal infection phenotype in HLA-mice , a series of precise , in-frame deletions were generated within the coding regions for each SAg gene in S . pyogenes MGAS8232 ( Figure 3A ) . As speL and speM are encoded in tandem , these SAgs were deleted together . In addition , we generated a complete SAg deletion strain lacking the coding regions for all six SAg genes ( MGAS8232 ΔSAg ) ( Figure 3A ) . Each of the S . pyogenes deletion strains grew comparably to wild-type MGAS8232 in vitro ( Figure 3B ) . Furthermore , the various mutants did not show any alterations in protease ( SpeB ) activity ( Figure 3C ) . To evaluate SAg production in these strains in vitro , we generated rabbit polyclonal antibodies to each rSAg . The specific rabbit antisera recognized each of the expected SAgs without cross-reacting with others ( Figure 3D ) . Next , we assessed in vitro production of the SAg proteins for the wild-type and isogenic SAg deletion strains . The production of both SpeC and SpeL from wild-type S . pyogenes MGAS8232 was clearly detectable by Western blot analysis , while SpeA was weakly detected ( Figure 3E ) . Importantly , for each of these three SAgs , the toxin was not made by the appropriate deletion mutant . To evaluate SAg activity from the different S . pyogenes mutants , we tested the supernatants from the isogenic S . pyogenes strains for the ability to activate HLA-B6 mice splenocytes . Consistent with the Western blot analysis ( Figure 3E ) , and the ability of select SAgs to activate these cells ( Figure 2B ) , the largest reduction in activity for the individual deletion strains was for MGAS8232 ΔSpeA , whereas the MGAS8232 ΔSAg mutant did not activate splenocytes above background levels ( Figure 3F ) . We next evaluated all of the isogenic S . pyogenes MGAS8232 strains for the ability to infect HLA-B6 mice . MGAS8232 ΔSpeA demonstrated a striking reduction in bacterial CFUs recovered at 48 h compared to wild-type MGAS8232 ( Figure 4A ) . Although some variability was seen for the other mutants in vivo ( ΔSpeC , ΔSpeG , ΔSpeLM and ΔSmeZ ) , multiple animal experiments demonstrated that each was capable of infecting the HLA-mice to wild-type levels . MGAS8232 ΔSAg however , resembled wild-type MGAS8232 recovery in B6 mice ( Figures 1A and 4A ) . In order to genetically complement the MGAS8232 ΔSAg strain , and to determine if a single SAg could promote infection , we introduced the wild-type speA gene ( and the corresponding speA promoter ) into the chromosome of MGAS8232 ΔSAg between the pepO and tsf genes . The wild-type SpeA complemented strain enhanced infection significantly above MGAS8232 ΔSAg that was not statistically different from wild-type MGAS8232 ( Figure 4A ) . To assess the requirement for SAg activity for the infection phenotype , we constructed a SpeA MHC-II binding mutant based on a structural model of SpeA in complex with HLA-DQ8 ( Figure 4B ) . This model predicted SpeA Tyr100 would interact with the conserved MHC-II α-chain Lys39 and both of these equivalent residues in the staphylococcal enterotoxins , and HLA-DR1 , respectively , have been shown to be important for SAg function [23] , [24] . Recombinant SpeAY100A ( Figure 4C ) demonstrated ∼100-fold reduction in potency relative to wild-type SpeA ( Figure 4D ) and thus we introduced speAY100A into MGAS8232 ΔSAg in a similar fashion to the wild-type speA complementation experiment . The speAY100A-complemented strain was significantly reduced compared to the wild-type SpeA complemented stain , and although CFUs were not statistically increased over MGAS8232 ΔSAg , there was a trend for enhanced infection by ∼1 log . We believe this potential increase may reflect the residual activity present in the SpeAY100A mutant ( Figure 4A ) . Furthermore , using qRT-PCR we confirmed that the in vitro expression of speAY100A was similar to speAWT between the two complemented strains ( data not shown ) . These collective data indicate that the enhanced nasopharyngeal infection by S . pyogenes MGAS8232 in HLA-B6 mice is due to the production of the SpeA exotoxin , and that SAg function is required . To gain insight into the S . pyogenes infection process in HLA-B6 mice , we generated coronal sections of the nasal passage for sham , wild-type MGAS8232 , or MGAS8232 ΔSAg treated mice , at both 24 and 48 h . Sections were stained with H&E , as well as DAPI ( blue ) and an anti-S . pyogenes fluorescent antibody ( red ) . As predicted , there was no evidence of S . pyogenes infection seen in the sham treated mice at 24 or 48 h ( Figure 5A ) . Both wild-type MGAS8232 and MGAS8232 ΔSAg demonstrated sparse , but detectable S . pyogenes cells , within the upper nasal turbinates at 24 h ( Figure 5A ) . This was consistent with bacterial counts from MGAS8232 ΔSAg infected mice at 24 h ( 975±475 CFU/cNT; n = 3 mice ) that did not differ significantly from wild-type MGAS8232 infected mice ( Figure 1B ) . Consistent with the CFU data ( Figure 4A ) , very few MGAS8232 ΔSAg cells were detected microscopically at 48 h , whereas wild-type MGAS8232 produced a robust infection that was localized to the upper nasal turbinates ( Figure 5A; 48 h MGAS8232 boxed insets ) . H&E-stained sections from 48 h post-infection ( n = 5 mice per group , 2 sections per mouse ) were scored in a blinded fashion for the presence and severity of mucus , red blood cells , and nucleated cellular debris on the surface of the respiratory epithelium and neuroepithelium . This analysis revealed no significant findings for the sham treated mice , and mice infected with MGAS8232 ΔSAg displayed only mild neutrophilic infiltration in the sub-epithelial spaces , and mild epithelial disruption ( Figure 5B ) . However , mice infected with wild-type MGAS8232 demonstrated significant signs of inflammation including sloughed cellular debris into the nasal cavity lumen , and marked neutrophilic infiltration and epithelial cell disruption with evidence of cocci on the epithelial surface , and evidence of hypersecretory activity from the neuroepithelium ( Figure 5B ) . Flow cytometric analysis of total cNT preparations from mice treated with the 3 groups ( n = 4 mice per group ) however , revealed few overall changes in immune cell percentages , although a significant decrease in the dendritic cell ( DC ) ( CD11c+ ) population was revealed in the wild-type MGAS8232 infected animals compared with MGAS8232 ΔSAg infect mice , and the analysis also showed a trend for increased neutrophils ( GR1+ ) in wild-type MGAS8232 infected animals ( Figure S1 ) that was consistent with the histological analyses . We did not observe any changes in immune cell populations in the spleen or lymph nodes ( Figure S1 ) . However , cytokine analysis of homogenized cNTs demonstrated an early inflammatory type or TH1-skewed response of the wild-type MGAS8232 infected mice compared with the MGAS8232 ΔSAg strain , including enhanced production of IL1α , IL-2 , IL-6 and IL-17 , as well as the chemokines KC , IP-10 and MCP-1 ( Figures 5C and S2 ) . By 48 h , the wild-type MGAS8232 infected mice displayed a robust chemokine response consistent with the high numbers of bacterial cells by this time point . Taken together , these data are consistent with an early SAg-dependent inflammatory environment within the nasal turbinates where S . pyogenes could both survive and rapidly expand to high numbers over the initial 48 h of infection . Given the prominent nasopharyngeal infection phenotype for S . pyogenes MGAS8232 that was SpeA-dependent ( Figures 4A and 5A ) , we tested the ability of humoral immunity to SpeA to influence nasopharyngeal infection by S . pyogenes MGAS8232 ( Figure 6A ) . We vaccinated HLA-mice with the SpeAY100A toxoid and these mice developed high anti-SpeA IgG titres compared with sham-vaccinated mice ( Figure 6B ) . Following nasal inoculation with ∼1×108 CFU of wild-type MGAS8232 , SpeAY100A-vaccinated mice showed a dramatic reduction in infection ( Figure 6C ) that was similar to the MGAS8232 ΔSpeA mutant ( Figure 4A ) , while sham-vaccinated mice were infected efficiently ( Figure 6C ) . These data indicate that humoral immunity to appropriate SAgs can inhibit nasopharyngeal infection in SAg-sensitive mice .
Bacterial SAgs from S . pyogenes are well recognized as important virulence factors for the development of serious toxin-mediated diseases such as the streptococcal TSS [5] . However , given that streptococcal TSS is rare , and since the disease has a very high mortality rate ( ∼45% ) [25] , it is unlikely that the induction of streptococcal TSS provides an evolutionary advantage for S . pyogenes . Thus , the actual biological function of these toxins has remained an enigma . We reasoned that since a major niche for S . pyogenes is the epithelial surfaces of the nasopharynx , we sought to evaluate the influence of SAgs in this context . Herein , we provide important evidence that acute infection of the upper respiratory tract by S . pyogenes MGAS8232 is enhanced dramatically in mice expressing human MHC-II , and that this phenotype is directly attributed to SpeA . A logical interpretation of these findings is that the true biological function of SAgs , in terms of the life cycle of S . pyogenes , is to promote the initial establishment of infection in humans . This work also provides further evidence that host MHC-II molecules are a central component of the human specific tropism of S . pyogenes . Human MHC-II [11]–[14] , human plasminogen [26] , [27] , and human CD46 [28] are each known to contribute to the development of invasive streptococcal disease , although the influence of these latter host factors have not yet been tested in a nasopharyngeal infection model . S . pyogenes produces a hyaluronic acid capsular polysaccharide [29] that is important for nasopharyngeal colonization in mice [30] , and the hyaluronic acid binding receptor CD44 is an important receptor for S . pyogenes [31] . However , the conserved amino acid sequences , structures , and binding affinities of human and mouse CD44 for hyaluronan [32] would argue against a specific role of CD44 in the human-specific tropism of S . pyogenes . Although there were no significant differences in CFUs at 24 h comparing wild-type S . pyogenes and the SAg-deficient strain , wild-type MGAS8232 was able to expand rapidly between 24 h to 48 h by ∼2 logs ( Figure 1B ) , which was entirely consistent with the immunofluorescence staining of the nasal passages ( Figure 5A ) . Although there were few differences in immune cell percentages from the cNT by 48 h , other than a clear decrease in CD11c+ leukocytes for wild-type MGAS8232 infected mice ( Figure S1 ) , S . pyogenes did induce a SAg-dependent Th1/inflammatory cytokine response , which was accompanied by the influx of innate immune cells by 48 h ( Figure 5A ) . Selective depletion of CD11c+ DCs in mice resulted in enhanced dissemination of S . pyogenes from a subcutaneous infection into lymph nodes and the liver , demonstrating that DCs are contributors to host defence against S . pyogenes [33] . However , some strains of S . pyogenes can also induce DC apoptosis in a streptolysin O-dependent manner [34] , which may have contributed to the decrease in CD11c+ populations . Nevertheless , we favour the hypothesis that S . pyogenes has provoked a localized and SAg-mediated cytokine response that resulted in a state of transient immunosuppression allowing S . pyogenes to escape myeloid cell mediated killing . In support of this hypothesis , staphylococcal enterotoxin B has been demonstrated in vivo to induce a transient ( ∼48 h ) Vβ-unrestricted immunosuppressive response in T cells with the inability to produce IL-2 , that also caused decreased numbers of splenic CD11c+ dendritic cells [35] . These responses were very consistent with mouse cytokine responses ( Figure 5C ) and immune cell analyses ( Figure S1 ) of MGAS8232 infected HLA-B6 mice . By 48 h , however , a robust IL-6 and IL-17 response was produced ( Figures 5C and S2 ) , concurrent with the high numbers of S . pyogenes , and each of these cytokines are known to be important for S . pyogenes control [21] , [36] . Alternatively , inflammation induced epithelial cell damage within the nasal turbinates ( Figure 5B ) may have promoted access to host cell adhesive factors to allow for the initial establishment of infection . The very weak cytokine response to the MGAS8232 ΔSAg strain was somewhat surprising , yet this finding may support this second hypothesis where in the absence of functional SAg , S . pyogenes are rapidly cleared through primarily mucociliary clearance mechanisms . Although S . pyogenes is capable of internalization into epithelial cells , the evidence indicates that S . pyogenes does not replicate efficiently within epithelial cells [37] , [38] , and thus we do not predict a role for enhanced intracellular survival based on SAg expression . SAgs have also been studied directly in the context of live invasive streptococcal disease using defined genetic knockout strains . Earlier work utilizing a S . pyogenes myositis model in HLA-DQ8 transgenic mice demonstrated clear Vβ-specific alterations during infection , as well as SpeA-dependent conjunctivitis , hyperplasia of the lymph nodes and spleen , and T cell infiltration into the liver , yet the SpeA knockout strain demonstrated no difference in overall mortality compared with the wild-type counterpart [14] . Additionally , although SmeZ is dominant in the speA- and speC-negative M89 isolate S . pyogenes H293 , genetic disruption of smeZ did not alter bacterial clearance or mortality in a peritoneal infection model [39] . Thus , a picture has emerged where individual SAgs may not contribute to S . pyogenes survival during invasive disease . Nevertheless , streptococcal SAgs can cause TSS directly in experimental animal models [40]–[42] , human MHC-II molecules contribute to mouse mortality during invasive infections [12] , [14] , and in severe invasive human infections , streptococcal TSS is a major contributor to overall mortality [25] . In the nasal infection model presented here , despite the high number of bacterial cells recovered from wild-type S . pyogenes infected HLA-B6 mice at 48 h , S . pyogenes did not become invasive ( Figure 1B ) , and the infection was reduced to very low levels after about 7 days . In humans , symptomatic pharyngitis infections typically last for about one week without antibiotic treatment [43] , and thus the presented model appears to be a reasonable approximation of acute upper respiratory tract infection in humans . However , whether the model could replicate a longer term asymptomatic colonization state is unlikely , as many patients can harbour S . pyogenes for extended time periods , potentially lasting over 2 years [3] . Although our data demonstrate that SpeA is critical for the infection phenotype in HLA-B6 mice , the genome of S . pyogenes MGAS8232 also encodes for 5 other SAgs , each which is known to activate human T cells [44]–[47] . Although SpeC was the predominant SAg secreted from MGAS8232 in vitro ( Figure 3E ) , the lack of a phenotype for the MGAS8232 ΔSpeC mutant was somewhat predicted , as this SAg does not activate murine T cells [48] ( Figure 2 ) . Although recombinant SmeZ did potently activate splenocytes from HLA-B6 mice , and SmeZ is the primary immunoactive SAg for some S . pyogenes strains [39] , we did not detect expression of SmeZ in vitro from wild-type MGAS8232 ( Figure 3E ) , which likely contributed to the inability of SmeZ to compensate functionally in the MGAS8232 ΔSpeA strain . However , the MGAS8232 ΔSAg strain complemented with wild-type SpeA did not appear to fully restore the infection phenotype , and the lack of SmeZ production could potentially be responsible for this result . The remaining SAgs showed very weak activity for the activation of splenocytes from HLA-mice and thus they do not play an important role for infection in this model . The vaccination experiments provide further evidence , independent of the genetic deletion strains , of the critical role played by SpeA during the infection . Neutralization of SAg activity by antibodies is consistent with clinical evidence that has established a link between the lack of streptococcal SAg-neutralizing antibodies and the development of streptococcal TSS [49]-[51] . Also , neutralizing antibodies are known to protect against experimental STSS in rabbits [40] , [41] . Thus , pre-existing anti-SAg antibodies may potentially inhibit infection by specific strains of S . pyogenes , yet S . pyogenes could theoretically circumvent this through up-regulation of additional SAgs for which neutralizing antibodies are absent . Since it is clear that different strains of S . pyogenes encode different repertoires of SAgs [7] , S . pyogenes may also potentially alter SAg expression patterns to engage different host MHC-II molecules . The SAgs remain well-recognized virulence factors for S . pyogenes . However , this work demonstrates that their genuine contribution to the life cycle of this pathogen is likely to promote the establishment of pharyngitis , or potentially asymptomatic colonization , in genetically susceptible individuals expressing SAg-responsive MHC-II molecules . The redundancy of SAgs within S . pyogenes has also remained unexplained , and this work further illustrates that this may exist , in part , to overcome the highly polymorphic nature of human MHC-II molecules , and also to avoid natural host immunity to SAgs . Thus , SAgs are important contributors to the complex genotype-phenotype relationship that exists between S . pyogenes and humans , and these toxins should be considered further as valid targets for vaccination studies to impede the enormous burden of disease by this versatile pathogen .
All animal experiments were in accordance with the Canadian Council on Animal Care Guide to the Care and Use of Experimental Animals . The animal protocol ( #2009-038 ) was approved by the Animal Use Subcommittee at Western University . S . pyogenes MGAS8232 is an M18 serotype that was isolated from a patient with acute rheumatic fever in Utah in 1987 and the genome has been sequenced and fully annotated [22] . S . pyogenes strains were grown in Todd Hewitt media supplemented with 1% ( w/v ) yeast extract . Deletions were made for all of the SAg genes using the pG+host5 system [52] , [53] . All recombinant plasmids were built with standard molecular procedures using Escherichia coli XL1-blue as the cloning host [54] . Briefly , deletion constructs were generated by amplification of ∼500 bp of DNA on either side of the relevant SAg gene ( Primers are listed in Table S1 ) and cloned into pG+host5 . Flanking DNA included the first and last 8 codons for each SAg gene to generate precise , markerless and in frame deletions of each SAg gene . Plasmids were electroporated ( Bio-Rad Gene Pulser XCell ) into S . pyogenes MGAS8232 and single crossover integrations were selected at 40°C under erythromycin ( 1 µg ml−1 ) selection . PCR confirmed single crossover integrations were subcultured without antibiotics at 30°C and single clones were screened for a loss of erythromycin resistance , and double crossover gene disruptions were confirmed by PCR . In each case , appropriate PCR products were sequenced to confirm the expected deletion . All mutants were confirmed to lack growth alterations using Bioscreen C ( Piscataway , NJ , USA ) assays . To assess for protease activity , S . pyogenes strains were grown on dialyzed brain heart infusion agar containing 1 . 5% skim milk . Five microliters of OD600 0 . 1 S . pyogenes mutants were inoculated into 2 mm holes punched in the plates and incubated at 37°C for 24 hours . The ability of each strain to hydrolyze casein was assessed by the diameter of the zones of clearing . For speA and speAY100A complementation experiments , wild-type and mutant speA from MGAS8232 ( including the native promoter ) were individually ‘knocked in’ using the pG+host5 system to the non-coding region between genes encoding endopeptidase O ( pepO ) and elongation factor-Ts ( tsf ) and confirmed by PCR and DNA sequencing . Genes encoding for SpeA , SpeG , SpeL , SpeM , and SmeZ , lacking nucleotides encoding the predicted signal peptides , were PCR amplified and cloned into a modified pET-28a vector to introduce an engineered tobacco etch virus ( TEV ) protease cleavage site downstream from a His6 tag . Cloning of SpeC into the pET-41a vector has been described [55] , and SmeZ was cloned in a similar manner to SpeC . All recombinant SAgs were produced by 200 µM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) -induced expression in E . coli BL21 ( DE3 ) , purified via nickel affinity chelation chromatography , and His6 tags were removed using autoinactivation resistant His6::TEV protease [56] , as described [57] , [58] . Proteins were run on 12% separating SDS-PAGE gels and Western blots were visualized on a LI-COR Odyssey ( LI-COR Biosciences ) using IRDye800 conjugated donkey anti-rabbit IgG as the secondary antibody ( Rockland Inc . ) . All recombinant SAgs ran as discrete homogenous bands by SDS-PAGE ( Figure 3D ) . Purified and lyophilized SpeA , SpeC , SpeG , SpeL , SpeM , and SmeZ were used to generate polyclonal rabbit antibodies from a commercial source ( ProSci Incorporated , USA ) . HLA-expressing humanized mice ( B6-DR4 , B6-DQ8 , B6-DR4/DQ8 ) have been previously described [12] , [59] , [60] . These mice were bred in a barrier facility at Western University and were routinely genotyped for the appropriate transgene ( s ) . The mouse nasopharyngeal infection model has been described [61] , with modifications . Briefly , mice were used at 9–13 weeks . Freshly grown exponential phase S . pyogenes cells ( OD600 0 . 2–0 . 3 ) were washed and suspended in Hanks balanced saline solution [HBSS; a total of ∼1×108 ( range 0 . 6–1 . 4×108 ) CFU per 15 µl] and 7 . 5 µl was used to inoculate each nostril under Forane ( isoflurane , USP ) inhalation anesthetic ( Baxter Corporation ) . Sham treated mice only received HBSS . Mice were sacrificed at the noted time points , and the cNTs , including the nasal associated lymphoid tissue , nasal turbinates , and maxillary sinuses were removed . Tissue was homogenized in HBSS , serially diluted , and plated on Trypticase Soy Agar with 5% Sheep Blood plates ( Becton , Dickinson and Company , MD , USA ) , or used for cytokine analysis , or flow cytometry . For splenocyte activation experiments , cells were harvested from B6 or HLA-B6 mouse spleens , treated with ammonium-chloride-potassium ( ACK ) lysis buffer ( 15 mM NH4CL , 10 mM KHCO3 , 0 . 1 mM EDTA ) and suspended ( 2×105 cells per well ) in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum ( Sigma-Aldrich ) , 0 . 1 mm minimal essential medium ( MEM ) non-essential amino acids , 2 mm L-glutamine , 1 mm sodium pyruvate , 100 U ml−1 penicillin , 100 µg ml−1 streptomycin ( all from Gibco Life Technologies ) and 50 µM β-mercaptoethanol ( Sigma ) . SAgs were added at the indicated concentrations and mouse IL-2 was determined after 18 h by enzyme-linked immunosorbent assay ( eBioscience ) . Proliferation was measure by the addition of 1 µCi/well [3H]thymidine after 72 h and after another 18 h cells were harvested on fiberglass filters and [3H]thymidine incorporation was assessed on a 1450 Microbeta liquid scintillation counter ( Wallac ) . The SpeA Tyr100→Ala mutation was predicted to disrupt the low-affinity MHC-II binding domain of SpeA based on a model of the crystal structure of SpeA in complex with HLA-DQ8 . To generate this model , the structures of SpeA ( PDB: 1FNU ) [62] and HLA-DQ8 ( PDB: 1JK8 ) [63] were superpositioned onto the known SEC3:HLA-DR1 complex ( PDB: 1JWM ) [64] , and visualized using Pymol ( pymol . sourceforge . net ) . Mutagenesis of speA was conducted using megaprimer-PCR ( Table S1 ) to introduce the SpeA Tyr100→Ala mutation . Mice were fully anesthetized with Forane ( isoflurane , USP ) inhalation anaesthetic ( Baxter Corporation ) and perfused through the heart with sterile PBS containing heparin using a Gilson Minipuls 3 peristaltic pump ( Middletown , WI , U . S . A ) at a constant flow rate . Mice were then perfused with 10% neutral buffered formalin ( BDH , VWR , West Chester , PA , USA ) through the peristaltic pump . The head was soaked in 10 volumes of formalin for 24 hours and re-suspended in Shandon TBD-2 Decalcifier ( TBD; Thermo Scientific , Kalamazoo , MI , USA ) for 96 hours . TBD-decalcified heads were placed in formalin for 48 hours and washed with 1× PBS , and resuspended in 10-volumes of 1× PBS twice daily for 4 days , washed in 70% ethanol twice , and stored in 10-volumes of 70% ethanol . Cassettes were processed in Leica ASP300 fully enclosed paraffin wax tissue processor overnight using the ‘bone’ program and embedded in paraffin wax . Heads were serially sectioned between the first and second molar on a HM335E Microtome ( Leica ) into 5 micron sections , mounted on Fisherbrand Superfrost Plus microscope slides ( Fisher Scientific , Fair Lawn , NJ , USA ) and dried at 45°C for 48 h prior to storage/staining . Tissues were stained with H&E in a Leica Autostainer XL . H&E stained slides were evaluated by an experienced mouse pathologist in a blinded fashion . The relative amount of mucus present covering the epithelia , the presence of red blood cells , and the presence of nucleated cellular debris on the surface of the epithelia was assessed and the presence and severity of these findings were used to assign a score of zero to two points to each of two sections per mouse ( n = 5 mice per group ) . The scores were averaged to determine differences in histological pathology in the mice . Fluorescence staining was done with adjacent serial sections using a Goat α-S . pyogenes polyclonal ( NB200-643; Novus Biologicals ) at 1∶100 dilution , and donkey α-Goat Alexflour 595 ( A-11058; Invitrogen ) at 1∶1000 dilution . Images were captured using an upright BX61 fluorescent microscope ( Olympus ) . For flow cytometry analysis , isolated cells ( from cNTs , lymph nodes , or spleens ) were aliquoted at 500 , 000 cells per 5 ml tube , and pre-treated with Fc block ( hybridoma clone 2 . 4G2 ) prior to cell staining . Staining was done in panels using the following antibodies: α-CD3-APC ( clone 145-2C11 ) , α-CD3-PE Cy7 ( clone 53–7 . 3 ) , α-CD8 PE Cy7 ( clone 53–6 . 7 ) , α-CD45 PE ( clone 30-F11 ) , α-CD19 ( clone MB19-1 ) , α-NK1 . 1 ( clone PK136 ) , α-CD11c ( clone N418 ) , α-F4/80 ( clone BM8 ) , and α-GR1 ( clone RB6-8C5 ) ( all from eBioScience ) ; α-CD4 APC Cy7 ( clone GK1 . 5; Biolegend ) ; and α-CD45 Alexafluor 700 ( clone 30-F11; BD Biosciences ) . Dead cells were excluded using 7-AAD ( BD Biosciences ) . Antibodies to stain cells for each panel were added , mixed , and incubated on ice in the dark for 30 minutes . Cells were washed twice with 1× PBS +5% FBS and resuspended in 500 µl of 1× PBS +5% FBS . Stained cells were run on a BD FACS Canto II flow cytometer ( BD Biosciences ) . Standard compensations were used for each tissue using FACSdiva software . Cytokine concentrations were determined from cNT homogenates isolated from mice treated with HBSS ( sham ) , wild-type S . pyogenes MGAS8232 , or isogenic S . pyogenes MGAS8232 ΔSAg strains at either 24 or 48 hours post-infection in HLA-B6 mice . Multiplex bead arrays were performed using the Mouse Cytokine 32-plex Discovery Array ( Eve Technologies ) . Heat maps were generated using the Matrix2png algorithm [65] and data is shown as the average cytokine responses from 3–4 mice per group . Quantitative data from the cytokine analyses are shown in Figure S2 . For vaccination experiments , 6–8 week old HLA-B6 mice were injected subcutaneously with 1 µg of recombinant SpeAY100A ( or sham ) emulsified in Imject Alum Adjuvant ( Thermo Fisher Scientific Inc . ) every 2 weeks for a total of three injections . Two weeks following the last injection , mice were bled for antibody titers as determined by direct ELISA against 1 µg wild-type SpeA per well , and calculated as the reciprocal of the lowest serum dilution with readings 4-fold above background . Mice were challenged with ∼1×108 CFU wild-type MGAS8232 as described above 24 hours after the final bleed and CFUs were determined 48 h post infection . When appropriate , individual data points , or the mean ± SEM , are shown , and p values were calculated using the Student's t-test with Prism software ( GraphPad ) . A p value of less than 0 . 05 was determined to be statistically significant . | Streptococcus pyogenes is the most common cause of bacterial pharyngitis , also known as ‘strep throat’ . However , this organism is also responsible for a range of other important human illnesses including necrotizing fasciitis and rheumatic heart disease ( RHD ) . Indeed , complications from RHD and invasive infections by S . pyogenes are responsible for over one half million deaths per year in the world . S . pyogenes produces potent toxins called superantigens ( SAgs ) , also known as the scarlet fever or erythrogenic toxins . SAgs have been studied for many years , yet we don't understand what purpose SAgs play in the life cycle of S . pyogenes . Rather than studying SAgs in the context of serious streptococcal disease , we studied the role of SAgs in a nasopharyngeal infection model . Our work demonstrates that for S . pyogenes to efficiently infect mice , the mice must express a human protein that is a receptor for the SAgs , and that S . pyogenes must produce SAgs . We further show that immunizing against SAgs prevents nasopharyngeal infection . This work demonstrates that SAgs are important factors for establishing infection by S . pyogenes and that SAgs may be potential candidates for inclusion within a S . pyogenes vaccine . | [
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] | 2014 | Bacterial Superantigens Promote Acute Nasopharyngeal Infection by Streptococcus pyogenes in a Human MHC Class II-Dependent Manner |
Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes . A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data . Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs , encoded in single-cell high-dimensional time series data . For biological reaction networks governed by the chemical Master equation , we derive model-based information approximations and analytical upper bounds , against which we benchmark our proposed model-free decoding estimators . In contrast to the frequently-used k-nearest-neighbor estimator , decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples . We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response , and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals . We argue that these single-cell , decoding-based information estimates , rather than the commonly-used tests for significant differences between selected population response statistics , provide a proper and unbiased measure for the performance of biological signaling networks .
For their survival , reproduction , and differentiation , cells depend on their ability to respond and adapt to continually changing environmental conditions . Environmental information must be sensed and often transduced to the nucleus , where an appropriate response is initiated , usually by selectively up- or down-regulating the expression levels of target genes . This information flow is mediated by biochemical reaction networks , in which concentrations of various signaling molecules code for different environmental states or different response programs . This map between environmental input or response output and the internal chemical state is , however , highly stochastic , because typical networks operate with small absolute copy numbers of signaling molecules [1]; moreover , different environments can be encoded by the same signaling molecule , by differentially regulating the dynamics of its concentration [2] . This raises two fundamental questions: first , how much information the cells could , even in principle , encode in the combinatorial and possibly time-varying concentrations of multiple signaling molecules and how such information could be plausibly read out during “downstream” processing; and second , how can we quantify , in an unbiased and model-free fashion , the amount of information available to the cells from limited experimental data . Information theory provides a framework within which the theoretical study of limits to communication as well as the empirical study of actual information flows can be addressed [3] . Applications of information theory to questions in biology and , in particular , neuroscience started already in the 1950s and continue to this day , with the main focus to understand how—and with what accuracy—neural activity encodes information about the environment [4–6] . Applications of analogous techniques to biochemical signaling only started recently and represent an active area of research at the interface of physics , biology , statistics , and engineering [7–10] . Recent theoretical work analyzed the reliability of information transmission through specific reaction systems in the presence of molecular noise , e . g . , during ligand binding [11] , in chemotaxis [12] , gene regulation [13–19] , biochemical signaling networks [20] , etc . , and asked how such transmission can be maximized by tuning the reaction rates . Generally , these studies focused on steady state , by considering the information encoded in a single temporal snapshot of the reaction network at equilibrium given the input signals . Rigorous extensions to dynamical signals have been either rare and only possible for simple cases , like the BIND channel [11] , or required specific operating regimes that permitted linearization and Gaussianity assumptions [12 , 21 , 22] . At its core , the analysis of signal transduction through nonlinear noisy chemical systems requires one to have control over the distribution of concentration trajectories given the ( possibly ) time-varying inputs; even if it were possible to calculate this distribution in principle , the curse of dimensionality puts strong limits to the manipulations required to compute the information transmission . Consequently , problems of this kind are currently considered intractable in their full generality . Empirical estimates of information transmission in biochemical networks similarly focused on the steady state [23 , 24] , or considered only specific , hand-picked dynamical features , such as the amplitude or the frequency of the response , as information carriers [25] . Recent developments of fluorescent reporters and microfluidics have enabled the characterization of dynamical responses at a single cell resolution using large ( > 104 ) numbers of sampled response trajectories , thereby permitting direct information estimates using generic estimators like the k-nearest-neighbors ( knn ) [26] . Existing approaches , however , suffer from severe limitations: they still require a prohibitive number of samples , especially when the response is distributed over multiple chemical species; or they necessitate uncontrolled assumptions about trajectory features that are supposed to be “relevant” . We recently proposed and applied decoding-based information estimators [27] as an alternative that draws on the past experiences in neuroscience [28–30] to dissect the yeast stress-response network . In this paper we provide a detailed account of the new methodology , show that it alleviates the most pressing problems of existing approaches , and benchmark it against synthetic and real data .
At their core , cellular processes consist of networks of chemical reactions . A chemical reaction network consists of a set of m molecular species {X˜1 , X˜2 , … , X˜m} that interact through K coupled reactions of the form: ν1k′X˜1+…+νmk′X˜m→θkν1k″X˜1+…+νmk″X˜m , k=1 , … , K ( 1 ) where ν1k′ , … , νmk′ and ν1k″ , … , νmk″ are coefficients that determine how many molecules of each species are consumed and produced in the k-th reaction . θ1…θk∈R+ determine the rates at which the reactions occur and depend on binding affinities of chemical species , temperature and possibly the external conditions . If we assume that the system is well-stirred , in thermal equilibrium and the reaction volume is constant , it can be shown that the probability that a reaction of type k takes place in an infinitesimal time interval [t , t + dt] can be written as ak ( x˜ , θk ) dt=θkgk ( x˜ ) dt , where x˜=[x˜1 , … , x˜m]T∈N0m contains the amounts of molecules of the m species that are present in the system at time t , and gk ( x˜ ) =∏i=1m ( x˜iνik′ ) counts all possibilities of choosing the required reaction molecules out of all available molecules [31 , 32] . θk is a constant that depends on the physical characteristics of the cell but also on the environmental conditions . Let us denote the probability that x˜ molecules of the m species are present in the system at time t∈R+ by p ( x˜ , t ) and define the stoichiometric change vectors νk=[ν1k , … , νmk]T∈Zm , k=1 , … , K , as the net changes in the amount of molecules in the reactions , i . e . νik=νik″−νik′ , i=1 , … , m , k=1 , … , K . Then it can be shown [32] that the chemical master equation ( CME ) can be written as: p˙ ( x˜ , t ) =−p ( x˜ , t ) ∑k=1Kak ( x˜ , θk ) +∑k=1Kp ( x˜−νk , t ) ak ( x˜−νk , θk ) , ( 2 ) or in a more compact form [32] p˙ ( t ) =Mp ( t ) , ( 3 ) where p ( t ) is a vector with components p ( x˜ , t ) , which is , in principle , infinite dimensional , and M contains the transition rates between all possible states , e . g . the transition rate from state x˜k′=x˜−νk to state x˜ is given by Mx˜ , x˜k′=ak ( x˜k′ , θk ) −δx˜ , x˜k′∑qaq ( x˜ , θq ) , ( 4 ) where δ is the Kronecker delta , which is 1 when x˜=x˜k′ and 0 otherwise . The CME given in Eq ( 3 ) is an instance of a continuous-time discrete-state-space Markov Chain for a random process X that can be solved exactly only for a few simple cases . It is nevertheless possible to efficiently generate samples x of the random process X , which we will refer to as “trajectories” or “paths” , for a selected time interval , t ∈ [0 , T] , according to the correct probability distribution p , by the Stochastic Simulation Algorithm ( SSA , or the Gillespie algorithm ) [33] . To study information transmission through the biochemical networks described by the CME , we need to define the input and output signals . In the simplest setup considered here , the input U is a discrete random variable that can take on one of the q ≥ 2 possible values , U ∈ {u ( 1 ) , u ( 2 ) , … , u ( q ) } . Each input in general corresponds to a distinct set of reaction rate constants θ , but in models of real biological networks , changing input often modulates only one or a few rates in the system , e . g . , by representing the change in a key external ligand concentration , receptor activity , etc . Changes in the input are reflected in changes in the resulting trajectories of chemical species amounts , x . Typically , only a subset of chemical species could be considered as biologically-relevant “outputs” that encode the information about the environmental change: this would correspond to marginalizing p in Eq ( 3 ) over the unobserved ( non-output ) chemical species for the purposes of information transmission . While this is an interesting theoretical problem in its own right , here we work with simple toy examples where the output will be the trajectory , x , over the complete state space , i . e . , we assume that all chemical species in the reaction network can be fully and perfectly observed . As we explain below , this allows us to define and compute the mutual information between a discrete input , U , and the output random process X given by the CME in a straightforward fashion . We later show that this computation can be carried out also when the continuous-time process X is sampled at uniform discrete times , as would be the case with experimental measurements . Information theory introduces the mutual information as the measure of fidelity by which changes in one random variable , e . g . , the input U , can effect changes in another random variable , e . g . , X . In this sense , mutual information is simply a measure of statistical dependency ( i . e . , any correlation , be it linear or not ) between U and X , and can thus be written as a functional of the joint probability density function p ( x , u ) : I ( X;U ) =∫X∫Up ( x , u ) log2 ( p ( x , u ) pX ( x ) pU ( u ) ) dudx ( 5 ) where pU and pX are the marginal density functions for U and X , respectively , and we have generically written u and x as continuous variables; if they are discrete , integral signs are replaced by summations over the support for the corresponding probability distributions , as appropriate . Mutual information is a non-negative symmetric quantity that is measured in bits , and is zero only if X and U are statistically independent . When studying information transmission through a channel U → X specified by p ( x|u ) , for which U serve as inputs drawn from an input distribution pU ( u ) , it is common to rewrite Eq ( 5 ) as I ( X;U ) =H ( U ) −H ( U|X ) =H ( X ) −H ( X|U ) , ( 6 ) where H ( X ) is the differential entropy of X ( and analogously for H ( U ) ) , defined as H ( X ) =−∫XpX ( x ) log2pX ( x ) dx , ( 7 ) and the conditional entropy , H ( X|U ) , is H ( X|U ) =∫UH ( X|u ) pU ( u ) du=−∫U∫XpU ( u ) p ( x|u ) log2p ( x|u ) dxdu . ( 8 ) Eq ( 6 ) can be interpreted in two ways: information is either the difference between the total variability in the repertoire of responses X that the biochemical network can generate ( measured by the response entropy , H ( X ) ) and the average variability at fixed input that is due to noise in the network ( measured by the noise entropy , H ( X|U ) ) ; alternatively , information is also the entropy of the inputs , H ( U ) , minus equivocation H ( U|X ) , or the average uncertainty in what input was sent given that a particular response was observed . These interpretations make explicit the dependence of information both on the properties of the channel ( the biochemical reaction network ) , as well as on the distribution of signals pU that the network receives . In this work , we will consider discrete inputs and will assume uniform pU . It is , however , also possible to compute the channel capacity C by maximizing the information flow at given p ( x|u ) over all possible input distributions , C=maxpUI ( X;U ) ; ( 9 ) Shannon’s classic work then proves that error-free transmission at rates higher than those given by capacity is impossible , while error-free transmission at rates below capacity can be achieved with the optimal use of the channel . Contrary to engineering , where the focus is on finding encoding and decoding schemes that best utilize a given channel , in biophysics and systems biology mutual information is used as a tool to quantify the limits to biological signal processing due to noise without needing to make assumptions about possible biochemical encoding and decoding mechanisms . The setup we consider here is one in which inputs U are drawn independently from a uniform distribution and change rarely , i . e . , at a rate that is much lower than the ( inverse ) timescale on which the reaction network in Eq ( 1 ) relaxes to its steady state . We assume that after an input change , we observe a fixed-time segment of the complete network dynamics , x , which is a sample path in m-dimensional discrete space , making direct calculation of information , I ( X; U ) , by integrating / summing over all possible trajectories as implied by Eq ( 5 ) intractable . We will nevertheless show that estimates of exact information are possible if the reaction network is known , by explicitly using the transition matrix M of the Markov Chain from Eq ( 3 ) and generating exact sample paths , that is , realizations of X , using SSA . We call this model-based approach exact Monte Carlo approximation and contrast it to uncontrolled model-free estimations such as those obtained by using Gaussian approximations or k-nearest-neighbors methodology . We then introduce various decoding estimators and establish a hierarchy through which these estimates upper and lower-bound the true information , as shown in Fig 1 . In the absence of a full stochastic model for the biochemical reaction network , mutual information estimation is tractable only if we make assumptions about the distribution of response trajectories given the input . We briefly summarize two approaches below: in the first , k-nearest-neighbor procedure , the space in which the response trajectories are embedded is assumed to have a particular metric; in the second , Gaussian approximation , we assume a particularly tractable functional form for the channel , p ( x|u ) . Here and in the next section we introduce a class of decoding-based calculations that lower-bound the exact information , I ( X; U ) , and can tractably be used as information estimators over realistically-sized data sets . Let D consist of a set of N labeled paths , typically represented in discretely sampled time , D={ ( u1 , x1 ) , ( u2 , x2 ) , … , ( uN , xN ) } , where ui and xi , for i = 1 , … , N , are realizations of the random variables U ∈ {u ( 1 ) , … , u ( q ) } and X∈Rm×d , respectively . Here , D can represent either real data ( typically containing N ∼ 102 − 103 trajectories ) in case of model-free information estimates , or trajectories generated by exact simulation algorithms ( in which case the sample size , N , is not limiting ) from the full specification of the biochemical reaction network in case of model-based approximations . The procedure of estimating the input u^ from x , such that the estimated u^ is “as close as possible” to true u for a given trajectory x , is known as decoding in information theory and neuroscience , and can equivalently be viewed as a classification task in machine learning or as an inference task in statistics . This procedure is implemented by a decoding function , u^=Fω ( x ) ; ( 22 ) F is typically parametrized by parameters ω that need to be learned from data for model-free approaches , or derived from biochemical reaction network specification in case of model-based approaches . F assigns to every xi in the dataset a corresponding “decode” u^i from the same space over which the random variable U is defined; formally , these decodes are instances of a new random variable U^ . The key idea of using decoding for information estimation starts with the observation that random variables U→X→TdX→FωU^ , ( 23 ) where Td represents time discretization , form a Markov chain . In other words , the distribution of U^ is conditionally independent of U and only depends on X , p ( u^|x , u ) =p ( u^|x ) , and so p ( u^ , x , u ) =pU ( u ) p ( x|u ) p ( u^|x ) . ( 24 ) The data processing inequality [43] can be used to further extend the bounds in Eq ( 18 ) : I ( U;U^ ) ≤Iexact ( U;X ) ≤Iexact* ( U;X ) , ( 25 ) where equality between the first two terms holds only if I ( U;X|U^ ) =0 . Consequently , I ( U;U^ ) is a lower bound to the information between trajectories X and the input U [44] . Note that analogous reasoning holds for decoding directly from continuous-time trajectories X . Better decoders which increase the correspondence between the true inputs and the corresponding decoded inputs will typically provide a tighter lower bound on the information . To compute the information lower bound , we apply the decoding function to each trajectory in D in model-based approximations or to each trajectory in the testing dataset for model-free estimators that need to be learned over training data first . We subsequently construct a q × q confusion matrix , also known as an error matrix , where each element ϵij counts the fraction of realizations of x generated by an input u = u ( i ) that decode into u^=u ( j ) . This matrix provides an empirical estimate of the probability distribution p ( u^ , u ) , which can thus be used to compute the information estimate: I ( U^;U ) =∑u , u^p ( u^ , u ) log2p ( u^ , u ) pU ( u ) pU^ ( u^ ) ≈∑i=1q∑j=1qϵijlog2ϵij ( ∑kϵkj ) ( ∑lϵil ) , ( 26 ) Crucially , in this estimation O ( N ) data points are used to empirically estimate the elements of a q × q matrix ϵ , and information estimation involves a tractable summation over these matrix elements; in contrast , direct estimates of I ( U; X ) would involve an intractable summation over ( vastly undersampled ) space for X . For typical applications where q is small , decoding thus provides an essential dimensionality reduction prior to information estimation: in a simple but biologically relevant case of two distinct stimuli ( q = 2 ) , information estimation only requires us to empirically construct a 2 × 2 confusion matrix . If required , one can apply well-known debiasing techniques for larger q [5] .
We start by considering three simple chemical reaction networks for which we can obtain exact information values using the model-based approach outlined in Section Exact information calculations for fully observed reaction networks . This will allow us to precisely assess the performance of decoding-based model-free estimates , and systematically study the effects of time discretization , the number of sample trajectories , and the number of distinct discrete inputs , q . The three examples are all instances of a simple molecular birth-death process , where molecules of X˜ are created and destroyed with rates α and β , respectively: →α ( U ) X˜→β ( U ) ⌀ . ( 36 ) The reaction rates , α and β , will depend in various ways on the input , U , and possibly time , as specified below . Given an initial condition , x ( t = 0 ) , the production and degradation reactions generate continuous-time stochastic trajectories , x ( t ) , recording the number of molecules of X˜ at every time t ∈ [0 , T] , according to the Chemical Master Eq ( 3 ) . These trajectories , or their discretized representations , are considered as the “outputs” of the example reaction networks , defining the mutual information I ( X; U ) that we wish to compute . In all three examples we start with the simplest case , where the random variable U can only take on two possible values , u ( 1 ) and u ( 2 ) , with equal probability , pU ( u ( 1 ) ) = pU ( u ( 2 ) ) = 0 . 5 . Example 1 . In this case , x ( t = 0 ) = 0 , β = 0 . 01 , independent of the input U , and the production rate depends on the input as α ( u ( 1 ) ) = 0 . 1 , α ( u ( 2 ) ) = 0 . 07 . Here , the steady state is given by Poisson distribution with mean number of molecules 〈x ( t → ∞ ) 〉 = α/β . Steady-state is approached exponentially with the timescale that is the inverse of the degradation rate , β−1 . These dynamics stylize a class of frequently observed biochemical responses where the steady-state mean expression level encodes the relevant input value . Even if the stochastic trajectories for the two possible inputs are noisy as shown in Fig 2A , we expect that the mutual information will climb quickly with the duration of the trajectory , T , since ( especially in steady state ) more samples provide direct evidence about the relevant input already at the level of the mean trajectories . Example 2 . In this case , x ( t = 0 ) = 0 , β = 0 . 01 , independent of the input U , and the production rate depends on the input as α ( u ( 1 ) , t ) = 0 . 1 , α ( u ( 2 ) , t ) = 0 . 05 for all t < 1000 , while for t ≥ 1000 the production rate is very small and independent of input , α ( u , t ) = 5 ⋅ 10−4 . In the early period , this network approaches input-dependent steady state with means whose differences are larger than in Example 1 , but the difference decays away for t > 1000 as the network settles towards vanishingly small activity for both inputs , as shown in Fig 2B . These dynamics stylize a class of transient biochemical responses that are adapted away even if the input state persists . In this case , lengthening the observation window T will not provide significant increases in information . Example 3 . In this case , x ( t = 0 ) = 10 . All reaction rates depend on the input , α ( u ( 1 ) ) = 0 . 1 , α ( u ( 2 ) ) = 0 . 05 , β ( u ( 1 ) ) = 0 . 01 , β ( u ( 2 ) ) = 0 . 005 , and are chosen so that the mean 〈x ( t ) 〉 = 10 is constant across time and equal for both conditions , as shown in Fig 2C . In this difficult case , inputs cannot be decoded at the level of mean responses but require sensitivity to at least second-order statistics of the trajectories . Specifically , signatures of the input are present in the autocorrelation function for x: the timescale of fluctuations and mean-reversion is two-fold faster for u1 than u2 . While this case is not frequently observed in biological systems , it represents a scenario where , by construction , no information about the input is present at the level of single concentration values and having access to the trajectories is essential . Because there is no difference in the mean response , we expect linear decoding methods to provide zero bits of information about the input . This case is also interesting because of the recent focus on pulsatile stationary-state dynamics in biochemical networks [53] . These pulses , reported for transcription factors such as Msn2 , NF-κB , p53 , etc . , occur stochastically and , when averaged over a population of desynchronized cells , can yield a flat and featureless mean response . Information about the stimulus could , nevertheless , be encoded in either the frequency , amplitude , or other shape parameters of the pulses . While a generative description of such pulsatile dynamics goes beyond a birth-death process considered here , from the viewpoint of decoding , both pulsatile signaling and our example present an analogous problem , where the mean response is not informative about the applied input . Before proceeding , we note that our examples are not intended to be realistic models of intracellular biochemical networks , but are chosen here for their simplicity and analytical tractability , in order to benchmark model-free estimators against known “gold truth” standard . In particular , while our examples include intrinsic noise due to the stochasticity of biochemical reactions at low concentration , they do not include extrinsic noise or cell-to-cell variability which , in some systems , is known to importantly or even dominantly contribute to the total variability in the response [26 , 54] . The presence of such additional sources of variation by no means makes the model-free estimators inapplicable , as we show in S1 Fig where we study estimator performance in the simplest Example 1 model that includes cell-to-cell variability; it solely prevents us from comparing their performance to a tractably-computable MAP decoder result . To illustrate the use of our estimators in a realistic context , we analyzed data from two previously published papers . The first paper focused on the representation of environmental stress in the nuclear localization dynamics of several transcription factors ( here we focus on data for Msn2 , Dot6 , and Sfp1 ) in budding yeast [27] . The second paper studied information transmission in biochemical signaling networks in mammalian cells ( here we focus on data for ERK and Ca2+ ) [26] . In both cases , single-cell trajectory data were collected in hundreds or thousands of single cells sampled at sufficient resolution to represent the trajectories discretized at tens to hundreds of timepoints . Similarly , both papers estimate the information transmission in trajectories about a discrete number of environmental conditions: Ref [27] uses the linear SVM approach presented here , while Ref [26] uses the knn estimator . This makes the two datasets perfectly suited for estimator comparisons . We further note that in both datasets the trajectories can be divided into two response periods: the early “transient” response period when the external condition changes , and the late “near steady-state” response period . Typically , the transient dynamics exhibit clear differences in the trajectory means between various conditions , reminiscent of our Example 1 or early Example 2; in contrast , in the late period the response may have been adapted away , or the stimulus could be encoded only in higher-order statistics of the traces , reminiscent of the late period in Example 2 or Example 3 . Fig 8 shows the raw data and summarizes our estimation results for the early and late response periods for the three translocating factors in yeast that report on the change from 2% glucose rich medium to 0 . 1% glucose poor stress medium . Fig 9 similarly shows the raw data and estimation results for the early and late response periods for the signaling molecules in mammalian cells responding to multilevel inputs . Consistent with the published report [27] , transient response in yeast nuclear localization signal can be decoded well with the linear SVM estimator that yields about 0 . 6 bits of information per gene about the external condition . Kernelized SVM outperforms the linear method slightly by extracting an extra 0 . 1-0 . 2 bits of information , while knn underperforms the linear method significantly for Msn2 and Dot6 ( but not for Sfp1 ) . The Gaussian decoder estimate shows a mixed performance and the neural network estimate is the worst performer , most likely because the number of samples here is only N = 100 per input condition and neural network training is significantly impacted . It is interesting to look at the stationary responses in yeast which have not previously been analyzed in detail . First , low estimates provided by linear SVM for Msn2 and Dot6 imply that information in the stationary regime , if present , cannot be extracted by the linear classifier . Second , the Gaussian decoder also performs poorly in the stationary regime , potentially indicating that the relevant features are encoded in higher-than-pairwise order statistics of the response ( e . g . , pulses could be “sparse” features as in sparse coding [58] ) ; it is , however , hard to exclude small number of training samples as the explanation for the poor performance of the Gaussian decoder . Third , K-nearest-neighbor estimator also yields low estimates , either due to small sample number or low signal-to-noise ratio , the regime for which knn method has been observed to show reduced performance [40] . A particularly worrying feature of the knn estimates is their non-robust dependence on the length of the trajectory T . As S6 Fig shows , the performance of knn peaks at T ≈ 50 min and then drops , even well into unrealistic negative estimates for T ≈ 400 min ( corresponding to the highest dimensionality d = 170 of discrete trajectories ) . While it is possible to make an ad hoc choice to always select trajectory duration at which the estimate peaks , the performance of kernelized SVM is , in comparison , extremely well behaved and increases monotonically with T , as theoretically expected . Finally , nonlinear SVM estimator extracts up to 0 . 4 bits of information about condition per gene , more than half of the information in the early transient period . This is even though on average the response trajectories for the two conditions , 2% glucose and 0 . 1% glucose , for Msn2 and Dot6 are nearly identical . For Sfp1 there is a notable difference in the mean response , which the linear estimator can use to provide a ∼ 0 . 15 bits of information , yet still significantly below ∼ 0 . 4 bits extracted by the nonlinear SVM . For both transient and stationary responses in yeast , our results are qualitatively in line with the expectations from the synthetic example cases—given the small number of trajectories , tightest and most robust estimates are provided by the decoding information estimator based on nonlinear ( kernelized ) SVM . Regardless of the decoding methodology and even without small sample corrections at N = 100 trajectories per input , our estimates are not significantly impacted by the well-known information estimation biases thanks to the dimensionality reduction that decoding provides by mapping high dimensional trajectories X back into the space for inputs U which is low dimensional; this is verified in S7 Fig by estimating the ( zero ) information in trajectories whose input labels have been randomly assigned . Random pulses that encode stationary environmental signals have been observed for at least 10 transcription factors in yeast [53] and for tens of transcription factors in mammalian cells [59] . Recent studies investigated the role of the pulsatile dynamics in cellular decision-making [57 , 60] . Nevertheless , methods for quantifying the information encoded in stochastic pulses are still in their infancy . Our nonlinear SVM decoding estimates convincingly show that there is information to be learned at the single cell level from the stationary stochastic pulsing . An interesting direction for future work is to ask whether hand-crafted features of the response trajectories ( pulse frequency , amplitude , shape , etc ) can extract as much information from the trajectories as the generic SVM classifier: for that , one would construct for each response trajectory a “feature vector” by hand , compute the linear SVM decoding bound information estimate from the feature vectors , and compare that to the kernelized SVM estimate over the original trajectories . This approach is a generic and operationally-defined path for finding “sufficient statistics” of the response trajectories—or a compression of the original signal to the relevant set of features—in the information-theoretic sense . A different picture emerges from the mammalian signaling network data shown in Fig 9 . The key difference here is the order of magnitude larger number of sample trajectories per condition compared to yeast data . Most of the information seems linearly separable in both the early and late response periods , as evidenced by the success of the linear SVM based estimator whose performance is not improved upon by the kernelized SVM ( indeed , for early ERK response period linear SVM gives a slightly higher estimate than the nonlinear version ) . The big winner on this dataset is the neural-network-based estimator that yields the best performance in all conditions among the decoding-based estimators , likely owning to sufficient training data . As before , the Gaussian decoder shows mixed performance which can get competitive with the best estimators under some conditions . Lastly , knn appears to do well except on the late Ca2+ data ( perhaps due to low signal-to-noise ratio ) . It also shows counter-intuitive non-monotonic behavior with trajectory duration T in S8 Fig ( cf . with Fig 2C of Ref [27] , where the analysis of information conveyed in dynamical signals as a function of trajectory duration was also very revealing about signaling in yeast ) . Once again it is worth keeping in mind that knn is estimating the full mutual information which could be higher than the information decodable from single responses .
Increasing availability of single-cell time-resolved data should allow us to address open questions regarding the amount of information about the external world that is available in the time-varying concentrations , activation or localization patterns , and modification state of various biochemical molecules . Do full response trajectories provide more information than single temporal snapshots , as early studies suggest ? Is this information gain purely due to noise averaging enabled by observing multiple snapshots , or—more interestingly—due to the ability of these intrinsically high-dimensional signals to provide a richer representation of the cellular environment ? Can we isolate biologically relevant features of the response trajectories , e . g . , amplitude , frequency , pulse shape , relative phase or timing , without a priori assuming what these features are ? How can cells read out the environmental state from these response trajectories and how close to the information-theoretic bounds is this readout process ? More broadly , a framework for analyzing complete response trajectories in signaling or genetic regulatory networks at the single cell level could lead to architectural and functional constraints on the biological network [27] , and allow us to further pursue the ideas of optimal information representation in biological systems [9] . Here , we made methodological steps towards answering these questions by focusing on two related problems: first , if we are given a full stochastic description of a biochemical reaction network , under what conditions can we theoretically compute information transmission through this network and various related bounds; second , if we are given real data with no description of the network , what are tractable schemes to estimate the information transmission . We show that when the complete state of the reaction network is observed and the inputs are discrete sets of reaction rates , there exist tractable Monte Carlo approximation schemes for the information transmission . These exact results that we compute for three simple biological network examples then serve to benchmark a family of decoding-based model-free estimators and compare their performance to the commonly-used knn estimator . We show that decoding-based estimators can closely approach the optimal decoder performance and in many cases perform better than knn , especially with typical problem dimensions ( d ∼ 1 − 100 ) and typical number of sample trajectories ( N ∼ 102 − 103 ) . This is especially true when we ask about the combinatorial representation of the environmental state in the time trajectories of several jointly observed chemical species , as in our previous work [27] , where alternative information estimation methods usually completely fail due to the high dimensionality of the input space . It is necessary to emphasize the flexibility of the decoding approach: decoding-based information estimation is based directly on the statistical problems of classification ( for discrete input variable , U ) or regression ( for continuous input variable , U ) , so any classification / regression algorithm with good performance can provide the basis for information estimation . Concretely , for problems in the low data regime ( small N ) , linear or kernelized SVM approaches appear powerful , while at larger N neural-network-based schemes can provide a better performance and thus typically a tighter information lower bound . In contrast to information approximations for which it is often impossible to assess their precision or bias ( or even its sign ) when the dimension , d , of the problem is large , the decoding approach yields a conservative estimate of the true information . Statistical algorithms underlying decoding-based estimations have the extra advantage that , ( i ) , we may be able to gain biological insight by inspecting which features of the response carry the relevant stimulus information ( e . g . , by looking at the linear kernels or features that neural networks extract in their various layers ) ; ( ii ) , pick a decoding algorithm based on features previously reported as relevant ( e . g . , the Gaussian decoder for second-order statistics as in Example 3 ) ; ( iii ) , estimate the information as a function of trajectory duration; and ( iv ) , gain confidence in our estimates by testing their performance on withheld data . While we tested these estimators on a very restricted set of toy examples in order to be able to compare to analytically computed results , model-free decoding-based approaches are applicable more generally , e . g . , to complex , partially-observed reaction systems , or networks with significant contribution of cell-to-cell variability or extrinsic noise . By construction , decoding-based estimators only provide a lower bound to the true information . This , however , could turn out to be a smaller problem in practice than it appears in theory , especially for biochemical reaction networks . First , our extension to the Feder-Merhav bound provides us with an estimate of how large the gap between the true information and the decoded estimation can be . The bound is not tight on our examples , and can only be applied when the optimal MAP decoder can be constructed [61 , 62] . Second , and perhaps more importantly , information that can be decoded after single input presentations is the quantity that is likely more biologically relevant than the true channel capacity , if the organisms are under constraint to respond to the environmental changes quickly . Typically , organisms across the complexity scale operate under speed-accuracy tradeoffs [63]: faster decisions based on noisy information lead to more errors and , conversely , with enough time to integrate sensory information errors can be reduced . When speed is at a premium or relevant inputs are sparse , decisions need to be taken after single input presentations . In this case , decoding-based estimation should not be viewed as an approximate but rather as the correct methodology for the biological problem at hand . Of course , there is still the question of whether the model-free decoders that we use on real data can achieve a performance that is close to the optimal MAP decoder that represents the absolute performance limit . While there is no general way to answer this question , it appears that simple SVM decoding schemes work well when the response trajectories differ in their conditional mean , and neural networks as general approximators can be used to check for more complicated encoding features when data is plentiful . Unlike in neuroscience , there is much less clarity about what kind of read-out or decoding operations biochemical networks can mechanistically realize to mimic the functioning of our in silico decoders , and it may be challenging to biochemically implement even arbitrary linear classification of response trajectories . Until experimentally shown otherwise , it thus appears reasonable to proceed with the assumption that environmental signals can be read out from the time-dependent internal chemical state with a simple repertoire of computations . We also mention a caveat when using decoding-based estimators that rely on classification or regression methods with large expressive power , such as neural networks . While it is possible to successfully guard against overfitting within the same dataset using cross-validation , scientific insights into biological function often require generalization beyond one particular dataset . Typically , we ask for generalization at least over independent experimental replicates , but sometimes even over similar ( but not same ) external conditions , strains , or experimental setups . This can present a serious issue if e . g . , neural networks overfit to such systematic variations between replicates or conditions even when such variations are not biologically relevant . Regularization alone will not necessarily guard against this , unless the networks are actually trained over a subset of all data on which they will be tested . A pertinent recommendation here is to evaluate the difference in performance of expressive decoding-based estimators when trained over a subset or over all replicates , and to compare that to the generalization of less-expressive methods for which the sufficient statistics are known ( e . g . , linear or Gaussian decoders ) . We conclude by emphasizing a simple yet important point . The decoding-based approach that we introduced here should also motivate us to look beyond methodological problems of significance and estimation , to truly biological problems of cellular decision making . Currently , data on biological regulatory processes is often analyzed by looking for “statistically significant differences” in the network response for , say , two possible network inputs . For example , one may report that the steady-state mean expression level of a certain gene is significantly larger in the stimulated vs unstimulated condition , with the statistical significance of the mean difference established through an appropriate statistical test that takes into account the number of collected population samples . While statistical significance is a necessary condition to validly report any difference in the response , it is very different from the question of whether a single cell could discriminate the two conditions given access only to its own expression levels . In caricature , population-level statistics tell us with what confidence we , as scientists having access to N samples , can discriminate between conditions given some biological readout; decoding based information estimates , on the other hand , are relevant to the N = 1 case of individual cells . We hope that further work along the latter path can clarify and quantify better the difficult constraints and conditions under which real cells need to act based on individual noisy readouts of their stochastic biochemistry . | Cells represent changes in their own state or in the state of their environment by temporally varying the concentrations of intracellular signaling molecules , mimicking in a simple chemical context the way we humans represent our thoughts and observations through temporally varying patterns of sounds that constitute speech . These time-varying concentrations are used as signals to regulate downstream molecular processes , to mount appropriate cellular responses for the environmental challenges , or to communicate with nearby cells . But how precise and unambiguous is such chemical communication , in theory and in data ? On the one hand , intuition tells us that many possible environmental changes could be represented by variation in concentration patterns of multiple signaling chemicals; on the other , we know that chemical signals are inherently noisy at the molecular scale . Here we develop data analysis methodology that allows us to pose and answer these questions rigorously . Our decoding-based information estimators , which we test on simulated and real data from yeast and mammalian cells , measure how precisely individual cells can detect and report environmental changes , without making assumptions about the structure of the chemical communication and using only the amounts of data that is typically available in today’s experiments . | [
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"sciences"
] | 2019 | Estimating information in time-varying signals |
Experimental obstacles have impeded our ability to study prion transmission within and , more particularly , between species . Here , we used cervid prion protein expressed in brain extracts of transgenic mice , referred to as Tg ( CerPrP ) , as a substrate for in vitro generation of chronic wasting disease ( CWD ) prions by protein misfolding cyclic amplification ( PMCA ) . Characterization of this infectivity in Tg ( CerPrP ) mice demonstrated that serial PMCA resulted in the high fidelity amplification of CWD prions with apparently unaltered properties . Using similar methods to amplify mouse RML prions and characterize the resulting novel cervid prions , we show that serial PMCA abrogated a transmission barrier that required several hundred days of adaptation and subsequent stabilization in Tg ( CerPrP ) mice . While both approaches produced cervid prions with characteristics distinct from CWD , the subtly different properties of the resulting individual prion isolates indicated that adaptation of mouse RML prions generated multiple strains following inter-species transmission . Our studies demonstrate that combined transgenic mouse and PMCA approaches not only expedite intra- and inter-species prion transmission , but also provide a facile means of generating and characterizing novel prion strains .
Prion diseases are transmissible , fatal , and incurable neurodegenerative disorders of the central nervous system ( CNS ) that include bovine spongiform encephalopathy ( BSE ) , ovine scrapie , chronic wasting disease ( CWD ) of cervids and human Creutzfeldt-Jakob disease ( CJD ) . While inoculation of diseased brain material into individuals of the same species typically reproduces disease , studies of prion transmissions are complicated by prolonged , clinically silent incubation periods lasting months to years . Inter-species prion transmission is generally an even less efficient process , a phenomenon referred to as the species barrier [1] . While studies in transgenic ( Tg ) mice [2]–[6] and cell-free systems [7] , demonstrated the influence of PrP primary structure on prion transmission , agent strain properties are an equally important determinant . Thus , the time between inoculation and onset of clinical signs , referred to as the incubation time , is a parameter that varies between strains . Different strains may also induce distinct clinical signs in inoculated animals . Neuropathologically , strains are distinguished by reproducible differences in the distribution of spongiform degeneration of the cerebral grey matter , and by the deposition of PrPSc , occasionally in the form of amyloid plaques . While the different strain properties of conventional pathogens are genomically encoded , it is less clear how multiple disease phenotypes can be accommodated in the context of a ‘protein only’ mechanism of pathogenesis where the infectious agent lacks nucleic acid . Numerous studies suggest that strain diversity is enciphered in the higher order structure of PrPSc [8]–[12]; accordingly , the biochemical properties of PrPSc have also been used as a means of typing prion isolates [13] , [14] . While prion strain properties are stably maintained upon passage within a particular species , inter-species prion transmission may result in the acquisition of new strain properties , the most profound of which may be host range alteration [15] , [16] . Thus , the species tropism of novel prion strains currently cannot be predicted . A powerful demonstration of the unpredictable influence of prion strains on species barriers is highlighted in the case of BSE . Cattle feed derived from rendered meat and bone meal which was contaminated with prions , possibly originating from scrapie-infected sheep , is the suspected origin of BSE [17] . BSE-related prion diseases were subsequently identified in domestic and captive wild cats [18] , [19] and exotic ungulates . The recognition that a variant of CJD ( vCJD ) is caused by the BSE prion strain [20]–[23] raised major public health concerns . Like BSE , the origin of transmissible mink encephalopathy ( TME ) of ranch-raised mink , is thought to be prion-contaminated feed [24] . Recent years have witnessed the emergence of additional novel mammalian prion strains . Atypical scrapie is a recently-recognized and surprisingly prevalent prion disease of sheep of unknown origin and host-range . First reported in Norwegian sheep in 2003 and referred to as Nor98 [25] , atypical scrapie appears to be a single , unique scrapie strain [26] , [27] infecting sheep with PRNP genotypes usually associated with resistance to classical scrapie . The increasing geographic range , contagious transmission , uncertain strain prevalence , and environmental persistence of CWD are also of concern . Uncontrolled prion dissemination in wild cervid populations brings into question the risk of transmission to other species , for example via shared grazing of CWD-contaminated rangeland . Insights into the factors controlling prion transmission and host-range adaptation are clearly of paramount importance for containing further prion epidemics . The aim of this study was to evaluate the feasibility of expediting studies of intra- , and inter-species prion transmission by combining the resources of protein misfolding cyclic amplification ( PMCA ) [28] with Tg mouse models of prion disease . During PMCA , the normal form of PrP , referred to as PrPC , is converted into protease-resistant PrP using small amounts of infectious PrPSc . Continued recruitment and conversion of PrPC by PrPSc is accomplished by sonication in a process analogous to amplification of DNA by the polymerase chain reaction [29] . We previously showed that Tg mice expressing PrP from mule deer , referred to as Tg ( CerPrP ) mice , are susceptible to prions from deer and elk dying of CWD [30]–[32] . Several other groups subsequently confirmed these observations using similar mouse models [33]–[37] . Here we used cervid PrPC ( CerPrPC ) expressed in the brains of Tg mice for the generation of CWD prions by PMCA . Using Tg ( CerPrP ) mice to characterize this in vitro-generated infectivity we demonstrate that PMCA results in the high fidelity amplification of CWD prions with apparently unaltered strain properties . In addition , while adaptation of mouse prions to form novel cervid prions required several hundred days in Tg ( CerPrP ) mice , we show that PMCA abrogated this barrier to prion transmission resulting in the rapid generation of novel cervid prions with similar properties .
A PMCA reaction was established using a CWD prion seed in a 10% brain homogenate from diseased mule deer 04-22412 , diluted 10-fold into 10% brain homogenate prepared from perfused Tg ( CerPrP ) 1536+/− mice [30] . Following a round of PMCA consisting of alternating periods of sonication and incubation for 36 cycles , the product , which contained amplified protease-resistant CerPrP ( Fig . 1A ) , was diluted 10-fold into another reaction containing CerPrPC from Tg ( CerPrP ) 1536+/− mouse brain homogenate for a further round of PMCA . This process of serial PMCA was repeated for 22 rounds . In accordance with previous studies using the experimentally-adapted hamster scrapie isolate 263K [38] , [39] , and the experimentally-adapted Chandler scrapie isolate [40] , protease-resistant CerPrP was amplified to high levels during each round of serial PMCA ( Fig . 1A and C ) . In contrast , after 10 rounds of serial PMCA of six duplicated samples of a healthy Tg ( CerPrP ) 1536+/− brain extract , no protease-resistant PrP was produced in the absence of prion seeds ( data not shown ) . To ascertain whether this process resulted in the in vitro amplification of CWD prions , Tg ( CerPrP ) 1536+/− mice were intracerebrally challenged with the product of 22 rounds of serial PMCA ( Fig . 1C ) . A separate cohort was inoculated with a 1% brain homogenate of the CWD-infected 04-22412 mule deer isolate that was the seed for the initial round of PMCA . In both cases , Tg ( CerPrP ) 1536+/− mice were inoculated with preparations containing similar amounts of protease-resistant CerPrP , as determined by Western blot analysis ( Fig . 1C ) . Serial PMCA reactions initially seeded with 04-22412 CWD prions but using Prnp0/0 knockout instead of Tg ( CerPrP ) 1536+/− mouse brain homogenate were also performed in parallel . A cohort of Tg ( CerPrP ) 1536+/− mice inoculated with this material after 22 rounds of serial PMCA served as negative controls to show that the original CWD inoculum was not detectable . All Tg ( CerPrP ) 1536+/− mice ( n = 6 ) inoculated with material derived from serial PMCA of 04-22412 CWD prions using CerPrPC from the brains of Tg ( CerPrP ) 1536+/− mice developed disease with a mean incubation time of 263±28 ( mean±standard error ) days ( d ) ( Fig . 2 ) . Consistent with previous results [30]–32 , CWD prions from the brain of diseased 04-22412 mule deer also induced disease in Tg ( CerPrP ) 1536+/− mice ( n = 6 ) with an incubation time of 284±22 d . The clinical signs that accompanied prion disease were identical in both cases , and included truncal ataxia and slowed movement , increased tone of the tail , dorsal kyphosis , head bobbing or tilting , and roughened coat . Confirming that prion infectivity produced by 22 rounds of serial PMCA was unrelated to persistence of the initial 04-22412 CWD prion seed , no disease was registered in Tg ( CerPrP ) 1536+/− mice inoculated with preparations from the negative control reaction in which 04-22412 CWD prions were seeded into Prnp0/0 brain homogenate followed by 22 rounds serial PMCA ( Fig . 2 ) . Detection of CerPrPSc in the brains of diseased Tg ( CerPrP ) 1536+/− mice by Western blotting ( Fig . 3A ) , histoblotting ( Fig . 4 ) , and immunohistochemical analysis ( Fig . 5B–E ) confirmed that the clinical signs following inoculation with CWD prions or the amplified samples were the consequence of prion disease . Collectively these results demonstrate that serial PMCA resulted in the efficient in vitro production of infectious CWD prions . We therefore refer to infectivity in the amplified samples as PMCA CWD prions . In order to fully evaluate the biochemical and neuropathological characteristics of disease induced by both inocula , we performed detailed comparative studies of the brains of mice infected with naturally occurring and PMCA CWD prions . The similar incubation times of naturally occurring and PMCA CWD prions ( Fig . 2 ) raised the possibility that the strain properties of the 04-22412 CWD prion isolate were maintained during serial PMCA . Consistent with this notion , the electrophoretic mobilities ( Fig . 3A ) and glycosylation profiles of CerPrPSc produced in the brains of Tg ( CerPrP ) 1536+/− mice inoculated with both preparations were similar ( Fig . 3D ) . Assessment of the neuroanatomical distribution of PrPSc by histoblotting is another parameter that has been used to characterize prion strains [20] , [21] , 32 , 41 , 42 . The appearance and distribution of CerPrPSc throughout histoblotted brain sections of diseased Tg ( CerPrP ) 1536+/− mice infected with 04-22412 CWD ( Fig . 4A ) or PMCA CWD prions ( Fig . 4B ) were similar ( n = 1 in each group ) . Markedly punctate accumulations of CerPrPSc were present in histoblotted brain sections of mice infected with both naturally occurring and PMCA CWD prions , either prior to , or following treatment with proteinase K ( PK ) . CerPrPSc-containing aggregates often coalesced into larger immunoreactive structures . Similar aggregation and distribution of CerPrPSc has been reported in Tg ( CerPrP ) 1536+/− mice infected with various naturally occurring deer and elk CWD isolates [30] , [32] . The accumulation of CerPrPSc in plaques was confirmed by immunohistochemical analyses of brains from diseased Tg ( CerPrP ) 1536+/− mice infected with naturally occurring CWD prions ( Fig . 5B and C ) and PMCA CWD prions ( Fig . 5D and E ) . The distribution of immunoreactive plaques and accompanying spongiform degeneration was similar in both cases ( Fig . 5B–E ) , with plaques often coalescing into larger structures frequently bordered by vacuoles ( Fig . 5E ) . Previous studies showed the unfolding characteristics of PrPSc to be a sensitive and quantitative means of assessing strain-dependent differences in PrPSc conformation [11] , [12] , [32] , [43] . We therefore determined the relative stabilities of CerPrPSc in the brains of Tg ( CerPrP ) 1536+/− mice infected with PMCA-generated or in vivo-derived CWD prions . Brian extracts were treated with increasing concentrations of guanidine hydrochloride ( GdnHCl ) , followed by PK digestion and analysis of residual CerPrPSc by Western blotting . When plotted , the mean amounts of PK-resistant PrP in the brains of three diseased Tg ( CerPrP ) 1536+/− mice at each concentration of denaturant , formed sigmoidal curves . The transition point at the concentration where half the CerPrPSc in the samples was denatured is referred to as the mean GdnHCl1/2 value . Similar denaturation properties and mean GdnHCl1/2 values indicated comparable CerPrPSc stabilities following infection with 04-22412 CWD and PMCA CWD prions ( Fig . 6A and B ) . Collectively , the concordant clinical and histological profiles of Tg ( CerPrP ) 1536+/− mice infected with naturally occurring and PMCA-derived CWD prions , as well as the similar biochemical properties of the resulting CerPrPSc , indicate that the characteristics of 04-22412 CWD prions were maintained during serial PMCA . While deer and elk CWD prions propagated efficiently in Tg ( CerPrP ) 1536+/− mice , with 100% rates of transmission and mean incubation times ranging from ∼225 to 270 d , our previous studies showed that Tg ( CerPrP ) 1536+/− mice remained free of prion disease for >1 year after infection with mouse RML prions [30] . To fully characterize the extent of this transmission barrier , we challenged additional Tg ( CerPrP ) 1536+/− mice with mouse RML prions and extended our observations beyond one year . While all RML-inoculated Tg ( CerPrP ) 1536+/− mice ( n = 9 ) eventually developed clinical signs , the time to disease onset was protracted and highly variable ( mean incubation time , 489±22 d; range of disease onset ∼400 to 590 d ) ( Fig . 2 ) . In contrast to the predominantly monoglycosylated profile of mouse PrPSc in the brains of RML infected wild type FVB mice ( Figs . 1C , 3B and 3D ) , CerPrPSc produced in the brains of RML infected Tg ( CerPrP ) 1536+/− mice was predominantly diglycosylated ( Fig . 3B , C and D ) . This suggested that adaptation of mouse RML prions occurred following transit across a species barrier in mice expressing CerPrPC . Histoblot analysis revealed variable distribution and aggregation of CerPrPSc in the CNS of two diseased Tg ( CerPrP ) 1536+/− mice infected with mouse RML prions ( Fig . 7 ) . The deposition of CerPrPSc in the brain of Tg ( CerPrP ) 1536+/− mouse #4825 , which developed disease 512 d after infection , was widespread and diffuse . In contrast , CerPrPSc in the brain of Tg ( CerPrP ) 1536+/− mouse #5302 , which developed disease after 488 d , accumulated in small , discrete plaques . The punctate staining observed in mouse #5302 differed from the CerPrPSc aggregates in Tg ( CerPrP ) 1536+/− mice infected with naturally occurring or PMCA CWD prions ( Fig . 4 ) , which were deposited in different brain regions , and frequently coalesced into larger immunoreactive structures . Immunohistochemical analysis of additional RML-infected Tg ( CerPrP ) 1536+/− mouse brains confirmed that CerPrPSc deposition and aggregation varied between animals . While diffuse CerPrPSc staining characterized the CNS of animal #5297 ( Fig . 5G ) , CerPrPSc accumulated in small plaques in the CNS of animal #5300 ( Fig . 5H ) . The brain of Tg ( CerPrP ) 1536+/− mouse #4827 that developed disease 394 d after infection with mouse RML prions was prepared for serial transmission studies . Infectivity in the brain of this mouse , referred to as Cer/RML-4827 prions , induced disease in Tg ( CerPrP ) 1536+/− mice ( n = 8 ) with a mean incubation time of 148±5 d ( Fig . 2 ) . This substantial reduction in time to onset of disease , as well as the narrow range of incubation times on second passage , is characteristic of prion adaptation following transit across a species barrier . The diglycosylated CerPrPSc pattern that characterized infection of Tg ( CerPrP ) 1536+/− mice with mouse RML prions was maintained upon passage of Cer/RML-4827 prions to Tg ( CerPrP ) 1536+/− mice ( Fig . 3C and D ) . Histoblot analysis of a recipient Tg ( CerPrP ) 1536+/− mouse brain #7263 showed that infection with Cer/RML-4827 prions was characterized by diffuse rather than punctate CerPrPSc distribution ( Fig . 7 ) . The denaturation profile of CerPrPSc in the brains of Tg ( CerPrP ) 1536+/− mice infected with Cer/RML-4827 prions differed from mouse PrPSc in the brains of RML-infected FVB mice ( mean GdnHCl1/2 values of 1 . 27 M and 1 . 57 M respectively , Fig . 6C and D ) . This conformational difference is consistent with adaptation of mouse RML prions following replication in Tg ( CerPrP ) 1536+/− mice . Moreover , the denaturation profile of CerPrPSc produced in response to infection with Cer/RML-4827 prions was considerably different from the profiles of CerPrPSc produced following infection with naturally occurring or PMCA-derived CWD prions ( mean GdnHCl1/2 values of 1 . 27 M and 2 . 7–2 . 8 M respectively , Fig . 6A and B ) . This indicated that adaptation of mouse RML prions in Tg ( CerPrP ) 1536+/− mice resulted in the formation of novel cervid prions with a conformation distinct from CWD . We investigated whether PMCA could abrogate the barrier to mouse RML prion transmission that was ultimately breached following adaptation in Tg ( CerPrP ) 1536+/− mice . Brain homogenates from uninfected Tg ( CerPrP ) 1536+/− mice were seeded with mouse RML prions and 22 rounds of serial PMCA were performed as before . Protease-resistant CerPrP was amplified to high levels during each round of serial PMCA , culminating in 22 rounds ( Fig . 1B and C ) . The change in PrPSc glycoform pattern that occurred following passage of mouse RML prions from wild type to Tg ( CerPrP ) 1536+/− mice ( Fig . 3B and D ) also appeared to be a feature of RML adaptation during serial PMCA , with protease-resistant CerPrP becoming predominantly diglycosylated at round 2 and thereafter ( Fig . 1B ) . No protease-resistant PrP was produced in the absence of prion seeds after 10 rounds of serial PMCA of six duplicated samples of a healthy Tg ( CerPrP ) 1536+/− brain extract ( data not shown ) . Remarkably , inoculation of Tg ( CerPrP ) 1536+/− mice with this PMCA-adapted material , rapidly induced disease in all inoculated Tg ( CerPrP ) 1536+/− mice ( n = 7 ) . The 143±6 d mean incubation time was strikingly similar to the ∼150 d mean incubation time of Cer/RML-4827 prions that resulted from adaptation of RML prions following replication in Tg ( CerPrP ) 1536+/− mice ( Fig . 2 ) . This indicated that the barrier to inter-species transmission of mouse RML prions , which requires several hundred days of adaptation in Tg ( CerPrP ) mice and stabilization on serial passage , can be directly bypassed by serial PMCA of RML using CerPrPC from Tg mouse brain . We therefore refer to infectivity in this amplified sample as PMCA Cer/RML prions . Using Western blotting , conformational stability assays , and histoblotting we characterized the properties of CerPrPSc produced in the brains of diseased Tg ( CerPrP ) 1536+/− mice infected with PMCA Cer/RML prions . The diglycosylated profile of CerPrPSc in the PMCA Cer/RML inoculum ( Fig . 1C ) was maintained in the brains of diseased Tg ( CerPrP ) 1536+/− mice ( Fig . 3C and D ) . The denaturation profile and mean GdnHCl1/2 value of CerPrPSc in the brains of Tg ( CerPrP ) 1536+/− mice infected with PMCA Cer/RML prions was equivalent to Cer/RML-4827 , but different from RML in wild type FVB mice or from naturally occurring or PMCA-derived CWD prions in Tg ( CerPrP ) 1536+/− mice . This indicated that , similar to the adaptation of RML in Tg ( CerPrP ) 1536+/− mice , serial PMCA resulted in adaptation of mouse RML prions to produce novel cervid prions with a CerPrPSc conformation distinct CWD prions . Histoblotting revealed a consistent pattern of CerPrPSc distribution in the brains of two Tg ( CerPrP ) 1536+/− mice infected with PMCA Cer/RML prions ( Fig . 7 ) . The diffuse CerPrPSc deposition in the brains of two such mice , referred to as #5294 and #5295 , was distinct from the small punctate staining pattern in the CNS of Tg ( CerPrP ) 1536+/− #5302 mouse infected with RML , or the large CerPrPSc deposits in the CNS of Tg ( CerPrP ) 1536+/− mice infected with CWD prions ( Fig . 4 ) . Comparison of the histoblot patterns in #5294 and #5295 mice with Tg ( CerPrP ) 1536+/− mouse #7263 that was infected with Cer/RML-4827 prions also revealed subtle differences in the regional distribution of CerPrPSc with , for example , relative sparing of the corpus callosum in the #5294 and #5295 mice infected with PMCA Cer/RML prions ( Fig . 7 ) . These differences in CerPrPSc distribution suggest that subtle strain differences distinguish Cer/RML-4827 and PMCA Cer/RML prions . Collectively , the similar rapid incubation times of PMCA Cer/RML and Cer/RML-4827 prions , and the distinctive properties of the resulting CerPrPSc , demonstrate that both processes produced novel cervid prions with biological properties distinct from CWD . Our findings therefore indicate that serial PMCA substituted for the long-term process of RML prion adaptation in Tg ( CerPrP ) 1536+/− mice . Nonetheless , our histoblotting and immunohistochemical analyses show that adaptation of RML prions in Tg ( CerPrP ) 1536+/− mice resulted in the formation of at least two distinct isolates , and that PMCA adaptation likely resulted in a third . These observations indicate that multiple isolates with different strain properties may be produced during the process of prion adaptation following inter-species transmission .
The studies reported here are significant in showing that PrPSc and CWD prion infectivity from diseased deer brain are faithfully reproduced in vitro by PMCA using CerPrPC from the brains of Tg ( CerPrP ) 1536+/− mice as the substrate for amplification . They extend previous reports using Tg ( CerPrP ) 1536+/− mice [44] by showing that PMCA-derived CWD prions induce disease and the production of CerPrPSc in Tg ( CerPrP ) mice as efficiently as prions isolated from the CNS of deer with CWD . Tg mice represent a convenient , controlled source of PrPC for PMCA with significant advantages over PrPC from animals or humans . Any form of transgene-derived PrP , either mutated or PrPC from different species , can be readily overexpressed on a Prnp0/0 background , and the brains of Tg mice can be appropriately prepared for use in PMCA . Underscoring this concept , brain homogenate from Tg mice expressing human PrP was recently used to amplify PrPSc from the brains of variant CJD patients by PMCA [45] . As we show here , Tg mice also provide a crucial additional resource in which to fully characterize the biological properties of PMCA-derived prions . These studies raise the prospect of using PMCA and Tg mice expressing mutant and wild type PrP , and polymorphic variants thereof , from cervids , humans , cattle , sheep , rodents , horses , and other mammals , to characterize the strain and host-range properties of naturally occurring prion strains . Our studies not only reassuringly support previous demonstrations that serial PMCA reproduces experimentally-adapted scrapie 263K prions [38] , [46] , but also demonstrate ( to our knowledge for the first time ) cell-free amplification of naturally occurring prion infectivity . Previous serial PMCA of the 263K isolate resulted in the generation of prions with apparently lower specific infectivity than brain-derived infectious material [38] , [46] . Here we show that the mean incubation times of naturally occurring 04-22412 CWD and PMCA-derived CWD prion preparations in Tg ( CerPrP ) 1536+/− mice were comparable . Since each inoculum comprised similar amounts of CerPrPSc , this suggests that equivalent levels of CWD prion infectivity were present in each case . While the reason for the discrepant behavior of 263K and 04-22412 CWD prions is unknown , the suggestion that PMCA may have generated a different prion strain after repeated in vitro amplification of 263K prions [38] appears unlikely in the case of PMCA of CWD prions . To analyze and compare the strain properties of PMCA CWD and naturally occurring CWD prions we analyzed several independent criteria previously used to characterize prion strains . These included the induction of clinical signs in mice , the electrophoretic migration and glycoprofiles of CerPrPSc by Western blotting , PrPSc deposition by histoblot , cerebral vacuolization and PrPSc deposition by immunohistochemistry , and the denaturation characteristics of PrPSc . Based on these criteria , it appears that PMCA CWD prions retain the biological and biochemical properties of the originating CWD prions . However , we realize that each approach is limited in its ability to unequivocally define strain variation . For example , subtle differences in the pattern of PrPSc deposition in histoblots of individual mice may result from slight variances in the locations of coronal sections between mice , or from the times at which mice were sacrificed . The sensitive and specific paraffin-embedded tissue ( PET ) blot technique [47] may provide finer resolution for future comparative analyses . Furthermore , while the indistinguishable denaturation profiles and GdnHCl1/2 values of CerPrPSc in the brains of Tg mice dying from infection with naturally occurring or PMCA-derived CWD prions suggests comparable CerPrPSc structures , equivalent conformational stability does not necessarily indicate invariant conformations at all structural levels . Other approaches may reveal evidence of PrPSc structural differences . For example , infrared-spectroscopy distinguished the secondary structures of protease-resistant PrP from two hamster scrapie strains when immunobiochemical typing failed to detect differences [48] . Fourier transform infrared-spectroscopy was also used to compare secondary structures of PMCA-generated and brain-derived protease-resistant PrP [38] as well as protease-resistant PrP products from seeded polymerization of recombinant PrP ( rPrP-PMCA ) [49] . Strain adaptation experiments , traditionally performed in vivo , often require years to generate prions with stable biological properties . While investigating the susceptibility of Tg mice to prions from other species provides a feasible approach to address the potential for inter-species prion transmission , the studies reported here demonstrate that abrogating the barrier to mouse RML prion transmission in Tg ( CerPrP ) mice required several hundred days followed by strain stabilization after serial passage . Our previous studies showed that Tg ( CerPrP ) 1536+/− are also susceptible to sheep SSBP/1 scrapie prions , but with apparently less of a transmission barrier than mouse RML prions [32] . An important sequel to inter-species prion transmission is frequently the acquisition of new strain properties . The different patterns of CerPrPSc deposition in diseased Tg ( CerPrP ) 1536+/− mice following RML infection indicates that abrogation of this transmission barrier most likely results in the formation and propagation of different prion isolates in individual mice . In this case we observed two general patterns by histoblotting and immunohistochemistry: diffuse CerPrPSc deposition in mice #4825 and #5297; and small plaque deposits in the case of mice #5302 and #5300 . Materials from the histoblotted #4825 and #5302 mice were not available for serial transmission studies . At the time of writing , serial transmissions of prions from the brains of mice #5297 and #5300 are ongoing . The brain of mouse #4827 was used for serial transmission and full strain characterization in Tg ( CerPrP ) 1536+/− mice . The substantial reduction and consistent time to onset of disease following serial passage of Cer/RML-4827 prions is characteristic of prion adaptation following transit across a species barrier . The ∼150 d mean incubation period of Cer/RML-4827 prions is ∼100 days shorter than either CWD or PMCA-generated CWD prions ( Fig . 2 ) , indicating adaptation of RML in Tg ( CerPrP ) 1536+/− mice and the production of novel cervid prions with biological properties distinct from CWD . Additional detailed comparisons with Tg ( CerPrP ) mice infected with either naturally occurring or PMCA CWD prions were consistent with the notion that the biological properties of Cer/RML-4827 prions were distinct from CWD . In accordance with previous studies of experimentally-adapted hamster prion isolates in Tg mice expressing artificial chimeric PrP genes , which indicated that a change in the conformation of PrPSc accompanied the emergence of a new prion strain [11] , the conformational stability of PrPSc changed following passage of mouse RML prions from wild type mice and subsequent adaptation to form Cer/RML-4827 prions in Tg ( CerPrP ) 1536+/− mice ( Fig . 6 ) . Also in accordance with the process of prion adaptation , the profile of RML PrPSc glycosylation changed following transmission to Tg ( CerPrP ) 1536+/− mice ( Fig . 3 ) , and there were distinct differences in the morphology and neuroanatomical distribution of PrPSc in Tg ( CerPrP ) 1536+/− mice infected with Cer/RML-4827 and CWD prions . We show that the adaptation of mouse RML prions , which required two passages in Tg ( CerPrP ) mice , can be accomplished by in vitro amplification of CerPrPC with heterologous RML prions to create cervid adapted RML prions in a matter of weeks . Accompanying this adaptation , the RML glycopattern changed from predominantly mono- to diglycosylated PrPSc , which is the form of CerPrPSc propagated in Tg ( CerPrP ) mice infected with RML , Cer/RML-4827 prions , and PMCA Cer/RML prions . Whereas 22 rounds were used to ensure the elimination of residual prion seed in the initial round of PMCA , it seems likely that PMCA-mediated inter-species transmissions can be accomplished with many fewer rounds of serial PMCA . Whether PMCA-mediated adaptation occurs with structural intermediates similar to the process in vivo is currently not known; however , since the properties of the initiating and resulting prions are , in most cases , likely to be distinct , it should be possible to determine the kinetics of prion adaption at each PMCA round . Should it be possible to reproducibly manipulate the extent of prion adaptation by varying the number of rounds of serial PMCA , then mechanistic studies of prion adaptation following inter-species transmission are likely to be considerably expedited by this approach . Our studies convincingly show that PMCA of murine RML prions using Tg ( CerPrP ) 1536+/− brain homogenate generates a novel strain of cervid-adapted prions with properties distinct from either naturally occurring or PMCA-generated CWD prions . It currently is less clear whether the PrPSc structures and strain properties of amplified and in vivo derived prions are equivalent . Direct comparisons of the strain properties of PMCA Cer/RML cervid prions and in vivo-adapted strains are complicated by our observations that in vivo adaptation gives rise to individual isolates with different strain-related properties , at least as judged by histoblot and immunohistochemical profiles of PrPSc . While certain strain-related attributes , including comparably rapid prion incubation times , and similar denaturation profiles of CerPrPSc after infection , suggest shared biological properties between Cer/RML-4827 and PMCA Cer/RML prions , other differences , including targeting of cerebral PrPSc deposition of Tg ( CerPrP ) 1536+/− mice infected with Cer/RML-4827 and PMCA Cer/RML prions , point to divergent strain properties and thus would rather argue for different strains . Whether inter-species PMCA-mediated prion adaptation also results in the generation of multiple and distinct prion strains remains to be determined , but our limited analyses consisting of uniform histoblot profiles , reproducible onsets of disease , and similar conformation stabilities of CerPrPSc in individual infected mice may indicate that PMCA selectively and stably propagates distinct strains following abrogation of a species barrier . Finally , we note that under certain conditions , PMCA may result in the spontaneous formation of PK-resistant PrP species [40] and de novo generated infectivity under conditions that do not involve seeding with infectious prions [50] . While it would be of considerable interest to determine the biological properties of spontaneously-produced cervid prions by PMCA of CerPrPC , we feel that the possibility of spontaneous generation of infectivity in the context of the current studies is remote . The experiments of Deleault and co-workers using purified PrPC plus poly ( A ) RNA , indicated that spontaneous generation of PrPSc was a stochastic and relatively infrequent event , estimated at <1 conversion event per 6×1011 input PrPC molecules per PMCA round . Consequently , amplification of preexisting PrPSc molecules was considered an unlikely origin for PrPSc formation under these conditions . In contrast , in the studies reported here where PMCA reactions were seeded with either CWD or RML prions , high levels of protease-resistant PrP were amplified at each round of serial PMCA and remained consistently so during both intra- and inter-species PMCA-mediated prion amplification ( Fig . 1A and B ) . In control experiments using unseeded healthy Tg ( CerPrP ) 1536+/− brain extract , 10 passages of serial PMCA failed to generate protease-resistant PrP ( data not shown ) . While we have also spontaneously generated PrPSc without the addition of prion seeds ( Soto and co-workers , unpublished results; Castilla and co-workers , unpublished results ) , the PMCA conditions required to accomplish this required modification from the standard PMCA conditions used in the current and previous studies . Standard serial PMCA conditions in which healthy hamster brain homogenate was serially diluted into itself in the absence of prion seed failed to produce protease-resistant PrP following the same number of PMCA cycles which resulted in amplification of hamster 263K prions [38] . In a larger experiment , samples of healthy brain homogenate from 10 different mice and hamsters were subjected to serial rounds of PMCA amplification in the absence of PrPSc seed using the PMCA conditions used in study . Following 20 rounds of serial PMCA , we did not observe de novo formation of PrPSc , nor did these materials , when inoculated into wild-type animals , induce disease after >400 d ( Soto and co-workers , unpublished observations ) . For these reasons we feel that the generation of PrPSc and cervid prions reported in the present study , when normal brain homogenate from Tg ( CerPrP ) 1536+/− mice was mixed with prion seeds , is unlikely to be influenced by spontaneous , de novo generated infectivity .
Healthy Tg ( CerPrP ) 1536+/− mice were perfused with phosphate-buffered saline ( PBS ) plus 5 mM EDTA immediately prior to harvesting the tissue . Ten % brain homogenates ( w/v ) were prepared in conversion buffer which consisted of PBS containing NaCl 150 mM , 1 . 0% Triton X-100 , and the complete™ cocktail of protease inhibitors ( Roche , Mannheim , Germany ) . The samples were clarified by a brief , low-speed centrifugation ( 1500 rpm for 30 s ) using an Eppendorf centrifuge ( Hamburg , Germany ) . A 1∶10 dilution of 10% brain homogenate from clinically sick 04-22412 infected mule deer or RML infected mice was diluted into a 10% brain homogenate from Tg ( CerPrP ) 1536+/− mice . Samples in 0 . 2 ml PCR tubes were positioned on an adaptor placed on the plate holder of a microsonicator ( Misonix Model 3000 , Farmingdale , NY ) . Each PMCA cycle consisted of 30 min incubation at 37°C followed by a 20 s pulse of sonication set at potency of 7 . Samples were incubated without shaking immersed in the water of the sonicator bath . After a round of 36 cycles , a 10 µl aliquot of the amplified material was diluted into 90 µl of additional Tg ( CerPrP ) 1536+/− mouse brain homogenate and a new round of 36 PMCA cycles was performed . This procedure was repeated for 22 rounds . The detailed protocol for PMCA , including reagents , solutions and troubleshooting , has been published elsewhere [39] , [51]–[53] . Tg mice expressing deer PrP , referred to as Tg ( CerPrP ) 1536 have been described previously [30] . While we showed that CWD prion incubation times are more rapid in Tg ( CerPrP ) 1536 homozygous for the transgene array than hemizygous Tg ( CerPrP ) 1536 mice [30] , because of difficulties associated with breeding homozygous Tg ( CerPrP ) 1536 mice we have maintained this line in the hemizygous state by breeding with Prnp0/0 mice . Such mice are therefore referred to as Tg ( CerPrP ) 1536+/− mice . CWD prions were derived from a diseased female mule deer , referred to as 04-22412 UWVS ESW/JEJ , but abbreviated here as 04-22412 . The animal was homozygous for the polymorphic codon 225 , encoding serine at this location . The RML isolate was originally a kind gift from Byron Caughey ( Laboratory of Persistent Viral Diseases , Rocky Mountain Laboratories , Hamilton , MT ) and was passaged by intracerebral inoculation of inbred FVB/N mice at the University of Kentucky . Ten % ( w/v ) homogenates , in phosphate buffered saline ( PBS ) lacking calcium and magnesium ions , of cervid and mouse brains were prepared by repeated extrusion through an 18 gauge followed by a 21 gauge syringe needle . Groups of anesthetized mice were inoculated intracerebrally with 30 µl of 1% ( w/v ) brain extracts prepared and diluted in PBS , or 1% v/v of the final PMCA product diluted in PBS . Inoculated mice were diagnosed with prion disease following the progressive development of at least three signs including truncal ataxia , ‘plastic’ tail , loss of extensor reflex , difficulty righting , and slowed movement . The time from inoculation to the onset of definitive and subsequently progressive clinical signs is referred to as the incubation time . For PrP analysis in brain extracts , total protein content from 10% brain homogenates prepared in PBS was determined by bicinchoninic acid ( BCA ) assay ( Pierce Biotechnology Inc . , Rockford , IL ) . Brain extracts were either untreated or treated with 40 µg/ml PK for one hour at 37°C in the presence of 2% sarkosyl . Protease digestion was terminated with 4 mM phenyl methyl sulfonyl fluoride ( PMSF ) . Proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) . Proteins thus resolved were electrophoretically transferred to PVDF-FL membranes ( Millipore , Billerica , MA ) . Membranes were probed with mAb 6H4 [54] , or the Hum-P anti-PrP recombinant Fab [55] followed by horse radish peroxidase-conjugated sheep anti-mouse IgG or goat anti-human secondary antibody respectively . Signal was developed using ECL-plus detection ( Amersham ) , and analyzed using a FLA-5000 scanner ( Fuji ) . Histoblots of 10 µm thick cryostat sections were generated and transferred to nitrocellulose as previously described [41] . Histoblots were immunostained with the Hum-P anti-PrP recombinant Fab followed by alkaline phosphatase-conjugated goat anti-human secondary antibody . Images were captured using a Nikon SM21000 microscope with Photometrics Coolsnap CF digital imager and processed using MetaMorph software The unfolding characteristics of PrPSc in brain homogenates of terminally sick mice were analyzed using a Western blot-based conformational stability assay [12] , [32] , [43] which is a modification of the original ELISA based protocol [11] . Analysis of PrP in the brains of Tg mice by immunohistochemistry was performed as previously described [56] using anti-PrP mAb 6H4 [54] as primary antibody and IgG1 biotinylated goat anti-mouse secondary antibody ( Southern Biotech ) . Digitized images for figures were obtained by light microscopy using a Nikon Eclipse E600 microscope equipped with a Nikon DMX 1200F digital camera . | Prions are unique pathogens that result from conversion of a normal host-encoded prion protein , PrPC , into a self-propagating , disease-associated conformation , referred to as PrPSc . An important aspect of prion diseases is their transmissibility , frequently as epidemics . The contagious transmission of chronic wasting disease ( CWD ) of deer and elk is of particular concern . The elements governing prion transmission between species , including the influence of agent strain properties , remain enigmatic , in large part because of considerable difficulties associated with experimental manipulation of prions . The aim of this study was to evaluate the feasibility of expediting studies of intra- and inter-species prion transmission . We made use of transgenic mice as a source of deer prion protein for the production of CWD prions by protein misfolding cyclic amplification ( PMCA ) . Characterization of infectivity in the same transgenic mice demonstrated that PMCA results in the efficient amplification of CWD prions with unaltered strain characteristics . Also , whereas adaptation of mouse prions to form novel cervid prions required several hundred days and subsequent stabilization in transgenic mice , we show that PMCA rapidly abrogated this inter-species transmission barrier . Our results indicate that PMCA can be used to replace the process of prion strain adaptation and selection occurring in vivo . | [
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] | [
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] | 2008 | Accelerated High Fidelity Prion Amplification Within and Across Prion Species Barriers |
Trypanosoma cruzi , the etiological agent of Chagas’ disease , affects 8 million people predominantly living in socioeconomic underdeveloped areas . T . cruzi trypomastigotes ( Ty ) , the classical infective stage , interact with the extracellular matrix ( ECM ) , an obligatory step before invasion of almost all mammalian cells in different tissues . Here we have characterized the proteome and phosphoproteome of T . cruzi trypomastigotes upon interaction with ECM ( MTy ) and the data are available via ProteomeXchange with identifier PXD010970 . Proteins involved with metabolic processes ( such as the glycolytic pathway ) , kinases , flagellum and microtubule related proteins , transport-associated proteins and RNA/DNA binding elements are highly represented in the pool of proteins modified by phosphorylation . Further , important metabolic switches triggered by this interaction with ECM were indicated by decreases in the phosphorylation of hexokinase , phosphofructokinase , fructose-2 , 6-bisphosphatase , phosphoglucomutase , phosphoglycerate kinase in MTy . Concomitantly , a decrease in the pyruvate and lactate and an increase of glucose and succinate contents were detected by GC-MS . These observations led us to focus on the changes in the glycolytic pathway upon binding of the parasite to the ECM . Inhibition of hexokinase , pyruvate kinase and lactate dehydrogenase activities in MTy were observed and this correlated with the phosphorylation levels of the respective enzymes . Putative kinases involved in protein phosphorylation altered upon parasite incubation with ECM were suggested by in silico analysis . Taken together , our results show that in addition to cytoskeletal changes and protease activation , a reprogramming of the trypomastigote metabolism is triggered by the interaction of the parasite with the ECM prior to cell invasion and differentiation into amastigotes , the multiplicative intracellular stage of T . cruzi in the vertebrate host .
The protozoan T . cruzi , the etiological agent of Chagas’ disease , affects a wide range of mammalian hosts , including humans . It is estimated that 8 million people are infected and 25 million are at risk , most of them living in areas of poor socioeconomic development [1] . The cell cycle of T . cruzi involves an invertebrate vector ( triatomine bugs ) and a mammalian host , and well-defined developmental stages ( epimastigotes , metacyclic trypomastigotes , amastigotes and blood trypomastigotes ) . T . cruzi trypomastigotes , the classical infective stage , invade almost all mammalian cell and tissue types , and invasion is an obligatory step in their life-cycle in mammals . An essential step immediately prior to the mammalian cell invasion is the interaction of the parasite with the surrounding extracellular matrix . The extracellular matrix ( ECM ) is a highly dynamic non-cellular three-dimensional macromolecular network , which regulates different cellular functions , such as growth , differentiation or survival [cf . 2] . Its composition includes structural components ( “matrisome” , [3] ) and elements that can interact with or remodel the ECM [4] . Collagens , proteoglycans and glycoproteins ( laminins , fibronectins , thrombospondins , tenascins , among others ) constitute the main core of the ECM proteins . ECM-affiliated proteins ( such as mucins , syndecans , plexins ) and ECM-regulators ( such as lysyl oxidases , sulfatases , extracellular kinases , proteases and secreted factors , such as TGFβ , cytokines ) were classified as matrisome-associated proteins [4] . Cell-ECM interactions occur mainly by integrins present at the cell surface , which connect the extracellular signals and the intracellular response by the activation of specific signaling pathways [5 , 6] and dysregulation of the ECM is associated with the development of several pathological conditions [4 , 7] . Adhesion of Trypanosoma cruzi and other parasites to distinct elements of ECM has been described to involve different surface proteins from the infective stage of the parasite , of which the gp85/transialidase family plays an essential role ( rev . [8] ) . T . cruzi binds to collagen [9] , fibronectin [10–14] , laminin [11 , 15 , 16 , 17] , thrombospondin [11] , [18] , heparan sulfate [11 , 12 , 14 , 19] , galectin-3 [20 , 21] , as well as TGF-β [22] . Also , remodeling of ECM was observed during infection , with modifications in collagen and fibronectin content , as well as reorganization of laminin [13] , at least partially due to T . cruzi proteolytic enzymes [23–25] . Despite the relevance of ECM for T . cruzi infection , the signaling pathways triggered by the trypomastigote-ECM interaction are less well known . Recently , we demonstrated a decrease in S-nitrosylation and nitration in the majority of the trypomastigote proteins when parasites are incubated with ECM [26] . In addition , dephosphorylation of proteins , such as α-tubulin , paraflagellar rod proteins ( PFR or PAR ) , as well as ERK 1/2 was observed in trypomastigotes incubated with either laminin or fibronectin [27] , although these do not reflect the entirety of possible interactions between parasite and ECM . To have a better understanding of the process , quantitative proteomic and phosphoproteomic approaches were employed to analyze changes in T . cruzi trypomastigote proteins when the parasites are incubated with ECM . Herein we show important changes in the T . cruzi trypomastigotes proteome , as well as in protein phosphorylation levels upon interaction with ECM . Proteins involved with metabolic processes , phosphatases , kinases and RNA/DNA binding elements were highly represented among the proteins modified by phosphorylation . In particular , a decrease of the glycolytic pathway was suggested by metabolite quantification and measurement of hexokinase , pyruvate kinase and lactate dehydrogenase activities , suggesting an extensive metabolic adaptation of trypomastigotes prior to host cell invasion . Taken together , our data show that not only structural adaptations but also important metabolic changes occur upon parasite interaction with ECM . Understanding these changes may further elucidate the adaptive mechanisms involved in parasite-host interaction .
Trypomastigotes ( ECM-treated for 120 min or control ) were fixed in 2% paraformaldehyde for 15 minutes at room temperature , pelleted by centrifugation ( 4 , 000 x g for 5 minutes ) , washed twice in PBS , resuspended in PBS , added to a coverslip and dried at room temperature . After permeabilization of the parasites with PBS containing 1% BSA and 0 . 1% Triton X-100 for one hour at 37°C , anti-phosphoserine , anti-phosphothreonine , anti-phosphotyrosine ( Invitrogen–dilution 1:200 for each antibody ) , anti-PAR monoclonal antibody ( 1:200 ) or anti–TcHexokinase ( kindly provided by Dr . Ana Cáceres , Universidad de Los Andes , Venezuela ) were added and incubated for 1 h at room temperature . After three washes with PBS containing 0 . 1% Triton X-100 , the correspondent secondary antibodies were added ( anti-rabbit or anti-mouse-Alexa 555 conjugated ( 1: 5000 ) ; followed by one hour incubation at 37°C . After three washes in PBS-0 . 1%-Triton X-100 , the coverslips were faced under a solution containing 50% glycerol , 50% milliQ H2O 2 mM sodium azide , and 20 μg/mL of 4' , 6-diamidino-2-phenylindole , dilactate ( DAPI-Invitrogen ) . The images were taken on an ExiBlue™ camera ( Qimaging® ) coupled to a Nikon Eclipse E 600 optical microscope and deconvoluted using the software Huygens Essential ( Scientific Volume Imaging ) . Frozen pellets of trypomastigotes ( 1 x 109 MTy or Ty ) were resuspended in 1 mL of Lysis Buffer ( 30 mM Tris-HCl , pH 7 . 6 , 1 mM EDTA , 0 . 1% Triton e 0 . 25 M sucrose , containing phosphatase and protease inhibitors , as described above , disrupted by ultrasonic for 4 x 10 s ( frequency of 40% , Thomas GEX 600 apparatus ) . After centrifugation ( 10 000 x g , 15 min ) , the supernatant was separated and employed to measure the enzymatic activities ( Hexokinase , Pyruvate kinase and Lactate dehydrogenase ) . In all cases , the amount of NADH / NADPH was measured spectrophotometrically at 340 nm and its concentration calculated ( extinction coefficient = 6 . 220 M-1 cm-1 ) [32] . Three independent biological samples were employed . Due to the possible presence of ECM proteins in the MTy samples , the enzymatic activities were expressed by 1x108 parasites . The number of parasites in each experimental point was also estimated by a calibration curve using the amount of paraflagellar rod protein in the Western blotting ( anti-PAR monoclonal antibody 1:2000 ) .
Previous studies demonstrated that there is a significant decrease in protein phosphorylation upon parasite interaction with fibronectin or laminin , both components of the ECM . Thus we decided to investigate possible changes in protein phosphorylation that could occur upon trypomastigote interaction with the entire ECM . The experiments outlined in the scheme ( Fig 1A ) were performed in order to analyze both the proteome and the phosphoproteome of the parasites incubated with ECM . The proteome and phosphoproteome analyses identified 3 , 093 proteins and 7 , 880 phosphopeptides , respectively , with FDR values less than 1% and p scores less than e-7 for peptide identification ( Fig 1B ) . Thirty six proteins ( 57% ) and 212 phosphopeptides ( 67% ) correspond to proteins with unknown function ( hypothetical proteins ) , as described by others in different trypanosomes [27 , 35–42] , which is in accordance with the number of proteins ( 49 . 2% ) with unknown functions predicted by the genome sequence of T . cruzi [43] ( Fig 1B ) . Sixty-three non-unique proteins from the proteome data exhibited significant variations , with only 5 showing reduction and 58 showing an increase in their protein level ( Fig 1B , S1 Fig , S1 Table ) . Seventeen proteins showed changes where MTy/Ty ≥1 . 5 , of which nine were hypothetical , including the one with the greatest change ( MTy/Ty ≥12 . 9 ) . Among the proteins with increased expression , it is worth emphasizing members of the gp85/trans-sialidase ( TS ) family , involved in host cell infection by T . cruzi , ( MTy/Ty = 2 . 2 for one member of group II and Mty/Ty = 4 . 5 for one member of group IV ) ; small GTP-binding protein rab6 ( Mty/Ty = 3 . 8 ) ; ribosomal RNA processing protein 6 ( MTy/Ty = 1 . 98; the splicing factor 3a; and the 2Fe-2S iron-sulfur cluster binding domain containing protein ( Mty/Ty = 1 . 77 ) . In the phosphoproteomic analysis , only phosphopeptides having MTy/Ty ratio below 0 . 8 or above 1 . 2 , with p values less than 0 . 05 , have been considered to evaluate the effect of ECM on trypomastigote protein phosphorylation . Among the 303 phosphopeptides selected by these criteria , 69 showed an increase and 234 a decrease in their phosphorylation levels . Of these , 91 ( 33% ) are proteins with known function , of which 19 showed an increase and 72 a decrease in their phosphorylation levels ( S2 Table; Fig 2A and 2B ) . Taken together the data indicate that adhesion of trypomastigotes to ECM overall leads to protein dephosphorylation , in agreement with previous observations with T . cruzi trypomastigotes incubated with ECM components , fibronectin and laminin [27] . Phosphorylation site analysis identified 371 differentially phosphorylated amino acid residues: 275 ( 74 . 1% ) serine , 83 ( 22 . 4% ) threonine and only 13 ( 3 . 5% ) tyrosine residues , in accordance with the abundance of serine/threonine kinases found in T . cruzi [44] , the fact that T . cruzi does not express receptor coupled tyrosine kinases but dual specificity kinases [44] , as well as with the phosphoproteome data described for different stages of the parasite [42 , 45 , rev . 46] . In silico analysis , as described below , suggested the involvement of different kinases ( CAMK , TKL , CMGC , CK1 , AGC and others ) . To understand better the role of the proteins controlled by phosphorylation during the parasite response to the ECM , GO-terms enrichment analysis was performed . Additional information was obtained on the molecular function and/or sub-cellular localization of 29 proteins and 126 phosphopeptides previously labeled as hypothetical ( unknown function ) based on version 40 of the TriTrypDB . The data are shown in S1 Table and S2 Table . Most of proteins identified are related to structural function , pathogenicity , metabolism and protein phosphorylation . Both cytoplasm and axoneme seem to be the main localization of the identified phosphopeptides . Among the proteins identified , gp85/trans-sialidase ( TS ) family members , involved in infection of host-cells by T . cruzi , were enhanced in trypomastigotes incubated with ECM ( MTy/Ty = 2 . 2 for one member of group II and MTy/Ty = 4 . 5 for one member of group IV ) . Small GTP-binding protein rab6 ( MTy/Ty = 3 . 8 ) , ribosomal RNA processing protein 6 ( MTy/Ty = 1 . 98 ) , splicing factor 3a and 2Fe-2S iron-sulfur cluster binding domain containing protein ( MTy/Ty = 1 . 77 ) were also identified in the group of the proteins with an increased expression . The following proteins are highly represented in the pool of proteins modified by phosphorylation ( Fig 2B and S2 Table ) : proteins involved in metabolic processes ( 19 phosphopeptides ) ; in phosphorylation/dephosphorylation ( such as kinases and phosphatases , 20 phosphopeptides ) ; structural proteins ( such as flagellum and microtubule related proteins , 19 phosphopeptides ) ; transport-associated proteins; and RNA/DNA binding elements . This suggests an extensive metabolic adaptation occurring in trypomastigotes prior to host-cell infection . Of note , enzymes that participate in glucose metabolism , in addition to adenylosuccinate lyase ( ADSL ) and alanine aminotransferase ( ALT ) ( Table 1; Fig 2C ) , lipid metabolism ( 3-oxo-5-alpha-steroid 4-dehydrogenase; putative ( pseudogene ) and ethanolamine phosphotransferase ) ( Fig 2B , S2 Table ) are modified by phosphorylation . Since a significant number of enzymes from the glucose metabolism were modified by phosphorylation , their role was further investigated . Seven enzymes involved in carbohydrate metabolism were identified in the phosphoproteomic analysis: hexokinase ( HK ) ; 6-phosphofructo-2-kinase/fructose-2;6 bisphosphatase ( PFK2 ) ; 6-phospho-1-fructokinase ( PFK1 ) ( pseudogene ) ; phosphoglucomutase ( PGM ) ; pyruvate phosphate dikinase ( PPDK ) ; phosphoglycerate kinase ( PGK ) and NADH-dependent fumarate reductase ( FRD ) , in addition to adenylosuccinate lyase and alanine aminotransferase ( Fig 2C , Fig 3 , Table 1 , S2 Table ) . Except for NADH-dependent fumarate reductase ( MTy/Ty = 1 . 5 ) and adenylosuccinate lyase ( MTy/Ty = 2 . 2 ) , all the others showed decrease in their phosphorylation levels when trypomastigotes were incubated with ECM ( Table 1 and Fig 3 ) . Most of these enzymes are localized in the glycosomes , a peroxisome-like organelle essential for trypanosomatids survival and characterized for containing most of the glycolytic/gluconeogenic pathways in kinetoplastids , in addition to enzymes of other metabolic pathways , such as the pentose phosphate pathway , beta-oxidation of fatty acids , and biosynthesis of pyrimidines ( rev . [47–49] ) . Likewise , phosphorylation of enzymes involved in carbohydrate metabolism was described in the proteome and phosphoproteome of the glycosomes in T . brucei and Leishmania donovani [40 , 41 , 50] . Since the data suggested possible changes in the metabolism of T . cruzi , the metabolite content was analyzed by GC/MS to understand better the response of trypomastigotes upon adhesion to ECM . GC-MS analysis allowed the identification of 21 metabolites with a significant variation ( p < 0 . 05 ) in the MTy/Ty ratio ( S5 Table ) , from which significant changes were found for carbohydrate , lipid and amino acid metabolites . Some of these metabolites are substrates or products of the enzymes modified by phosphorylation that are found , although not exclusively , inside the glycosomes ( Fig 3A and 3B , Table 1 ) . The following metabolites from the glycolytic pathway were modified in parasites upon incubation with ECM: increase in glucose/galactose ( molecules indistinguishable in the GC-MS methodology ) with ECM ( MTy/Ty = 1 . 2 ) ; decrease in pyruvic acid ( MTy/Ty = 0 . 2 ) and lactic acid ( MTy/Ty = 0 . 34 ) . Interestingly , metabolites derived from the glycolytic pathway branch and common to TCA cycle: succinic acid ( MTy/Ty = 1 . 37 ) , malic acid ( MTy/Ty = 1 . 21 ) and fumaric acid ( MTy/Ty = 1 . 15 ) , were increased in parasites incubated with ECM . Succinate is considered the main source of reducing equivalents to the respiratory chain through the action of a NADH-dependent fumarate reductase and it is also ( in addition to alanine ) one of the main products excreted by trypanosomatids [48 , 51] , although lactate excretion by T . cruzi [52] may increase , depending on metabolic adaptations . These changes in metabolite level do not appear to correlate with changes in metabolic enzyme levels ( S1 Table ) . Rather , they may reflect modulation of metabolic enzyme activity . Although less representative in the metabolite quantification , an increase in free amino acids in MTy ( tyrosine , glycine or isoleucine , MTy/Ty ratio ≈1 . 4 ) and fatty acids ( mainly palmitic acid , MTy/Ty ratio ≈1 . 4 ) may also indicate wider changes in the metabolism of MTy ( S5 Table ) . To analyze the potential role of metabolic enzyme phosphorylation in modulating the glycolytic pathway in MTy , enzymatic activities of hexokinase/glucokinase ( HK/GK ) and pyruvate kinase ( PK ) were determined , as well as for lactate dehydrogenase ( LDH ) . Although PK and LDH were not detected in our phosphoproteomic analysis ( S2 Table ) , these enzymes were included because significant depletion of pyruvate and lactate were observed in MTy ( S5 Table ) . Further , as pointed out before , the changes in the metabolite levels seem to be independent of the relevant metabolic enzyme expression levels accordingly to the proteomic data ( S1 Table ) . The enzymes HK and GK catalyze the formation of glucose-6-phosphate , the first reaction of the glycolytic pathway and both lack the regulatory allosteric inhibition by glucose-6-phosphate , common to other organisms . Both are localized inside the glycosomes , of which HK presents the highest activity [53 , 54] . A distinct HK , which phosphorylates glucose and fructose , as well as GK activity were also described in the cytosol [53] . The affinity of HK for glucose is higher than that of GK ( Km values of 0 . 06 mM and 0 . 7 mM , respectively ) in addition to the higher amounts of HK over GK in the parasite [55] . However , in the present work we could not separate the activities of both enzymes and , thus , they are collectively represented by HK/GK activities . The three phosphorylated residues of T . cruzi hexokinase ( S161 , T153 , T159 ) ( Table 2 , S1 Table , S2 Fig ) were located in the same peptide identified by LC-MS/MS and presented similar changes in the phosphorylation level ( MTy/Ty ratio = 0 . 76; 0 . 76 and 0 . 57 , respectively ) . The identified phosphorylated peptide is localized in the catalytic domain or in the substrate-binding site of the enzyme , according to the alignment of T . cruzi-HK ( TcCLB . 508951 . 20 ) , T . brucei ( Tb927 . 10 . 2010 ) and the three isoforms of human-HK ( P52790; P19367; P52789 ) , performed by ClustalW platform ( S2B Fig ) . HK/GK activities from MTy and Ty homogenates were measured spectrophotometrically in the presence of a coupling system containing glucose-6-phosphate dehydrogenase . HK/GK activity is clearly reduced ( by approximately 46% ) in MTy relative to Ty ( Fig 4A , a ) . Previous treatment of Ty and MTy homogenates with alkaline phosphatase significantly reduced the activity ( approximately 45% for Ty and 59% for MTy extracts , Fig 4A , c , d ) . Also , the activity was drastically reduced ( approximately 70% ) when the homogenate was previously treated at 56°C for 1 h . Since HK is inhibited by small phosphate molecules , such as PPi present in distinct organelles including glycosomes [53 , 56 , 57] , the experiment was repeated with the parasite extract previously immunoprecipitated with anti-HK antibodies . Similar results have been obtained , confirming the relevance of phosphorylation for HK/GK activity ( S4 Fig ) . Inhibition of the enzymatic activity by dephosphorylation is consistent with the accumulation of glucose detected in MTy . However , one cannot rule out other possibilities , such as changes in glucose transport in MTy , alterations of HK oligomerization , which is usually tetrameric [54] or somehow by contributing to the hysteretic and cooperative behavior of HK at low enzyme concentration described in T . cruzi epimastigotes [58] . The phosphorylation of HK could be by auto-phosphorylation [59] or by protein kinase ( s ) . Of note , the same pattern of HK in MTy and Ty inside the glycosomes was shown by immunofluorescence using specific anti-HK antibodies ( S2A Fig ) . These data also indicate that no significant changes in the number of glycosomes occur in MTy , in accordance with the literature , where approximately the same number was found in the different forms of T . cruzi , in contrast to the variability described during the life cycle of other species [60] . Pyruvate kinase ( PK ) was not detected in the phosphoproteome/proteome described herein , as pointed out above , but the low amount of pyruvate found in MTy led us to measure a corresponding enzymatic activity . In the cytosol , PK catalyzes the formation of pyruvate and ATP from phosphoenolpyruvate and ADP and , in the case of T . cruzi epimastigotes , PK is inhibited by millimolar concentrations of ATP and Pi and activated by micromolar concentrations of fructose 2 , 6-bisphosphate ( rev . [48 , 61] ) by the tetrameric stabilization of PK in response to the effector binding [62] . As shown ( Fig 4B , a ) , PK activity is strongly inhibited in trypomastigotes incubated with ECM ( approximately 65% ) . Treatment of the enzyme with alkaline phosphatase increased its activity , mainly in MTy homogenates ( 25% Ty and 125% MTy ) , as shown in Fig 4B , c , d . The decrease in pyruvate content observed could be attributed to the inhibition of pyruvate kinase in the cytosol and/or pyruvate phosphate dikinase in the glycosome , which would lead to an increase of dicarboxylic acids from the glycolytic branch ( succinate , fumarate and malate ) in the glycosome , ( cf . Fig 3 and S5 Table ) . However , higher consumption of pyruvate , for example by its conversion to acetyl-CoA inside the mitochondria or to lactate in the cytosol , cannot be ruled out . Lactate dehydrogenase-like ( LDH ) activity was measured in the parasite homogenate due to the decrease observed in lactate content in MTy ( S5 Table ) . In many organisms , a tetrameric form of the enzyme catalyzes the oxidation of lactate to pyruvate in the presence of NAD+ as hydrogen acceptor . LDH activity in T . cruzi epimastigotes was attributed to the isoenzyme I of ∂-hydroxyacid dehydrogenase localized inside the glycosomes and in the cytoplasm [63] and to an unknown protein in T . brucei [51] , since a typical lactate dehydrogenase is absent from the genomes of trypanosomatids . The measurement of LDH activity showed approximately 33% reduction in MTy in comparison to trypomastigotes ( Fig 4C , a ) , in agreement with the decrease of lactate detected in MTy . LDH was also inhibited by alkaline phosphatase treatment ( approximately 65% for Ty and 41% for MTy extracts ) , reinforcing the role of phosphorylation in modulating LDH activity ( Fig 4C , c , d ) . Alanine , rather than lactate , is usually the main product of the reduction of pyruvate in T . cruzi , a reaction catalyzed by alanine aminotransferase inside the glycosomes ( rev . [48 , 49] ) . Lactate excretion by the parasite [52] may increase depending on metabolic adaptations , as described for the procyclic forms of T . brucei [51] and may explain the aforementioned results in trypomastigotes . Although no significant differences in alanine content between MTy and Ty were detected by GC/MS analysis , alanine aminotransferase is less phosphorylated in trypomastigotes incubated with ECM and may be responsible for the switch to the LDH reaction for NADH-reoxidation . Since T . cruzi possesses the enzyme repertoire for gluconeogenesis , this pathway may also be activated in MTy , resulting in higher consumption of pyruvate , lactate and glycerol , although no reserve polysaccharide was detected and gluconeogenesis has not been fully established in T . cruzi . Our data suggest that incubation of trypomastigotes with ECM triggers metabolic adaptations in the parasites , and that phosphorylation , or more specifically protein dephosphorylation , may be involved in these processes . In spite of the relevance of the dephosphorylation and the high representative number of phosphatases in the genome and proteome of T . cruzi [64 , 42] , only two protein phosphatases with diminished phosphorylation levels in MTy were found: an endonuclease/exonuclease/phosphatase responsible for dephosphorylation of DNA sequences ( TcCLB . 504073 . 10 ) and PP1 , a serine/threonine phosphatase ( TcCLB . 507757 . 50 ) . Serine/threonine phosphatases are abundant in T . cruzi and constitute more than 50% of the 86 protein phosphatases in the genome . Protein phosphatase PP1 is dephosphorylated in MTy ( MTy/Ty = 0 . 65 ) , which would be expected to increase its enzymatic activity and consequently contribute to the dephosphorylation of proteins [65] ( Table 2; S2 Table ) . Other protein phosphatases , such PP2A , PP2C or dual specific phosphatase were described in the proteome / phosphoproteome of T . cruzi by different groups or in the glycosomes of T . brucei [41] or L . mexicana [50] , which might also contribute to the decrease of the phosphorylation level of the proteins . To understand further the signaling pathways activated in the parasite upon contact with host ECM , the kinases capable of phosphorylate the peptides detected by LC-MS/MS were predicted by the GPS 2 . 1 software . Only the higher scores for each phospho-residue ( S , T and Y ) in the peptide were selected , totaling 378 putative kinases for the 303 phosphopeptides identified by LC-MS/MS ( S3 and S4 Tables ) . Most of the identified sites correspond to modifications by serine/threonine kinases and the phosphorylation of tyrosine was attributed to the dual-specificity kinases , in agreement with the absence of conventional tyrosine kinases in the genome of trypanosomatids [44] . Of the 303 phosphopeptides analyzed , 78 . 5% and 21 . 1% , respectively , presented one or two phosphorylated sites and only one phosphopeptide showed three phosphorylated-sites ( Fig 5A and 5B ) . The phosphorylated sites identified herein are predicted to be modified mainly by elements of the CMGC kinases superfamily ( 68 ) , STE kinases ( 62 ) , TKL kinase ( 61 ) , AGC kinase ( 31 ) , CK1 family ( 31 ) ( Fig 5D , Tables 1 and 2 , S3 and S4 Tables ) . Kinases that do not belong to any characterized family are grouped as “Others” . Only the catalytic subunit of protein kinase A , a member of the AGC kinase superfamily showed in MTy a slightly increase in its phosphorylation level ( MTy/Ty = 1 . 27 ) . The phosphorylation of T197 of mouse PKA , located in the activation loop , which corresponds to T35 described herein for T . cruzi , increases PKA activity , indicating that upon interaction with ECM , PKA is activated [66 , 67] . Proteins responsible for a plethora of biological phenomena in T . cruzi have been described as PKA substrates , such as kinases ( type III PI3 kinase-Vps34 , PI3 kinase , mitogen-activated extracellular signal-regulated kinase ) , cAMP-specific phosphodiesterase ( PDEC2 ) , putative ATPase , DNA excision repair protein , aquaporin , hexokinase and members of gp85/TS [68 , 69] . The role of PKA during metacyclogenesis ( differentiation of epimastigotes to metacyclic trypomastigotes [70] ) or during the amastigogenesis ( differentiation of trypomastigotes into amastigotes [42] ) is well established , with the stimulation of adenylyl cyclase and increment in cAMP concentration during the process . Albeit the relevance of PKA in the physiology of the parasite , their specific role in trypomastigotes incubated with ECM was not determined . In contrast to PKA , reduction in phosphorylation was detected in the majority of the kinases , for example in glycogen synthase kinase 3 ( GSK3 ) and ERK1/2 , presumably leading to a decrease of their activities in MTy: Y187 from GSK3 ( MTy/Ty = 0 . 78 ) corresponds to human Y216 also located in the activation loop and whose phosphorylation is necessary for activity [71]; T190 and Y192 from mitogen-activated protein kinase ( MTy/Ty = 0 . 75 ) , corresponds to human T185/Y187 , whose phosphorylation is necessary for ERK1/2 activation [72] . Dephosphorylation of ERK1/ 2 was also observed in the incubation of trypomastigotes with laminin or fibronectin [27] . Decrease in phosphorylation of other kinases was also found: protein kinase ck2 regulatory subunit ( MTy/Ty = 0 . 76 ) , inositol-related signaling kinases; inositol polyphosphate kinase-like protein ( MTy/Ty = 0 . 67 ) and phosphatidylinositol-4-phosphate 5-kinase type II beta ( MTy/Ty = 0 . 37 ) ( Table 2 ) . Whether this is a reflection of their activation status remains to be determined . The phosphopeptides assigned to particular kinase families by the GPS 2 . 1 software ( Fig 3D ) were used for the construction of sequence logos , which correspond to sequence alignment with the central point as the likely phospho-amino acid residue ( Fig 3C ) . For the AGC kinase families , "other" and CK1 , no consensus was observed in the amino acid sequences surrounding the S/T residue . For the "Atypical" , TK and STE families some amino acids were identified with a high level of conservation . The analysis of the sequence logos ( Fig 3C ) indicates some conservation relative to the sites already characterized for humans , such as the group of peptides phosphorylated by CMGC , where a proline next to the phosphorylation site was also identified; lysine and arginine near the phosphorylation site for AGC; residues of aspartate and glutamate for CK1 family substrates and the conservation of phenylalanine and proline residues near the phosphorylated site for the atypical kinases . Incubation of T . cruzi trypomastigotes with the extracellular matrix results in important and more extensive changes than the ones previously described by the incubation of trypomastigotes with fibronectin or laminin [27] . Reduction in the phosphorylation level of proteins seems to be a general event in trypomastigotes incubated with ECM ( Tables 1 and 2 , S2 Table ) . Kinases , except for PKA , PP1 phosphatase and enzymes from the glycolytic pathway and probably the glycolytic branch exemplify these decreases ( S2 and S3 Tables ) . Strikingly , correlating with the observed decrease in phosphorylation level of the enzymes , a significant inhibition of hexokinase , pyruvate kinase and lactate dehydrogenase-like were detected . ( S2 and S5 Tables , Fig 4 ) . These results , in association to the slightly increase of glucose and drastic reduction of pyruvate and lactate strongly suggest that ECM triggers important reduction in the glycolytic pathway in T . cruzi trypomastigotes . Although hexokinase is among the many substrates described for PKA [69] , a possible correlation between these enzymes has not been explored herein . Interestingly , trans-sialidases , surface glycoproteins belonging to the T . cruzi gp85/trans-sialidase family , are also one of the substrates for PKA [69] and PKA activity and trans-sialidase expression has been associated with differentiation and invasion of host cells by T . cruzi [73] . A similar coincidence of increase in PKA activity and expression of two members of the gp85/trans-sialidase family were observed upon incubation of trypomastigotes with ECM ( MTy/Ty = 2 . 2 and 4 . 5 , S1 Table ) , perhaps preparing the parasites for an efficient invasion of the host cell . The possibility that members of the large gp85/ trans-sialidase family interact with different components of ECM to trigger all the modifications described here remains to be determined . Taken together , the data presented herein suggest reprogramming of the metabolism of trypomastigotes triggered by their interaction with the extracellular matrix , an obligatory step before cell invasion and differentiation into amastigotes , the multiplicative stage of T . cruzi in the vertebrate host . The reduction in glycolytic enzyme activity in trypomastigotes by phosphorylation/dephosphorylation events seems to be part of this reprogramming , with the involvement of yet to be identified protein kinases and phosphatases . | Adhesion of Trypanosoma cruzi to distinct elements of ECM involving different surface proteins from the infective stage of the parasite has been described . Despite the relevance of ECM for T . cruzi infection , the signaling pathways triggered in trypomastigotes upon interactions with ECM are less well understood . In previous work we demonstrated the dephosphorylation of proteins , such as α-tubulin , paraflagellar rod proteins and ERK 1/2 in trypomastigotes incubated with either laminin or fibronectin . Further , we described changes in the S-nitrosylation and nitration pattern of proteins from trypomastigote incubated with ECM . To expand our knowledge on ECM triggered parasite signaling we applied quantitative proteomic and phosphoproteomic studies to trypomastigotes incubated with ECM ( MTy ) compared to controls ( Ty ) . Our results indicate relevant changes in total protein and phosphoprotein profiles in MTy . The kinases implicated in the modifications were suggested by bioinformatic analyses , as well as the number of modifications and the frequency of amino acids per peptide that have been modified . Proteins involved in metabolic processes , including enzymes from the glycolytic pathway , phosphatases and kinases were the most representative groups among the proteins modified by phosphorylation . Quantification of metabolites in MTy and Ty also indicated that glucose metabolism is impaired in trypomastigotes incubated with ECM . The significant inhibition of hexokinase , pyruvate kinase and lactate dehydrogenase activities in MTy associated with phosphorylation levels , strongly suggests that trypomastigotes reprogram their metabolism in response to interaction with the extracellular matrix , an obligatory step prior to host cell invasion . | [
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] | 2019 | Reprogramming of Trypanosoma cruzi metabolism triggered by parasite interaction with the host cell extracellular matrix |
Clinical and preclinical studies indicate that early postnatal exposure to anesthetics can lead to lasting deficits in learning and other cognitive processes . The mechanism underlying this phenomenon has not been clarified and there is no treatment currently available . Recent evidence suggests that anesthetics might cause persistent deficits in cognitive function by disrupting key events in brain development . The hippocampus , a brain region that is critical for learning and memory , contains a large number of neurons that develop in the early postnatal period , which are thus vulnerable to perturbation by anesthetic exposure . Using an in vivo mouse model we demonstrate abnormal development of dendrite arbors and dendritic spines in newly generated dentate gyrus granule cell neurons of the hippocampus after a clinically relevant isoflurane anesthesia exposure conducted at an early postnatal age . Furthermore , we find that isoflurane causes a sustained increase in activity in the mechanistic target of rapamycin pathway , and that inhibition of this pathway with rapamycin not only reverses the observed changes in neuronal development , but also substantially improves performance on behavioral tasks of spatial learning and memory that are impaired by isoflurane exposure . We conclude that isoflurane disrupts the development of hippocampal neurons generated in the early postnatal period by activating a well-defined neurodevelopmental disease pathway and that this phenotype can be reversed by pharmacologic inhibition .
Several large retrospective analyses link exposure to anesthetics and surgery within the first 3 years of life with subsequent effects on cognitive function , as measured by worsened performance on school assessments , an increase in billing codes relevant to learning disorders , and deficits in neuropsychological testing [1–3] . It is difficult to separate the effects of surgery , anesthesia , and comorbidity in clinical studies . However , multiple independent investigations conducted in rodent models using different anesthetics and varying exposure paradigms in the absence of surgery indicate that early developmental exposure to general anesthetic agents results in lasting impairment on behavioral measures of neurocognitive function , predominantly in the domain of learning and memory [4–12] . While 2 recent clinical studies give some reassurance that short , single exposures in healthy children may not have dramatic consequences [13 , 14] , clear evidence of lasting cognitive deficits was detected recently in a carefully conducted study of a somewhat longer clinically relevant anesthetic exposure in nonhuman primates [15] . Thus , there are serious concerns in the anesthesiology , surgery , and pediatrics literature that anesthetic exposure may result in worsened cognitive outcomes for some unknown fraction of the hundreds of thousands of children under age 4 who undergo surgery each year [16–18] . In response to these findings , the US Food and Drug Administration recently issued a drug safety communication warning that anesthetic exposure may pose risks to brain development and calling for further research on this topic . The molecular and cellular mechanisms underlying this phenomenon have yet to be clearly elucidated , and no prophylactic or treatment strategies exist . Much of the literature on the effects of anesthetic exposure on brain function focuses on the potential for anesthetics to activate apoptotic cell death pathways in neurons [6 , 19] , but more recent work has led to the novel hypothesis that anesthetics cause lasting effects on cognitive function via sublethal effects on critical processes in neuronal development [20] . In humans , the neural circuitry underlying higher brain functions , such as learning , is primarily established between the second trimester and early childhood [21] , a period that includes the window of putative vulnerability to anesthetics identified in epidemiologic studies [18] . During this time , critical ongoing developmental events are occurring in many neurons of the hippocampus , including growth of dendritic arbors and generation of dendritic spines , which are the postsynaptic elements of excitatory synapses [22] . There are substantial differences in developmental timelines in the different species in which the effects of early postnatal anesthesia exposure on cognitive function have been studied , but one notable common feature is the generation and development of a large percentage of the dentate gyrus granule cell ( DGC ) neurons in the hippocampus [23] , a structure that is critical to cognitive functions , including learning and memory . Thus , in this study we investigated the effects of anesthesia exposure on dendritic arbor and spine development in early postnatally generated DGCs , which may be an important target population and may also serve as a model for postnatal neuron development in other brain regions . We employed a retrovirus-mediated labeling method in intact mice to examine the development of dendrite arbors and dendritic spines in DGCs in vivo after exposure to a clinically relevant dose of isoflurane . This approach allows morphological analyses of a uniform and well-studied population of neurons , the DGCs , at a single cell level in vivo [24] . We find that early postnatal exposure to isoflurane results in a substantial and lasting disruption of dendritic arborization and spine development . Isoflurane was found to over-activate the mechanistic target of rapamycin ( mTOR ) pathway , a signaling system critical for normal development , which has been implicated in neurodevelopmental disorders in which cognitive function is affected , including autism and fragile X mental retardation [25 , 26] . Strikingly , the adverse effects of isoflurane on both dendrite morphology and behavioral tests of learning can be reversed with rapamycin , an mTOR inhibitor . Our findings reveal a novel mechanism by which anesthetics disrupt brain development that has been implicated in other neurodevelopmental disorders and that is potentially reversible via drug therapy .
In order to investigate the effects of anesthetics on dentate gyrus neuron development in vivo , we employed stereotaxic injection to deliver a retrovirus expressing green florescent protein ( GFP ) to label newly generated dentate gyrus neurons [24] . Injections were conducted at postnatal day ( P ) 15; on P18 , the animals were exposed to isoflurane , a canonical halogenated ether vapor anesthetic . The dose of isoflurane exposure ( 1 . 5% ) falls well within clinically relevant parameters , as the minimum alveolar concentrations of isoflurane ranges between 1 . 6% and 1 . 8% in children between ages 0 and 4 [27] . A 4 hour-exposure duration was selected based on clinical data , which showed that significant learning deficits in children are associated with more than 2 hours of anesthetic exposure [3] . All exposed mice survived and recovered readily , and results of physiologic monitoring of sentinel animals are shown in S1 Table . Tissue was collected for morphological studies at P30 . A flow diagram of these experiments is shown in Fig 1A . We sought to determine whether exposure to anesthetics during development alters neuronal structure in newborn DGCs lasting fashion . Previous investigations have been potentially confounded by an inability to determine the developmental stage at which any given neuron under analysis was affected by anesthetics , given the nonhomogenous timeline of neuronal development that occurs even within discrete brain regions . In our model , the labeled DGCs , which have fully definable structure due to GFP expression that allows for easy analysis of morphology ( Fig 1B ) , represent a cohort of cells with a uniform birthdate , all of which were exposed to anesthetics at the same point in their developmental timeline . Examination of dendritic structure revealed a striking finding: compared to neurons in unexposed littermate controls , labeled neurons in isoflurane-exposed animals exhibit an 83% increase in total dendritic arbor length at P30 ( p < 0 . 005; Fig 1C–1E ) . To further elucidate this phenomenon , we conducted a Sholl analysis , which revealed a significant increase in dendrite arbor complexity with isoflurane exposure ( p < 0 . 0001; Fig 1F ) . This finding seems to represent an acceleration of dendrite growth , because dendritic length and complexity in the isoflurane group no longer differs from controls at P60 ( S1A–S1D Fig ) . Branch number is unaffected at either time point ( S1E Fig ) . Cell positioning within the dentate gyrus is unaffected ( S1F Fig ) , suggesting no deficits in migration , but soma size is significantly increased with isoflurane exposure at P30 , but not P60 ( S1G Fig ) , further suggesting an abnormal acceleration in DGC growth . The change in timing of dendritic development resulting from anesthetic exposure represents a novel and surprising effect of anesthetics on the developing brain . In vitro studies of axon growth suggest that volatile anesthetics such as isoflurane may slow the growth of axons and prepolarized neurites [28 , 29] , but axons and dendrites have substantial differences in their developmental properties [30] . A cell culture study that specifically examined dendrites found that exposure to propofol , but not midazolam , at 1 day in vitro ( DIV ) caused a lasting suppression of dendritic growth in GABAergic neurons [31] . While the timing of exposure and measurement loosely resembles our model , the difference in anesthetic agents and the lack of an in vivo context may explain the disparate findings . Furthermore , the DGCs are primarily glutamatergic and have properties quite distinct from the GABAergic interneurons population [32] . The only other study to assess the effects of anesthetics on dendrites in vivo found no acute change in the dendritic arbors of prefrontal cortex pyramidal neurons in P16 rats 6 hours after isoflurane exposure , but did not examine longer-term effects [33] . Thus , it is unclear whether the transient dendritic hypertrophy we observed might generalize beyond the DGCs exposed early in their development . Abnormalities in dendritic arbor development may have a profound impact on the function of a neuron via effects on the neuron’s synaptic field and pathologic overgrowth of dendrites has been hypothesized as a component of human neurodevelopmental diseases such as autism and schizophrenia [34] . Overgrowth of dendritic arbors has been observed in some animal models of Fragile-X syndrome [35] and autism [36] . However , we cannot determine whether the phenomenon that we observed is a cause of neuronal dysfunction or simply an epiphenomenon or adaptive response . We next asked whether isoflurane exposure results in long-term deficits in learning potentially attributable to a disruption of the function of the DGCs in which we have detected a morphological abnormality . Animals were exposed to isoflurane 1 . 5% for 4 hours at P18 and evaluated for deficits in the object-place recognition and the Y-maze tests of spatial learning at P60 ( Fig 2A ) . Both of these tasks are highly sensitive to alterations in the function of even small numbers of dentate gyrus neurons [37] . In the object-place recognition test , control animals spend significantly more time exploring objects in novel positions , but isoflurane-exposed animals exhibit no exploration preference ( Fig 2B , S2A and S2C Fig ) . Similarly , in the Y-maze test , unlike controls , isoflurane-exposed mice do not exhibit a preference for exploration of the newly available arm ( Fig 2C , S2B and S2D Fig ) . These data demonstrate that isoflurane exposure results in a lasting reduction in performance on the tasks of spatial learning that are dependent on the hippocampus and potentially sensitive to disruption of the development of the dentate gyrus . Next , we asked whether the observed changes in behavior after anesthetic exposure could be attributed to a lasting change in synapses of the DGCs . We used the retrovirus-mediated labeling method to quantify the density of dendritic spine formations at P60 , the age at which behavioral testing took place ( Fig 2A ) . Dendritic spines are dynamic , actin-dependent structures that are critical for learning and memory functions [38] . Spines have range of morphologies traditionally classified as stubby , thin , and mushroom shape . We found a small , but significant decrease in the total density of spines ( 12% decrease , p < 0 . 05 ) in anesthesia-exposed groups , and a very striking 39% decrease in the density of mushroom spines ( p < 0 . 001; Fig 2D , S2E and S2F Fig ) . No significant change was seen in the density of stubby or thin spines ( S2G and S2H Fig ) . Stubby spines are thought to be immature , thin spines are highly plastic and often transient unless converted into mushroom morphology , and mushroom spines typically represent long-lasting , stable synaptic connections [39] . The reduction in mushroom spine number suggests a substantial loss of synapses that could reasonably account for the reduced performance in spatial learning . Our finding of a reduction in spine density in the cohort of labeled DGCs is in keeping with an increasing body of work suggests that relatively immature neurons exposed to anesthetics may suffer a long-lasting loss of synaptic connections . Studies from 2 different groups in rats found that early postnatal exposure to either sevoflurane alone or a combination of isoflurane , midazolam , and nitrous oxide resulted in a long-term reduction in the number of synaptic profiles measured by quantitative electron microscopy in the hippocampal CA1 and subiculum areas , respectively [40 , 41] . The hippocampus is a relatively late developing structure [42] , and thus during early postnatal life , it has numerous neurons that are still undergoing active dendrite arborization and spine formation . In support of the hypothesis that developing neurons may be vulnerable to anesthesia-induced synapse loss , a long-term study of the effects of single dose propofol exposure in rats found a decrease in spines in the medial prefrontal cortex of rats exposed at P5 and measured at P90 [43] . In striking contrast , exposure at P15 actually caused an increase in spine number [43] , suggesting a notable difference in vulnerability that occurs with neuronal maturation . If developmental exposure to anesthetics can cause a lasting or even permanent loss of synaptic connections in key brain regions such as the hippocampus and pre-frontal cortex this event may represent a perturbation of the development of key brain circuitry , which , in turn , could explain an ongoing loss of cognitive function . A common feature shared by several neurodevelopmental disorders with phenotypes reminiscent of what we have observed in neurons exposed to anesthesia during development is an alteration in signaling in the mTOR pathway [44] . To determine whether activity in the mTOR system is altered by an early exposure to anesthetics we conducted quantitative fluorescence immunohistochemistry using an antibody against phospho-S6 ( pS6 ) , a reliable reporter of activity in this pathway [37] . We exposed mice to isoflurane at 1 . 5% for 4 hours and measured pS6 immunoreactivity in the DGC layer . We found an increase of greater than 2-fold in pS6 intensity at P30 ( p < 0 . 0005; Fig 3A , S3 Fig ) , which was still evident at P60 ( S4 Fig ) . This demonstrates a substantial and lasting upregulation of activity in the mTOR pathway in the dentate gyrus during the period in which we have observed morphological alterations . We next asked whether increased activity in the mTOR pathway is required for the isoflurane-induced deficits in spatial learning that we observed previously . Mice were exposed to isoflurane 1 . 5% for 4 hours on P18 , given intraperitoneal ( IP ) injections either of vehicle control or 20 mg/kg rapamycin , a pharmacologic inhibitor of mTOR , every other day between P21 and P29 , and then assayed for spatial learning via behavioral testing ( Fig 3B ) . To confirm that our rapamycin treatment effectively suppressed isoflurane-mediated activity in the mTOR pathway , we tested for pS6 immunoreactivity in the dentate gyrus of animals exposed to isoflurane and then treated with rapamycin . We found that rapamycin treatment significantly reduced pS6 immunoreactivity compared to isoflurane and that levels were comparable to untreated controls ( Fig 3A ) . Subsequently , we tested whether blocking mTOR activation induced by isoflurane could rescue the morphological disruptions and behavioral deficits observed after isoflurane treatment . First , we tested the effects of mTOR inhibition on isoflurane-induced dendrite growth acceleration . We found that rapamycin treatment after isoflurane significantly reduces total dendritic length compared with the control group ( p < 0 . 05 ) and that dendritic length in the isoflurane plus rapamycin group is not significantly different from controls ( Fig 3C ) . Sholl analysis indicates that rapamycin treatment after isoflurane results in arbor complexity that is more similar to what is measured with control conditions than with isoflurane alone ( Fig 3D ) . Rapamycin treatment alone has no effect on spatial learning ( S5A–S5D Fig ) , but rapamycin treatment after isoflurane exposure restores performance to near control levels in both the object-place recognition and Y-maze tests ( Fig 3E and 3F and S5C–S5F Fig ) . Subsequently , we assayed the numbers of dendritic spines in the retrovirus-labeled DGCs exposed to isoflurane with and without rapamycin treatment . We find no significant difference in the total dendritic spine density between the vehicle and rapamycin groups exposed to isoflurane ( Fig 3G and S5G and S5H Fig ) . However , when only the mushroom spines are considered , we find an increase in spine density in the rapamycin group compared to the vehicle treated group ( p < 0 . 0001 ) ( Fig 3G ) . There is no significant difference in mushroom spine density between the control group that did not receive isoflurane and the isoflurane plus rapamycin group ( Fig 3G ) . By contrast stubby spine density appears to be reduced by isoflurane and rapamycin treatment relative to isoflurane alone , and no significant differences are measured in thin spines ( S5I and S5J Fig ) . Thus , our data suggest that rapamycin , by inhibiting the mTOR pathway , prevents an isoflurane-induced reduction in stable synaptic connections . Taken together , our findings indicate that isoflurane causes a sustained increase in activity in the mTOR pathway that leads to dendrite growth acceleration and either synapse loss or reduced synapse formation in DGCs . Superficially , our results are at odds with a previous study , showing no activation of mTOR in the hippocampus after sevoflurane anesthesia [45] , but in the other study , measurements were taken hours after exposure , whereas in the current study we made measurements 1 to 2 weeks later , with a goal of elucidating longer term effects on neuronal development . The mTOR pathway is an intriguing potential mechanism of injury , as it has been implicated both in normal functions in brain development and it is disarrayed in a wide-range of human neurodevelopmental disease [46] . The mTOR pathway is involved in normal development of dendrites and synapses through its actions , integrating signals from the phosphoinositide 3 kinase-protein kinase B ( PI3K-Akt ) system , which is influenced by both activity and neurotrophic growth factors , such as brain-derived neurotrophic factor ( BDNF ) , that act via tyrosine kinase receptors [47 , 48] . Downstream mediators of mTOR that influence synaptogenesis include actions on mitochondrial function , lipid synthesis , and translational control via the mTOR1 complex and RhoGTPase actions on the cytoskeleton via the mTOR2 complex [47 , 48] . Enhanced activity in the mTOR pathway induced by knockdown of disrupted in schizophrenia 1 ( DISC1 ) in newly generated DGCs in adult animals causes accelerated development of dendrites , similar to what we have seen , but it is accompanied by an increase in spine formation [37 , 49] , which stands in apparent contrast to the spine decrease seen in our model . However , several key differences exist between the models that may explain this discrepancy: ( 1 ) our study follows the neurons in question for a much longer period , and thus it is possible that overgrowth leads to spine loss over a sufficient length of time; ( 2 ) in the DISC1 study , only the studied cohort of newborn DGCs was affected , whereas in our model isoflurane may exert an effect on the surrounding cells as well as the labeled cells; ( 3 ) the influence of the DISC1 knockdown was permanent , whereas in our model isoflurane is given transiently and its effects may therefore be manifested differently over time; and ( 4 ) we observe overgrowth at P30 , which is no longer apparent at P60 , and it is possible that early acceleration of growth followed by slowing may induce synaptic loss as a result of a disruption of the normal timing of dendritic arbor growth relative to dendritic spine growth . Additionally , it should be noted that the effects of changes in mTOR signaling may depend on context and on activity in other systems . For instance , Kumar et al . showed that transient inhibition of mTOR , which alone decreases spine formation , could actually increase formation of mushroom spines in a developmental model when it was accompanied by activation of the PI3K-Akt system or treatment with BDNF [50] . In this model , an increase in mushroom spines is accompanied by a decrease in filopodial protrusions that the authors interpret as a destabilization or regression of synapses . Isoflurane and other anesthetics act on multiple targets in developing neurons , and thus understanding their actions on spine and synapse formation will require a full investigation of how each component of the signaling systems that underlie this process is affected . Given the complexity of the mTOR pathway , the effects of a lasting change in the activity of this pathway are difficult to predict . A sustained increase in mTOR pathway tone certainly has the potential to powerfully alter neurotransmission in the dentate gyrus , as evidenced by the appearance of epileptiform activity in mice with selective deletions of phosphatase and tensin homolog , an mTOR pathway inhibitor , in DGCs [51] . Thus , we hypothesize that isoflurane-induced changes in mTOR signaling have the potential to disrupt the course of neuronal development in the dentate gyrus and perhaps in other brain areas in such a way as to disrupt cognitive function . Even if our findings do not generalize to other cell types and brain regions , they still have significant implications given that substantial populations of DGCs are generated in rodents [52] , nonhuman primates [53] , and humans [52] during the hypothesized period of susceptibility to anesthesia-induced cognitive deficits in each of these species and these neurons are critical for learning across species . Furthermore , our findings suggest the possibility that harmful effects of mTOR overactivation could be prevented . Complex neurodevelopmental cognitive disorders like autism , in which the pathophysiology may involve changes in mTOR pathway activity that stem from a combination of genetic and environmental factors occurring at unknown times during development , present great challenges in designing an mTOR targeted therapy [54] . By contrast , anesthetic effects on cognitive function result from a brief toxic insult at a known time , and therefore might be more amenable to treatment . Thus , our discovery of a novel , reversible mechanism of injury in developmental anesthetic neurotoxicity has translational potential that can be explored in future studies .
All study protocols involving mice were approved by the Animal Care and Use Committee at the Johns Hopkins University ( protocol MO14M315 ) and conducted in accordance with the NIH guidelines for care and use of animals . C57BL/6 mice were housed in a temperature- and humidity-controlled room with a 12:12 hour light:dark cycle , and provided with ad libitum access to water and food . Both sexes were equally represented in all experiments . No animals were excluded . P18 mouse littermates were randomly assigned to 2 groups . In Group 1 ( isoflurane ) , mice were exposed to 1 . 5% isoflurane carried in 100% oxygen for 4 hours . A calibrated flowmeter was used to deliver oxygen at a flow rate of 5 L/min and an agent-specific vaporizer was used to deliver isoflurane . In Group 2 ( control ) , mice were exposed to room air for 4 hours . Animals were returned to their cages together with their littermates upon regaining righting reflex . Mice were continually monitored and recorded for skin temperature , heart rate , and oxygen saturation during the 4-hour isoflurane treatment ( PhysioSuite; Kent Scientific , Torrington , CT ) . Intracardiac puncture was used to collect left ventricular blood samples from selected sentinel animals , and those confirmed to be arterial are reported . Engineered self-inactivating murine retroviruses were used to express GFP under Ubiquitin promotor ( pSUbGW vector ) specifically in proliferating cells and their progeny [55 , 56] . High titers of engineered retroviruses ( 1 x 109 unit/ml ) were produced by cotransfection of retroviral vectors and VSVG into HEK293gp cells followed by ultracentrifugation of viral supernatant as previously described [24 , 49 , 55–57] . After induction with a single ketamine injection ( 50mg/kg ) , high titers of GFP-expressing retroviruses were stereotaxically injected into the P15 mice dentate gyrus through a 32-gauge microsyringe ( Hamilton Robotics , Reno , NV ) at 2 sites of the following coordinates relative to the bregma ( mm ) : AP: −2 . 2 , ML: ±2 . 2 , DV: −2 . 4 . The retrovirus-containing solution was injected at a rate of 0 . 025 μl/min for a total of 0 . 5 μl per site . After infusion , the microsyringe was left in place for an additional 5 minutes to ensure full virus diffusion and to minimize backflow . After surgery , mice were monitored for general health every day until full recovery . In order to test for a possible confound related to the use of ketamine anesthesia , pS6 immunoreactivity in the dentate gyrus was quantified at P30 in naïve control animals and compared to pS6 immunoreactivity in animals doses with ketamine as above . No significant difference is seen in pS6 levels between these groups ( S6 Fig ) . After transcardial perfusion fixation with 4% paraformaldehyde/PBS , brains were sliced transversely ( 50 μm thick ) with microtome and processed for immunohistochemistry . Primary antibodies , including goat anti-GFP ( Rockland , 1:1000 ) and chicken anti-GFP ( Millipore , 1:1000 ) were used . Immunofluorescence was performed with a combination of Alexa Fluor 488- or Alexa Fluor 594-labeled anti-goat , anti-chicken , or anti-rabbit secondary antibodies ( 1:250 ) and 4ʹ , 6ʹ-diaminodino-2-phenylindole ( DAPI , 1:5000 ) . For analysis of pS6 levels , primary antibodies against pS6-Ser235/236 ( rabbit , 1:1000 , Cell Signaling ) were used . Effective immunostaining of pS6 required an antigen retrieval protocol as previously described [58] . Briefly , sections were incubated in target retrieval solution ( DAKO ) in 85°C for 20 minutes followed by washing with PBS for t3 times before the incubation with primary antibody . Images were acquired on a confocal system ( Zeiss LSM 710 or Leica SPE ) and morphological analyses were carried out as previously described [24 , 49 , 55 , 56 , 58 , 59] . Images for dendritic and spine morphology were deconvoluted with Auto Quant X ( Media Cybernetics , Rockville , MD ) using the blind algorithm , which employs an iteratively refined theoretical PSF . No further processing was performed prior to image analysis . For visualization , brightness , and contrast levels were adjusted using Image J ( NIH ) . For analysis of dendritic development , three-dimensional ( 3D ) reconstructions of entire dendritic processes of each GFP+ neuron were obtained from Z-series stacks of confocal images using excitation wavelength of 488 nm at high magnification ( x 40 lens with 0 . 7x optical zoom ) . The two-dimensional ( 2D ) projection images were traced with NIH Image J plugin , NeuronJ . All GFP+ DGCs with largely intact , clearly identifiable dendritic trees were analyzed for total dendritic length . The measurements did not include corrections for inclinations of dendritic process and therefore represented projected lengths . Sholl analysis for dendritic complexity was carried out by counting the number of dendrites that crossed a series of concentric circles at 10 μm intervals from the cell soma using ImageJ ( NIH ) . For complete 3D reconstruction of spines , consecutive stacks of images were acquired using an excitation wavelength of 488 nm at high magnification ( x 63 lens with 5x optical zoom ) to capture the full depth of dendritic fragments ( 20–35 μm long , 40~70 dendritic fragments in each condition analyzed ) and spines using a confocal microscope ( Zeiss , Oberkochen . Germany ) . Confocal image stacks were deconvoluted using a blind deconvolution method ( Autoquant X; Media Cybernetics , Rockville , MD ) . The structure of dendritic fragments and spines was traced using 3D Imaris software using a “fire” heatmap and a 2D x–y orthoslice plane to aid visualization ( Bitplane , Belfast , UK ) . Dendritic fragments were traced using automatic filament tracer , whereas dendritic spines were traced by means of an autopath method with the semiautomatic filament tracer ( diameter; min: 0 . 1 , max: 2 . 0 , contrast: 0 . 8 ) . For spine classification , a custom MatLab ( MathWorks , Natick , MA ) script was used based on the algorithm; stubby: length ( spine ) <1 . 5 and max width ( head ) <mean_width ( neck ) *1 . 2; mushroom: max width ( head ) >mean width ( neck ) *1 . 2 and max_width ( head ) >0 . 3; if the spine was not classified as mushroom or stubby , it was defined as long-thin . Axonal bouton volume from axonal fragments was measured by using 3D Imaris software and using a magic wand menu ( Bitplane , Belfast , UK ) after deconvolution . For analysis of pS6 levels , the sections were processed in parallel and images were acquired using the identical settings , ( Zeiss LSM 710 , 20X lens ) . Fluorescence intensity was measured within the granular cell layer using ImageJ ( NIH ) and the value was normalized to background signal in the same image . These data were then subsequently normalized to the area of the dentate gyrus granule layer as defined by DAPI staining . All experiments were carried out in a blind fashion to experimental conditions . Sixty-day-old mice housed in groups ( 5 mice per cage ) were handled for at least 2 minutes per day for 3 days before the start of the behavioral experiments . All behavioral tests were performed during the light phase of the cycle between 8:00am and 6:00pm . Experimenters were blind to the samples when behavioral tests were carried out and quantified . The numbers of mice per condition are indicated in the figure legends . P21 mouse littermates were given IP injections of rapamycin ( Sigma-Aldrich , St . Louis , MO ) prepared from a stock solution ( 25 mg/ml in 100% ethanol , stored at -20°C ) diluted to a final concentration of 4% ( v/v ) ethanol in the vehicle . Vehicle consisted of 5% Tween 80 ( Sigma-Aldrich , St . Louis , MO ) and 10% polyethylene glycol 400 ( Sigma-Aldrich , St . Louis , MO ) as previously described [58 , 60 , 61] . Both rapamycin- and vehicle-treated mice received the same volume for each injection ( 200 μl ) . Mice received treatments at 48 hour intervals from P21 to P29 . Results are expressed as mean ± SEM . A one-tailed Student t test or ANOVA with Bonferroni test for intergroup comparisons were used for most statistical comparisons between groups as described in the figure legends using Prism Software ( Graphpad Software Inc , La Jolla , CA ) . For Sholl analysis ANOVA was used at each point to test for differences between distributions . All data examined with parametric tests were determined to be normally distributed , and the criteria for statistical significance was set a priori at p < 0 . 05 . Sample sizes were predicted based on experience from previous similar work [24] . All relevant data are available from the authors . | The United States Food and Drug Administration has recently warned that exposure to anesthetic and sedative drugs during the third trimester of prenatal development and during the first 3 years of life may cause lasting impairments in cognitive function . The mechanisms by which this undesirable side effect occurs are unknown . In this manuscript , we present evidence in mice that early developmental exposure to isoflurane , a canonical general anesthetic , disrupts the appropriate development of neurons in the hippocampus , a brain region associated with learning and memory . Isoflurane also causes up-regulation of the mechanistic target of rapamycin ( mTOR ) pathway , a signaling system that has been associated with other neurodevelopmental cognitive disorders . Treatment with an inhibitor of the mTOR pathway after isoflurane exposure normalizes neuronal development and also ameliorates the impairments in learning induced by isoflurane . We conclude that early exposure to isoflurane can cause learning deficits via actions on the mTOR pathway , and that this mechanism represents a potentially druggable target to minimize the side effects of anesthetics on the developing brain . | [
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] | 2017 | Early postnatal exposure to isoflurane causes cognitive deficits and disrupts development of newborn hippocampal neurons via activation of the mTOR pathway |
Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another , and is of particular importance for the development of animal models of human disease . While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology , sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes , and has limited utility for analyzing families with several parologous members . Nevertheless , we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data . Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia . We demonstrate that even among closely related genes , our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone . We also demonstrate that the landscape of functional similarity is often complex and that definitive “functional orthologs” do not always exist . Even in these cases , our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user .
The idea that protein sequence similarity implies shared function is a central paradigm in modern biology , allowing experimental knowledge obtained from model organisms such as yeast or mouse to be applied to our understanding of human diseases , or to be transferred via functional annotations to newly sequenced genomes . When no clear one-to-one homology relationship exists , however , and proteins of interest belong to families with several paralogous members ( which may have arisen from post-divergence duplications ) , our ability to correctly transfer functional annotations based on sequence-similarity is fundamentally limited . Such difficulties regularly arise in model organism studies of disease , where it is essential to identify which proteins and pathways are functionally analogous to the mammalian protein of interest . Attempts to understand human laminopathies through studies in Drosophila , for example , are limited by the lack of knowledge of the relationships among the human and fly lamin families; no one-to-one orthology exists in this case , and the most promising approach seems to be one based on functional information , rather than sequence . Frequently , however , not enough directed experimental information is available to make an accurate comparison among all homologs , as it is often the case that some members of homologous families are much better studied than others ( see Figure S1 for a quantitative assessment ) . Thus , the possibility of leveraging high-throughput information to improve functional coverage is of significant interest . Prior efforts at identifying functional orthologs ( i . e . proteins that not only share sequence ancestry but also perform the same function ) have investigated the technical aspects of global network alignments , largely focusing on large-scale protein-protein ( physical ) interaction networks [1] , [2] , [3] , [4] . While PPI network alignments have been shown to yield orthology assignments that better conserve protein function when compared to using sequence similarity alone , such approaches have several limitations . Though it is constantly improving , the coverage of PPI networks is currently quite biased . Close homologs often differ widely in the number of reported interactions ( Figure S2 ) , and some proteins must often be excluded from consideration altogether because their interactions have not been assayed . On the other hand , protein interactions are often assayed under non-native conditions , potentially leading to the measurement of interactions that never occur biologically in spite of being chemically possible . Thus crucial information regarding cellular context , such as tissue specificity , may be entirely ignored when making PPI based orthology assignments . We address these issues by developing a new local approach to alignment that leverages large collections of diverse gene expression data to identify functionally analogous homologs whose relevance to a particular research context can be easily interpreted . Microarray data is a complementary source of high throughput functional information that is in many cases as accurate as large-scale protein-protein interactions for predicting function [5] , [6] and presents several advantages over PPI networks . It is one of the most unbiased and complete sources of functional information , as microarray experiments typically cover a large fraction of the genes in the genome , providing functional information about genes that have not been studied in any other way . Microarray studies are preformed with genes acting in their native context , preserving information about cellular state , tissue and developmental stage . Identifying functional orthologs based on integrative analysis of large microarray compendia presents new challenges , including dense , hard to align networks , need for robust integration that is comparable across organisms , and robust identification of similarly functioning proteins . While we focus our discussion on microarray data because it provides the most unbiased coverage , our Bayesian integration method can readily combine different data types . In fact , in our online interface we provide both microarray-only predictions and predictions that incorporate PPI information . Furthermore , while in this study we focus on microarray data because of its excellent coverage in model organisms , our approach can easily integrate other expression data such as RNA-seq as it becomes widely available [7] . We employ a local alignment to provide a robust measurement of functional similarity among homologous proteins . This is in contrast to earlier work [1] , [3] , [4] , [8] , [9] that focused on global network alignment . The richness of expression data make global alignment methods both impractical and ultimately undesirable , since expression similarity may not be easily reduced to one-to-one alignment ( see relevant results section ) . We develop and provide evaluation methods and functional gold standards for benchmarking prediction of functionally analogous proteins . While we demonstrate that our network similarity ( NS ) score accurately recapitulates experimental observations , we view it as evidence of functional analogy ( not of one-to-one correspondence ) and provide an exploratory interface that allows biologists to trace the sources of inferred similarity and evaluate its relevance to their area of interest .
While it has been shown that microarray data can been used to accurately distinguish functionally interacting gene pairs from unrelated ones , it is significantly more difficult to demonstrate that an integration successfully detects the subtle differences in the functional relationships between homologous proteins . Solving this evaluation problem is a prerequisite to providing functional analogy predictions that can be trusted in cases where a sequence comparison is ambiguous , as there is no reason to believe a priori that functional predictions which are accurate on average over all genes will be accurate on average over families of close homologs . While sequence based analyses are indispensable for identification of molecular functions or domain architecture of proteins , organisms often possess several genes that belong to a cohesive family whose members are predicted to have the same structural or enzymatic features , which are nevertheless involved in quite different biological processes . Consider , for example , the mouse genes Snap25 ( discussed briefly above ) and its paralog Snap23 . The two proteins have the same structural features [19] , [20] , interact with many of the same secretory proteins ( BioGrid human and mouse ) [21] and can complement one another in some assays [22] . However , physiologically they play quite different roles with Snap25 being involved in synaptic exocytosis while Snap23 is involved in a diverse array of trafficking processes in other tissues and cell types [23] , [24] , [25] , [26] , [27] . We hypothesize that our method , based on genomic datasets , is especially useful for the differentiation of such genes , as it provides a complementary characterization of function by specifically probing biological responses that may discriminate genes that otherwise appear similar at the sequence level . As this approach would be the most valuable for closely related genes ( as distantly related genes can be distinguished based on sequence alone ) , which are very hard to distinguish functionally based on sequence , we focus our analysis on homologs that belong to the same TreeFam family . Our first evaluation method is based on the tissue-expression pattern of genes . This evaluation is motivated by the fact that cross-species homologs that perform the same function are expected to express in similar tissues , reflecting the specific molecular requirements of different cell types . Correctly identifying homologous genes with similar tissue expression patterns is also a worthwhile goal in its own right , as tissue-specific expression is a critical facet of complex human disorders such as cancer and diabetes [28] , [29] . Although the anatomies of worm , fly and mouse are quite different , and many tissues in one organism have no obvious analog in another , all three organisms possess a nervous system . We thus use fly and mouse genes annotated with brain expression and worm genes annotated with neuronal expression ( all based on small-scale experiments such as in situ or GFP tagging ) to define a nervous system standard . ( Standard data is available in the download section of the website . ) We then evaluate our predictions by assessing whether nervous system expressed genes are predicted by our method to have greater similarity to their homologs that are also present in the nervous system than to those which are not . Our method is significantly better than chance at matching pairs of nervous-system-expressed homologs . Moreover , when we subject a sequence derived measure to the same evaluation , we find that our network similarity score ( based solely on the microarray-based network similarity ) outperforms sequence in all comparisons ( Figure 2 ) . It is important to note that our approach is complementary to—and can be used side by side with—sequence-alignment based methods . In fact , as our network similarity score and sequence-based scores provide orthogonal information , we find that a simple combined rank score can improve performance further in cases where both scores perform comparably-well ( as evident in the fly/worm nervous system comparison , Figure 2 ) . In addition to finding that our method can accurately pair homologs that are expressed in the same tissues , we aim to assess whether we are able to correctly identify cross-species homologs that play the same biological role in a cell . Thus , we also design an evaluation based on the Gene Ontology annotations , using a standard in which homologs that share “biologically specific” ( i . e . sufficient for follow-up experiments , see Methods ) GO Biological Process annotations are considered positives , while homologs that have been studied but do not have any specific annotations in common are considered negatives . This evaluation presents more challenges for our method since , in spite of the fact that we have excluded annotations based on sequence from the standard , it is likely that significant sequence bias remains , as sequence similarity often influences which proteins are studied experimentally and how they are annotated in the Gene Ontology . Nevertheless , our network similarity score still performs significantly better than background for nearly all comparisons when evaluated against this standard , and in many cases our network similarity score—or at least the combined network and sequence ranks score—outperforms sequence alone ( Figure 3 ) . Detailed analysis reveals that there are areas of biological annotations where our network similarity score is especially accurate ( as compared to sequence similarity ) at identifying functionally analogous homologs . For example , if we use a standard based just on the “cell-cycle” GO annotation , the NS score performs well for all comparisons involving C . elegans . The GO evaluation standard for the cell-cycle process is likely unusually unbiased for C . elegans , as this process has been studied extensively in worm due to the ability to perform genome-wide RNAi screens [30] . This suggests that such pockets of strong performance could in fact be reflective of the real power of our method , which in other cases may be obscured by incomplete and biased evaluation standards . To support this notion , we perform an evaluation using mitochondrial localization annotations , focusing on the homologs in mouse and yeast as these are the only two organisms in which genome-wide screens for mitochondrial localization and function have been performed [31] , [32] , [33] , [34] . Despite the fact that the network similarity score does not appear to outperform sequence in the comparison of these two organisms in a GO-based evaluation , it is better than sequence at pairing homologs that both localize to the mitochondria , further lending evidence that the NS score performance is under-estimated by the GO standard due to annotation bias . Results for all evaluations performed in this study , including those for datasets generated using PPI information and additional process-specific standards , are presented in Table S1 . We have shown that the network similarity score provides reliable functional information that is complementary to sequence-based comparisons , correctly differentiating homologs with shared tissue-specific expression and playing similar roles in biological processes . We now illustrate how our method may be used to gain insight into the functional landscape of protein families with complex evolutionary histories . We first consider the family represented by the mouse gene Snap25 , a SNARE protein that participates in the regulation of synaptic vesicle exocytosis [35] and is the target of the Botulinum toxin A [36] . The inferred evolutionary history ( Treefam ) of the family is shown in Figure 4A . The functional similarity among these genes follows a surprisingly different pattern ( Figure 4B ) : when the network similarity score is used to cluster the genes , two clear classes emerge . ( Note: while our method is targeted towards evaluating homologs from two different species , we use our meta-gene approach to compute the neighborhood overlap score within-species to provide a family-wide clustering in visualizations . ) In particular , mouse Snap25 shows strong functional similarity to fly Snap25 but has no significant similarity to mouse Snap23 or fly Snap24 , which are nevertheless similar to each other . This is particularly unexpected since multiple alignment analysis shows that the four genes arose from a single ancestor with subsequent lineage-specific duplications ( Figure 4A ) . Despite the lack of direct evolutionary relationships defining the functional classes we have inferred , this division identified by our method is supported by experimental evidence . The mammalian and Drosophila Snap25 are predominantly neuronal , localize to the synapse , and mediate synaptic exocytosis [23] . However , both Snap23 and Snap24 have broader expression patterns and contribute in diverse processes . Snap23 is ubiquitously expressed [24] and has been shown to participate in glucose uptake in adipocytes [27] and platelet and mast-cell secretions [26] , [37] and is localized to cell bodies in neurons [25] . Likewise Drosophila Snap24 appears in cell bodies of neurons and is expressed in other cell types , and has been shown to be involved in salivary gland exocytosis [38] . Thus the Snap25 cluster is comprised of neuron specific genes , while the Snap23/Snap24 cluster consists of genes that partake in regulated exocytosis in other cell types . The distribution of yeast and C . elegans members among these functional classes is also consistent with this view . The yeast SEC9 gene shows strong functional similarity to non-neuronal family members , while the C . elegans family member ric-4 that appears in the neuronal cluster is expressed exclusively in neurons [39] . The other C . elegans member ( aex-4 ) clusters with non-neuronal genes , though it does show similarity with mouse Snap25 and ric-4 . The tighter association with non-neuronal genes is supported by experiments that demonstrate that aex-4 expresses in intestine , where it functions in signaling between the intestine and neurons that regulate defecation [40] . Since the fly and mouse homologs are predicted to have arisen by lineage-specific duplications , it appears that the independent emergence of neuronal and non-neuronal genes is an instance of convergent evolution . Interestingly , the development of both these types of Snap25 homologs may also be evident in the two C . elegans homologs , despite the fact the two genes appear to have arisen from a much earlier duplication . This observation raises intriguing questions regarding what biochemical constraints could be driving the emergence of the neuronal/non-neuronal specializations . It has recently been shown that the tissue-expression and functional role of the mammalian proteins parallels their calcium sensitivities [41] , and it is possible that similar constrains hold in other organisms as well . In any case , we hope that our method will allow further examination of questions regarding convergent evolution of homologs , which by their nature cannot be addressed by sequence-based approaches alone . Protein families with several lineage-specific duplications present a particular challenge for the transfer of disease models between organisms , since sequence similarity produces ambiguous mappings in such cases . For example , there has been significant interest in generating Drosophila models of vertebrate laminopathies that has been complicated by the lack of one-to-one orthologs . Vertebrate lamins have been classified into two types , type-A and type-B . Mutations in type-A cause a large class of diseases collectively termed laminopathies , such as muscular dystrophy and premature aging , while viable type-B mutations are extremely rare ( the two B-type genes together are required for cellular viability while type-A lamin is not ) . Two main attributes are associated with the type-A/type-B distinction . First , B-type lamins are expressed ubiquitously while A-type lamins have a dynamic developmental expression profile . Second , type-B lamins possess a CaaX box that is prenylated and anchors the protein to the nuclear envelope , while mature type-A proteins do not [42] . Unlike many other invertebrates that have a single lamin gene , the Drosophila genome has two lamin genes ( Lam and LamC ) that resemble type-B and type-A lamins , respectively . Lam is ubiquitously expressed and has a CaaX box while LamC is developmentally regulated and lacks the anchor motif [43] . However , despite the striking similarities , the two types of lamins appear to have developed independently in the Drosophila and vertebrate genomes [2] , leading to debate in the literature regarding how to best model vertebrate laminopathies in Drosophila [44] , [45] , [46] . Using our NS score to cluster mouse , Drosophila , and C . elegans lamins we are able to recapitulate the global type-A/type-B pattern: mouse Lmna clusters with Drosophila LamC , while mouse Lmnb genes cluster with Drosophila Lam and C . elegans lmn-1 ( which is also ubiquitously expressed and possesses a CaaX box , and thus is classified as Type-B [47] ) . However , our analysis also shows a surprising level of similarity between Mouse Lmna and the invertebrate type-B lamins , suggesting a more complex pattern of functional similarity ( Figure 5 ) . The similarity between Lmna and lmn-1 is perhaps not surprising , since lmn-1 is the only C . elegans lamin and thus must perform both type-B and type-A functions . The similarity between Lmna and Lam is more intriguing , however , as it suggests that no single functional homolog of vertebrate type-A lamin exists in the Drosophila genome . This is supported by the diverse phenotypes produced by Lam and LamC mutations in Drosophila . Mutations in LamC cause muscle nuclear envelope defects similar to those of the vertebrate A-type lamins [44] , while Lam mutations cause locomotion defects and premature aging [45] . Consistent with these observations , our analysis suggests that while LamC is purely a type-A lamin , some type-A functions may be performed ( possibly exclusively ) by Lam alongside its role as a Type-B lamin , suggesting it could play an important role in understanding human type-A laminopathies . While we have shown that the network-based score can aid in finding homologs that perform the same function and have similar phenotypes , the nature of functional conservation is complex and may not be easily summarized with a single score , as demonstrated by the lamin example . Our method was in fact designed with this challenge in mind . Once genes from different organisms are made comparable by projecting their correlation neighborhoods onto organism-independent meta-genes , our network similarity score is generated using the neighborhood overlap metric ( see Methods ) that allows one to investigate which specific genes contribute to the functional similarity between two homologs . Such investigation is not easily possible if we apply other similarity metrics to the meta-gene neighborhoods , such as correlation ( whose use would otherwise have little impact in the performance of our method in the evaluations described above ) . Our choice of the neighborhood overlap metric is primarily to allow detailed investigations of the underlying “reasons” for high network similarity scores . To enable biologists to easily perform such analysis , we have made our method accessible through an interactive web interface that not only provides network similarity scores , but also allows the user to explore the source of inferred similarities . The user can identify precisely which meta-genes connections are shared by a pair of homologous genes and evaluate whether the overlap is representative of the particular biological functions that the user is interested in . As an example we consider the gene SOD1 in S . cerevisiae , which is the only representative of the cytosolic superoxide dismutase family . In contrast to S . cerevisiae and other organisms , C . elegans possesses two genes that belong to this family , sod-1 and sod-5 . The web interface allows one to explore the functional relationship among these three genes . As shown in Figure 6 , sod-1 and sod-5 overlap completely disjoint functional regions of the SOD1 neighborhood in a manner consistent with what is known about their function . sod-1 functions during reproductive growth , when the animal is most metabolically active , while sod-5 is most active in the diapausal dauer stage [48] , an alternative larval stage induced by starvation , when the animal does not feed and must rely on internal lipid stores . Consistent with these functional differences , the intersection of the neighborhoods of yeast SOD1 and worm sod-1 is enriched for terms associated with high metabolic activity and contains clusters of genes that encode for enzymes in the TCA cycle , such as malate and citrate dehydrogenases . In contrast , the overlap between yeast SOD1 and worm sod-5 contains meta-genes that have been implicated in lipid metabolism , such as the yeast TES1 ( a peroxisomal acyl-CoA thioesterase ) , members of a glutathione peroxidase family ( TF105318 ) and aldehyde dehydrogenase family ( TF300455 ) . In spite of the fact that sod-1 has a very large and significant intersection with SOD1 , none of these genes associated with lipid metabolism are among the top interactors of sod-1 ( F54D8 . 3 , an aldehyde dehydrogenase , ranks 1256th while all others rank bellow the top 5000 ) . Since lipids are not a major source of energy storage for S . cerevisiae , lipid metabolism and TCA cycle are typically active concurrently and are not separated in our compendium of expression data . In C . elegans , however , the two branches of metabolism are utilized to different extents during development . TCA cycle genes are down-regulated while lipid metabolism genes are up-regulated during the dauer stage [49] , and our analysis suggests that the two cytosolic superoxide dismutases have specialized to be active under specific metabolic conditions in worm . Interestingly the family of mitochondrial superoxide dismutases ( SOD2 , sod-2 , sod-3 ) show a similar pattern of specialization , as can be examined through the web interface .
We have developed a method to leverage a large compendium of gene expression data to provide a measure of functional similarity of homologs across organisms . Our measure reliably predicts gene pairs that share tissue expression patterns or participate in the same biological process even for closely related genes and can therefore serve as a useful tool for identifying homologs with analogous function , as well as a way of examining more general questions about the landscape of functional similarity . By leveraging a large compendium of expression data , our method yields both good gene coverage and extensive functional coverage by combining datasets from many tissues and perturbations . As expression datasets provide information for many genes that have not been studied in any other way , our method is , for many homologous gene-pairs , the only way currently available to explore functional relationships . Our method is also designed to allow detailed examination of the sources of our functional predictions through the provided web interface ( available at http://networkhomologs . princeton . edu ) , maximizing its utility as an exploratory tool for biology researchers , especially in cases where functional similarity is complex and context dependent and no “best” ortholog exists .
Microarray data for S . cerevisiae was identical to that used in [17] and C . elegans data was identical to that used in [50] . Raw CEL files for Data for D . melanogaster ( DrosGenome1 ) and M . musculus ( Mouse420_2 ) were downloaded from Gene Expression Omnibus ( GEO ) [51] and processed using Bioconductor [52] with VSN normalization [53] and probesets collapsed according to the algorithm described in [54] . See Text S1 for a list of datasets and sources . Standards for Bayesian integration were constructed using a custom set of Gene Ontology terms described in [54] . A pair of genes is considered positive if both genes are experimentally annotated to the same specific GO term though homologous pairs were excluded from positive examples . Negative pairs were selected at random from the set of genes included in positive examples to give a prior probability of functional interaction of 0 . 05 . Bayesian integration was performed as described in [17] using the BNCreator tool provided with our open-source Sleipnir library [55] . While there is a large number of publically available microarray experiment for Mus musculus , many datasets perform randomly with respect to this Gene Ontology standard and thus cannot meaningfully affect functional relationship probabilities . To avoid decreases in performance due to violations in the independence assumption and overfitting only the top 100 datasets ( as measured by area under performance recall curve over the top 10% recall ) were used in the integration of the mouse network ( Small modificatin to this precedure did not affet the performance of the resulting network ) . Functional interaction networks were used to define gene neighborhoods . A “hard-cutoff” neighborhood of a query gene is defined as all genes connected to the query with a resonable probability . While the hard-cutoff of 0 . 5 is a natural probability cutoff ( and was used by us in this paper ) , this too can be adjusted by the user in the on-line interface . As not all genes have a sufficient number of neighbors above this threshold , we have also set the minimal size of the neighborhood at 50 , termed “soft-cutoff” . While using the soft-cutoff method slightly decreases our evaluation performance , we believe that it is nevertheless important to give scores for as many gene pairs as possible and we use this method throughout the paper . The soft-cutoff is the default option in our web interface though it can be turned off by the user . For the purpose of all evaluations and figures a hard-cutoff of 0 . 5 and a soft-cutoff of 50 is used . Based on our calculations a hard-cutoff of 0 . 5 performed best overall though may not be optimal for all queries . After gene neighborhoods are defined based on our functional interaction networks , TreeFam B families [2] were used to map gene neighborhoods from different organisms onto a species-independent space of meta-genes . The TreeFam system defines families that evolved from a single gene in the last common ancestor of all animals ( with closely related plant and fungi genes included ) . A small number of genes that appeared in more than one TreeFam family were excluded from consideration . To map a gene's neighborhood onto the meta-gene set , a meta-gene is considered present if any of the member genes are present , thus for multi-gene families a connection to any of the members is sufficient . To determine the functional similarity of genes from different organisms , we compute the hypergeometric p-value of their meta-gene neighborhood overlap . The background set of TreeFam families used for the p-value computation is specific to the organism pair considered and is defined as all TreeFam families that contained at least one gene from each organism such that the gene is also present in our microarray compendium . Likewise for the purpose of the p-value calculation the size of each gene's TreeFam neighborhood is considered to be the set of those TreeFam families that are both present in the gene's neighborhood and in the organism-pair-specific background set . Our evaluation methodology is motivated by how we believe our system is likely to be used by biology researchers . In particular , given a query gene we would like to evaluate if our network similarity score produces a ranking of potential homologs that is consistent with what is known about the genes experimentally . We expect that homologs expressed in the same tissue or those that show the same phenotype as the query should be ranked above those that do not share these functional attributes . In order to evaluate this we define various standards for homolog pairings . In the nervous system standard homolog pairs that both express in the nervous system are considered positive , while homolog pairs whose expression has been studied but were not co-expressed in the nervous system are considered negative . For Gene Ontology based evaluations we used a set of specific GO terms with experimental evidence codes ( the same set that is used for standard construction ) . Homolog pairs that shared at least one such annotation were considered positive , while homolog pairs that have been experimentally annotated ( to this set of specific GO terms ) but did not have annotations in common are considered negatives ( See Dataset S1 for all evaluation standards and annotation sources ) . To perform the evaluation we consider a single query gene with several homologs in another organism such that that at least one query-homolog pair would be considered positive and at least one would be considered negative according to a particular standard ( nervous system standard or GO standard ) . Our evaluation is designed to determine if our functional similarity ranks the query-homolog pairs non-randomly relative to a particular standard . While it is possible to compute AUCs on a per-query basis , the set of query-homolog pairs defined in the standard is often quite small ( for GO derived standards often there are only 2 pairs , the minimum possible ) . To increase the statistical power of this evaluation we combine results from all query genes by first normalizing their ranks so that the query-homolog pairs with the highest score receives a rank of 1 and the pair with lowest score receives the rank of 0 with the remaining pairs ( if any ) falling somewhere in between . The normalized ranks are then combined to compute a global AUC and determine significance . To compute GO enrichments for Treefam families we consider a family to be annotated to a particular term if any of the member genes have an experimental annotation for that term . GO enrichment is computed as hypergeometric p-values with the background count taken from the organism-pairs-specific background families defined above . Thus , while the annotations are not organism-specific , the enrichment computation does depend on the organism pair being considered . All p-values are cutoff at and FDR of 0 . 05 . | Common ancestry is a central tenet of modern biology , as genes from different species often show a high degree of sequence similarity , making it possible to study analogous processes across model organisms . However , many genes belong to large families with several duplicates and the relationship between genes from different species is often not one-to-one , complicating the transfer of experimental knowledge . We present a method that uses a large compendia of high-throughput expression data , that covers many genes that have not been analyzed in any other way , to systematically predict which genes are most likely to participate in the same biological process and thus have analogous function in different organisms . We show that our method agrees well with current experimental knowledge and we use it to investigate several families of genes that demonstrate the complexity of functional analogy . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"computational",
"biology/genomics"
] | 2011 | Accurate Quantification of Functional Analogy among Close Homologs |
The complexity of clinical manifestations commonly observed in autoimmune disorders poses a major challenge to genetic studies of such diseases . Systemic lupus erythematosus ( SLE ) affects humans as well as other mammals , and is characterized by the presence of antinuclear antibodies ( ANA ) in patients’ sera and multiple disparate clinical features . Here we present evidence that particular sub-phenotypes of canine SLE-related disease , based on homogenous ( ANAH ) and speckled ANA ( ANAS ) staining pattern , and also steroid-responsive meningitis-arteritis ( SRMA ) are associated with different but overlapping sets of genes . In addition to association to certain MHC alleles and haplotypes , we identified 11 genes ( WFDC3 , HOMER2 , VRK1 , PTPN3 , WHAMM , BANK1 , AP3B2 , DAPP1 , LAMTOR3 , DDIT4L and PPP3CA ) located on five chromosomes that contain multiple risk haplotypes correlated with gene expression and disease sub-phenotypes in an intricate manner . Intriguingly , the association of BANK1 with both human and canine SLE appears to lead to similar changes in gene expression levels in both species . Our results suggest that molecular definition may help unravel the mechanisms of different clinical features common between and specific to various autoimmune disease phenotypes in dogs and humans .
SLE is a chronic autoimmune disorder caused by multiple genetic and environmental risk factors . The disease tends to be clinically heterogeneous [1] , with manifestations ranging from relatively mild symptoms such as skin rash to severe impairment of functions of kidney , heart , lung , central nervous system and other organs [2 , 3] . A hallmark of the disease is the production of autoantibodies directed to self-antigens located in the nucleus , cytoplasm or on the cell surface . Antinuclear antibodies ( ANA ) are found in more than 95% of human SLE cases [4] . While SLE and SLE-related diseases were first described in human patients , they are also seen in other species including dogs with similar clinical manifestations [5–8] , which makes dog a good comparative model for genetic studies of human SLE . Nova Scotia duck tolling retriever ( NSDTR ) dogs appear to be predisposed to an SLE-like disease called immune-mediated rheumatic disease ( IMRD ) [5] , and also show strong predisposition to another related immune-mediated disease , steroid-responsive meningitis-arteritis ( SRMA ) , which share some features with human vasculitides including Kawasaki disease [9–14] , Henoch-Schönlein purpura [15] and Behçet’s disease [16] . It was shown in the recent years that circulating autoantibodies could be linked to specific types of both canine and human autoimmune diseases [8 , 17–19] . The immunofluorescent ANA test reveals two major patterns of ANA , homogeneous with a concomitant cytoplasmic and chromosomal reactivity and speckled with only cytoplasmic antigens stained . A previous study showed that among canine IMRD cases positive for indirect immunofluorescence ( IIF ) -ANA , 61% showed the speckled pattern ( ANAS ) , whereas 39% displayed homogeneous phenotype ( ANAH ) [5] . While the link between autoantibodies and sub-phenotypes of disease may be evident , especially in the case of tissue-specific antigens , the genetic factors behind this connection are not well known . To date , autoimmune diseases in both humans and dogs have been found associated with both major histocompatibility complex ( MHC ) class II alleles [20–25] and many other susceptibility genes [26 , 27] . Out of 40 loci that have been associated with human SLE the causative variant and susceptibility mechanism has been described only for a few [28] , leaving a lot of remaining work in understanding genome function and genotype-phenotype correlations . Overall , dogs share many of man’s common diseases , but they also have a unique genome structure , which greatly facilitates genome wide association studies ( GWAS ) and , compared to human studies , significantly fewer genetic markers and samples are required for gene mapping in dogs [29 , 30] . This is a result of the canine genome architecture characterized by high linkage disequilibrium within breeds being 40- to 100-fold longer compared to that observed in the human genome . The genomic architecture of domestic dogs has been formed by multiple genetic bottlenecks , founder effects and restricted breeding practices [30] . Gene mapping in dogs has proven successful with only ~100 cases and ~100 controls for complex traits and the list of disease-causing genes that have been identified in dogs is constantly growing ( some are reviewed in [31 , 32] ) . In fact , in the first successful GWAS for a canine complex trait we mapped five loci for IMRD and SRMA using only 57 controls and 81 cases including 37 with IMRD and 44 with SRMA [27] . To replicate these loci we performed fine-mapping using a total of 160 cases including 82 dogs with IMRD and 78 with SRMA and 173 controls , and replicated all of the five loci in at least one of the phenotypes analysed [27] . In this study , we have performed further functional and genetic dissection of all five GWAS loci identified previously . We present evidence for the association of 11 genes located on five chromosomes and specific genotypes for the canine leukocyte antigen ( DLA , equivalent to MHC ) class II to different sub-phenotypes of SLE-related disease and SRMA in dogs , as well as study the correlation between the associated SNPs and haplotypes and an altered expression of genes in the respective loci .
We first performed an indirect immunofluorescent ANA test on serum from 59 cases and 63 healthy control NSDTRs . Of these , 26 cases were classified as ANAH and 27 cases as ANAS ( six cases could not be classified due to lack of serum ) ( Fig 1 ) , while all healthy controls were ANA-negative . The polymorphic exon 2 was sequenced for each of the DLA-DRB1 , -DQA1 and-DQB1 genes in all dogs ( S1 Table ) . A total of five DLA-DRB1 , four DLA-DQA1 and five DLA-DQB1 alleles , forming five different haplotypes were identified ( S2 and S3 Tables ) . Ten different genotypes were observed in the study population ( Table 1 ) . Association analysis was performed for alleles , haplotypes and genotypes for the ANAH and ANAS case groups separately as well as the combined case group , and each was compared to controls ( Table 1 , S2 and S3 Tables ) . There was a significant association with haplotype 2 in ANAS cases compared to the control group ( OR = 9 . 7 and p = <0 . 0001 ) ( S3 Table ) , and an even higher OR in homozygote individuals ( OR = 21 . 0 and p<0 . 0001; genotype 2; 77 . 8% in ANAS cases vs . 14 . 3% in controls ) ( Table 1 ) . In total , 93% of the twenty-seven ANAS dogs were either homo- or heterozygous for haplotype 2 ( DLA-DRBI*00601/DQA1*005011/DQB1*02001 ) and twenty-one of them ( 77 . 8% ) were homozygous . No significant association was observed between the haplotypes or genotypes of DLA and the cases with ANAH pattern . However , at the allelic level a significant association was identified for the DQA1*00601 ( 86 . 5% in ANAH cases compared to 55 . 6% in controls; OR = 5 . 1 and p = 0 . 00017 , S2 Table ) . As homozygosity has been hypothesized to be a risk factor in itself , we removed the ANAS risk genotype and analyzed the remaining data for association to homozygosity regardless of haplotype . We found an increase in homozygosity in ANAH cases ( 62 . 5% ) vs . controls ( 14 . 8% ) , implicating a general homozygous disadvantage at DLA class II for ANAH dogs ( OR = 9 . 6 , p<0 . 0001; S4 Table ) . To search for candidate variants , the five genetic risk loci on CFA 3 , 8 , 11 , 24 and 32 that were previously identified by GWAS to be associated to IMRD and SRMA [27] were re-sequenced in four ANA-positive cases , two SRMA cases and three healthy dogs using Nimblegen capture and Illumina sequencing . Using standard methods , a total of 13 , 084 SNPs and 2 , 780 indels were detected . No structural changes or CNVs that differed between cases and controls were identified . Among those , 426 SNPs and 88 indels showed a potential functional effect by SeqScoring [33] . Next , 308 SNPs following the risk haplotype patterns were chosen for genotyping in the entire sample set ( S5 Table ) . For each locus , association analysis was performed between the 132 healthy controls and each of the different sub-phenotypes: 1 ) SRMA-affected dogs ( N = 66 ) , 2 ) all ANA-affected dogs ( N = 52 ) , 3 ) ANAS ( N = 24 ) and 4 ) ANAH staining pattern ( N = 21 ) . Furthermore , two conditional analyses were performed where only ANAS dogs homozygous for DLA risk haplotype 2 ( N = 18 ) and ANAH dogs homozygous for DLA ( N = 14 ) and ANAH dogs with the DQA1*00601 allele ( N = 16 ) were included respectively . For the risk locus on chromosome 11 the strongest association was observed with all ANA dogs ( Fig 2 ) , for the risk locus on chromosome 24 the strongest associated sub-phenotype was the ANAH dogs homozygous for DLA ( Fig 3 ) , while the risk locus on chromosome 32 showed two independent association signals for SRMA and ANAS with and without DLA association ( Fig 4 ) ; and the risk locus on chromosome 3 showed signals for all ANA , and for ANAH and ANAS tagged by different haplotypes , and to a lesser extent to SRMA ( Figs 5 and 6 ) . Finally , the risk locus on chromosome 8 also showed two separate signals to SRMA and ANAH dogs associated DLA ( Fig 7 ) . Of note , in the current report , as we further investigate the already associated and replicated regions [27] , we used raw p-values focusing on the highest peaks for each sub-phenotype to investigate its effect on gene expression . These regions have already been replicated in our previous genome-wide association study with P values ranging between 10-5-10-6 , with three loci on chromosomes 3 , 11 and 24 reaching genome-wide significance following fine-mapping and validation with P values of 10-11-10-13 [27] . The results are discussed in more detail below , together with expression analysis of the genes at each locus .
SLE and other autoimmune diseases occurring in humans have been intensely studied over the past years due to high heritability of such diseases and the availability of modern genetic tools . The most recent review article reports over 40 loci associated with human SLE [28] . The heterogeneity of SLE reflected by the 11 diagnostic criteria established by the American College of Rheumatology ( ACR ) [2 , 3] supports the current understanding that the genetic factors underlying such disparate clinical manifestations could be different as well . Recently , differential genetic associations with SLE based on the anti-dsDNA autoantibody status , either anti-dsDNA positive or anti-dsDNA-negative SLE , were reported [41] . The study of the relationships between the SLE risk alleles and clinical sub-phenotypes led to the finding that certain lupus manifestations are more dependent on the presence of multiple risk alleles , while others are more strongly associated with a single variant , for example , renal disease more significantly associated with HLA-DRB1 , and arthritis with the protective allele of ITGAM [42] . Interestingly , a third group of sub-phenotypes ( malar rash , discoid rash , photosensitivity , serositis , and neurological disorders ) was found not to be associated with the currently known SLE susceptibility genes , suggesting either the presence of not yet identified factors or potentially non-genetic factors , such as environmental conditions or epigenetic effects . Dogs , like other mammalian species also develop autoimmune disorders . Furthermore , the existence of canine breeds such as NSDTRs , which are predisposed to the development of autoimmune diseases , make them potentially useful for mapping disease genes and finding novel disease pathways [31] . A hallmark of SLE-related immune-mediated rheumatic disease ( IMRD ) in dogs is the presence of ANA autoantibodies , which display two major patterns when stained with indirect immunofluorescence , homogeneous and speckled ANA . Both sub-phenotypes have overlapping clinical and pathological features in NSDTRs such as musculoskeletal signs , including stiffness and joint pain without joint swelling , sometimes muscle pain and lymphopenia; and all dogs showed good response to corticosteroid treatment [5] . The possible link between different ANA patterns and various clinical signs for SLE was previously observed for a group of dogs including German Shepherds , NSDTRs , and several other breeds [8] . Of note , in our NSDTR sample set , skin lesions were present in the ANAH group , while muscle pain and fever were slightly more frequent in the ANAS group ( S9 Table ) . The onset of disease in the study cohort was at a median age of 3 years for ANAH dogs and less than 2 years for ANAS dogs . The MHC region has the strongest association to many autoimmune diseases in humans due to its utmost importance in the recognition of antigens; and it was also shown to be important for canine SLE [25] . Moreover , certain genotypes such as the HLA-DRB1*03:01 was found recently to be significantly associated with specific sub-phenotypes with anti-Ro/SSA and anti-La/SSB autoantibodies [43] . In dogs , we observed that different DLA genotypes and alleles were associated with either ANAH or ANAS , which may indicate the reactivity towards certain autoantigens produced more frequently in a particular ANA pattern . Among the seven genes associated with “all ANA” , WFDC3 , HOMER2 and VRK1 are clearly not associated with ANAS reactivity ( Table 2 ) . Moreover , for these genes the cumulative association with DLA , either general homozygosity for any DLA haplotype or the DQA1*00601 allele , plays a somewhat stronger role . The AP3B2 gene is associated with “all ANA”; and also has equally strong signals for two phenotypes , the speckled ANA group and SRMA , but no association with ANAH . Interestingly , the observed genetic segregation may favor a previously suggested hypothesis based on the differences in clinical manifestations that ANAS pattern could represent another SLE-related disorder , while the ANAH is more similar to human SLE [5] . The PTPN3 , WHAMM and BANK1 genes are associated with both ANA sub-phenotypes and thus could be considered as common genes . A function of WHAMM ( WAS protein homolog associated with actin , Golgi membranes and microtubules ) , a gene previously reported to participate in Golgi transport and membrane remodeling , and cytoskeleton formation by binding to microtubules and promoting actin polymerization [44] , has not been described before in immunity , while the other two common genes PTPN3 and BANK1 implicate major perturbations in both T and B cells . Human protein tyrosine phosphatase PTPH1 encoded by the PTPN3 gene inhibits T cell-activation by dephosphorylating the immune tyrosine-based activation motifs ( ITAM ) in the TCRζ chain that results in a downstream inhibition of NF-AT [45 , 46] . The observed substantial reduction of the PTPN3 mRNA levels in dogs carrying the risk haplotypes may cause a sustained activation of TCR signaling and lead to development of autoimmune disease . The BANK1 gene encoding the B-cell scaffold protein with ankyrin repeats was previously found associated with human SLE and other autoimmune diseases in distinct populations and ethnic groups [47–53] . The expression of the human BANK1 gene , similarly to what we found in IMRD dogs , is up-regulated in patients carrying the risk alleles [54] . This may suggest a common disease mechanism in the human and dog diseases . Whether or not and to what extent , the individual genes contribute to a particular ANA-staining pattern , and more generally , to specific clinical manifestations , and what interplay could be between the associated genes and their pathways , remains to be further studied . Also , while the identity of major autoantibodies present in the serum of human individuals with different ANA reactivity is already known [18] , it needs to be studied in more detail in dogs . SRMA , on the other hand , differs from IMRD by displaying predominantly neurological signs including pain , cervical rigidity , pyrexia and a polymorphonuclear pleocytosis of the cerebrospinal fluid ( CSF ) [10] . The disease usually occurs at a young age ( 4–19 months ) and is characterized by inflammation of leptomeninges and vasculitis of the leptomeningeal and mediastinal blood vessels , including arteritis of heart , thymus , and also vessels of the thyroid glands and muscles [55 , 56] . The disease can be treated with immunosuppressive doses of corticosteroids similarly to IMRD [14] . While the etiology is largely unknown , it has been proven that inflammatory processes in the CNS are not caused by viral or bacterial infection [14] . The strongest genetic association with SRMA detected in NSDTRs is located on chromosome 32 , followed by signals on chromosome 8 and 3 . Although there is no strong LD ( r2<0 . 8 ) between the genetically associated variants on chromosome 32 , the presence of multiple highly associated SNPs across the 1 . 3 Mb region and their association with gene expression levels suggests that the entire region including several genes is important for SRMA susceptibility . While many typed variants show correlation with gene expression , there is one tag SNP ( chr32:24 , 827 , 518 ) whose risk allele A is most strongly associated with increased expression of DAPP1 , LAMTOR3 , DDIT4L and PPP3CA . The distant cis-effect on expression of genes placed far away from each other may indicate a complex topology of the chromosomal locus with a possible locus control region ( s ) and common enhancers driving regulation of genes located on the opposite DNA strands . The previously reported signal on chromosome 8 [27] was corroborated in our study , but we found no correlation between the SRMA SNPs and expression levels of the only nearby gene VRK1 . Thereby , the identity of the gene affected in this locus in SRMA remains to be identified or the SNP could have its effect in a different tissue than PBMCs . Also , the progress in mapping non-coding genes including microRNA and lincRNA genes on the dog genome [57] may help to resolve this question in the future . The SRMA associated haplotype on chromosome 3 is correlated with increased expression of the AP3B2 gene . Interestingly , there is little overlap between the genes associated with SRMA and IMRD , suggesting the involvement of largely distinct pathways in these phenotypes ( Fig 8 ) . While most of the 11 genes are widely expressed , there is a certain emphasis on the predominant brain , muscle and immune cells expression of SRMA genes AP3B2 , PPP3CA , DDIT4L , DAPP1 and LAMTOR3 ( S7 Fig ) , consistent with the involvement of the central nervous system and muscles seen in this disease . Moreover , two genes , DDIT4L and PPP3CA , show relatively strong association signals in human neuromuscular inflammatory conditions ( S10 Table ) . Overall , for nine dog genes out of eleven , there is either evidence or at least a trend of association with various human autoimmune disorders , as can be viewed using the GRASP Search tool ( http://apps . nhlbi . nih . gov/Grasp/Search . aspx ) [58] . Without wishing to be bound by a theory , we hypothesize that primary inflammation triggered by as yet unknown environmental factors in SRMA-susceptible dogs is then maintained by the over-activated B cells producing high titers of IgA , often detected in the serum and cerebrospinal fluid ( CSF ) of SRMA patients [14 , 56] . The enhanced function of DAPP1 , PPP3CA and LAMTOR3 acting downstream of the B cell receptor via MAPK signaling pathway may be responsible for the altered B cell reactions . Moreover , the arteritis of the blood vessels may lead to ischemia in CNS or myocardial infarction through decreased nutrient and oxygen supply by damaged arteries and may further induce an already genetically modulated DDIT4L in neurons or cardiac myocytes , which in turn inhibit mTOR signaling and activate autophagy ( genes LAMTOR3 and AP3B2 code for adaptor proteins in the lysosome-endosome system ) and lead to apoptosis or necrosis [59] . It is tempting to speculate here that the enhanced levels of brain-specific expression of the SRMA-associated genes could be responsible also for hyperesthesia , an extreme pain sensitivity condition mainly exhibited by cervical , neck and spinal pain , and always seen in SRMA but not IMRD dogs [14] . Of note , out of four genes associated with ANAS IMRD only AP3B2 , the gene also shared with SRMA , shows the highest expression levels in the brain . In contrast , the ANAH genes showed a more diverse pattern of expression with the highest levels in the following tissues: blood ( BANK1 and VRK1 ) , skin and kidney ( WFDC3 ) , muscle and kidney ( HOMER2 ) , heart , kidney and liver ( PTPN3 ) , muscle/heart , liver and kidney ( WHAMM ) . Many cellular pathways are interconnected and therefore it is difficult to predict the exact biological effect , especially when several genes are altered , so the signaling output of each affected gene in all three phenotypes , ANAH , ANAS and SRMA , will depend on a particular tissue and cell context , the precise timing of expression and responsiveness to environmental inducers . Thus , for instance , the highest shift in expression levels associated with SRMA was a 2 . 5-fold up-regulation observed for the DDIT4L gene ( Fig 4H ) , DNA-damage-inducible transcript 4-like , known to be induced by a variety of stress factors , including hypoxia [60] . Although the precise role of the encoded protein remains largely unknown , the function of DDIT4L in autoimmunity may be related to the negative regulation of mTOR [61] . While the inhibition of mTOR promotes generation of CD8+ memory T cells [62] , at the same time it induces cell death by necrosis in the culture of U-937 monocytes [63] , which may trigger an auto-inflammatory response . In addition to the complexity of phenotypes relating to the different genes and genetic variants , the genomic architecture of almost all loci show a considerable complexity with SNPs showing long-range effects on gene regulation either alone or in conjunction with other SNPs . For example , the chromosome 3 top SNP ( 3:57 , 432 , 981 ) is a part of two different haplotypes present in different sub-phenotypes . On chromosome 32 , a haplotype associated with ANAS exerts a distant effect on a gene located in the other part of the locus , while SNPs located internally in the region affect four other genes in SRMA . It remains to be shown whether this type of complex regulatory structure will be a common finding in both canine and human disease , or an unusual event found here due to the potentially strong natural selection applied when only a handful of NSDTR dogs survived a canine distemper outbreak in the early 1900s [64] remains to be seen . In conclusion , we identified functional changes in eleven genes associated with different sub-phenotypes of canine IMRD resembling human SLE , and with SRMA which share clinical signs with a group of human vasulitides including Kawasaki disease , Henoch-Schönlein purpura and Behçet’s disease . Based on the gene functions and pattern of expression , we hypothesize how different genetic factors , sometimes located in the same genomic region , may lead to diverse clinical manifestations . The common genes and pathways may account for overlapping manifestations , whereas alterations in the functions of certain tissue-specific or even ubiquitous genes may be linked to specific clinical signs . The strikingly complex pattern of genomic regulation suggests that one should keep an open mind to multiple variants causing disease when studying GWAS regions both in canine and human diseases . The utility of dog breeds in identification of disease genes underlying human complex diseases is demonstrated by the identification of a well-known human SLE gene , BANK1 , and several novel genes . The novel genes warrant further study both in canine and human autoimmune disease .
This study was performed in strict accordance with the guidelines of the EU directive 2010/63 on the protection of animals used for scientific purposes . The protocols were approved by the regional Ethical board for experimental animals in Uppsala , Sweden ( Dnr C103/10 and C417/12 ) , and Animal Ethical Committee of County Administrative Board of Southern Finland ( ESAVI/6054/04 . 10 . 03/2012 ) . 250 Nova Scotia duck tolling retrievers ( NSDTRs ) were included in this study , 52 of them classified as ANA-positive IMRD cases , 66 as SRMA cases and 132 as healthy controls . All dogs included were privately owned and samples were collected during 2002–2013 . The dog pedigrees were verified to exclude closely related animals ( littermates ) from the analysis . Individual dog owners had consulted different veterinary clinics in Sweden and Finland . The inclusion criteria for IMRD ANA-positive dogs were musculoskeletal signs indicating a systemic rheumatic disorder , including stiffness mainly after rest , and pain from several joints of extremities . These signs had to be apparent for at least 14 days and were the main reason for the dog owner to visit the veterinary clinic . The examining veterinary physician suspected no other diseases in their diagnosis . All IMRD dogs should also display a positive IIF ANA test . Healthy controls were above seven years of age with no history of autoimmune disease . All study dogs were verified for relatedness and those related were excluded from the analysis . ANA tests were analyzed with indirect immunofluorescence at the University Animal Hospital , Swedish University of Agricultural Sciences ( SLU ) , Uppsala , Sweden using monolayers of HEp-2 cells fixed on glass slides ( Immuno Concepts ) . The glass slides were examined by fluorescence microscopy and considered positive at a titer of ≥1:100 . The visible nuclear fluorescence patterns could be divided into two groups; homogeneous ( ANAH ) or speckled ( ANAS ) patterns as previously described[8] . Genomic DNA was purified from 200 μl of blood using Qiagen QIAamp DNA Blood Mini Kit ( Qiagen ) according to the manufacturer’s protocol . DLA-DRB1 , -DQA1 and DQB1 exon 2 were amplified by PCR as previously described[25] . DNA sequencing was performed using capillary electrophoresis on an Applied Biosystems 3730xl . BigDye Terminator v3 . 1 ( Applied Biosystems ) Sequencing of the purified PCR products was made in one direction , reverse for DLA-DRB1 and-DQA1 and forward for DLA-DQB1 . Analysis of the nucleotide sequence was performed using MatchTools and MatchTools Navigator ( Applied Biosystems ) [25] . Statistical analyses were performed using VassarStats ( http://vassarstats . net/odds2x2 . html ) . Odds ratios and p-values for each allele , haplotype and genotype were calculated using a 2x2 contingency table . The total number of cases and controls carrying a specific allele or genotype was compared with the cases and controls not carrying it . The same comparison was made for alleles as well as genotypes for the ANA-positive cases with homogeneous or speckled pattern and the controls . The total numbers of homozygous dogs was also compared in cases and controls . To identify candidate variants , the five regions previously found associated with IMRD [27] , spanning approximately 5Mb , were re-sequenced in nine NSDTR individuals ( four ANA cases , two SRMA cases and three controls ) using 385K custom designed capture arrays from Roche NimbleGen and 400–600 X coverage Illumina sequencing . The sequencing data was aligned with BWA ( http://bio-bwa . sourceforge . net/ ) [65] and analyzed using SAMtools ( http://samtools . sourceforge . net/ ) [66] , BEDTools ( http://code . google . com/p/bedtools/ ) [67] , SEQscoring [33] ( http://www . seqscoring . org/ ) and other in-house tools to discover variants ( SNPs , indels and structural changes ) in the genomic sequence between IMRD , SRMA and healthy control dogs . A total of 13 , 084 potential SNPs were detected and of these , 426 SNPs were located within or close ( ±5 bp ) to a conserved element [39 , 68] . 2780 possible InDels were detected , among those , 88 occurred within or close to a conserved element ( ±5 bp ) . To identify structural variations , such as larger insertions , CNV or deletions , SEQscoring was used to calculate coverage differences between cases and controls . We did not identify any structural variants that differed between cases and controls . Sequencing data was deposited in European Nucleotide Archive ( ENA ) ( Study accession: PRJEB6494 , available at: http://www . ebi . ac . uk/ena/data/view/PRJEB6494 ) . 308 SNPs for five loci ( chromosome 3 , 8 , 11 , 24 and 32 ) were chosen from the re-sequencing data . SNPs were chosen based on the following criteria: difference in allele frequency in cases compared to controls , positioned in either protein coding regions , 5’ UTR or 3’ UTR and located within non-coding conserved elements . Conserved elements were identified using comparative sequence analysis based on the analysis of 29 mammals using SiPhy [39 , 68] . These SNPs were genotyped by GoldenGate Genotyping Assay . PLINK [69] ( http://pngu . mgh . harvard . edu/purcell/plink/ ) was used to analyze the markers with a MAF >0 . 05 and a call rate >0 . 75 . Total genotyping rate was 97% . Peripheral blood was drawn from 165 healthy NSDTR dogs directly in Tempus Blood RNA tubes ( Applied Biosystems ) and kept on ice during transportation . For isolation of total RNA from different dog tissues , fresh tissue samples from euthanized dogs were immediately immersed in the TRIZOL solution ( Invitrogen ) and RNA and DNA were purified according to the manufacture’s protocol . Total RNA from blood was purified using the Tempus Spin RNA Isolation Reagent kit ( Applied Biosystems ) according to the manufacturer’s instructions , and the quantity and the quality of RNA was assessed by NanoDrop ND-1000 spectrophotometer ( Thermo Scientific ) . In parallel , genomic DNA was purified for each sample and genotyped using pyrosequencing or direct Sanger sequencing with the primers shown in S11 Table . cDNA synthesis was performed in 20 μL at 42°C for 80 min using 2 μg of total RNA , 5 μM oligo-dT primer , MuLV transcriptase , RNase inhibitor in the buffer supplemented with 5 mM MgCl2 and 1 mM dNTPs . All reagents were purchased from Applied Biosystems . The reaction was terminated by heating for 5 min at 95°C and diluted to 25 ng/μl . Gene expression was measured by quantitative real-time PCR on 7900HT Sequence Detector ( Applied Biosystems ) with SDS 2 . 3 software using SYBR Green for signal detection . Gene-specific primers and annealing temperatures are shown in S12 Table in accordance with the guidelines for the minimum information for publication of quantitative real-time PCR experiments ( MIQE ) [70] . The regions for primers’ design were selected to target all known transcripts for a particular gene and thus allow to measure the total gene expression . In order to avoid amplification from genomic DNA , primers were located to cover either several exons separated by long introns or exon/exon junctions and further verified by BLAST search . The PCR conditions were optimized for each primer set prior to qPCR , and the specificity of amplification was verified by the post-PCR analysis of melting curves and agarose gel analysis . Initial denaturation at 95°C for 5 min was followed by 45 cycles ( 95°C for 15s , annealing at primer-specific Tm for 15s and 72°C for 25s ) . PCR buffer ( Invitrogen ) was supplemented with 1 . 5 mM MgCl2 , 200 μM of each dNTPs , 0 . 2 μM of each primer , SYBRGreen ( Molecular Probes ) , 15 ng of cDNA and 0 . 5 U of Platinum Taq polymerase ( Invitrogen ) . The reaction was carried out in 20 μL on a MicroAmp Optical 384-well reaction plate ( Applied Biosytems ) . Expression levels were normalized to the reference gene TBP using the comparative 2-ΔCt-method [71] . All experiments were run in triplicate . Correlation of gene expression with genotypes and haplotypes was performed using one-way ANOVA tests in PRISM 6 ( GraphPad Software ) . The dog 550 bp genomic fragment containing SNP chr8:68 , 708 , 503 was amplified by PCR and cloned in front of the minimal promoter in the pGL4 . 26 reporter vector ( Promega ) . After sequence validation , the plasmids were purified with EndoFree Plasmid Maxi Kit ( Qiagen ) . The transfection of K562 cells was performed in the 24-well plates as follows: 7x105 cells/well were seeded 24 hours before transfection in the RPMI-1640 medium supplemented with L-glutamine and 10% of heat-inactivated bovine serum . 750 ng of the reporter plasmid and 50 ng of the pRL-TK ( Promega ) normalization vector were transfected into each well by Lipofectamine 2000 ( Invitrogen ) according to the manufacture’s protocol . Twenty-four hours after transfection , cells were additionally stimulated with 20 ng/ml of PMA for 10 hours , then harvested and assayed for the Firefly and Renilla luciferase activities with the Dual-Luciferase Reporter Assay System ( Promega ) . The experiment was repeated three times with four technical replicates for each plasmid . | Autoimmune disorders display complex phenotypes with clinically diverse manifestations , which together with complex genetic inheritance and environmental factors triggering the disease may complicate the diagnosis and investigation of the disease mechanism . The use of dog breeds may facilitate the analysis of genetic factors based on genetic homogeneity within a breed . We performed genetic analysis of two diseases common in dogs , immune-mediated rheumatic disease ( IMRD ) and steroid-responsive meningitis-arteritis ( SRMA ) that are similar to human SLE and a group of vasulitides such as Kawasaki disease , Henoch-Schönlein purpura and Behçet’s disease , correspondingly . We identified eleven genes along with specific alleles and genotypes for the major histocompatibility complex II involved in susceptibility , and studied their expression . The genes shared between the two diseases may be involved in the common immune signaling pathways and hence account for the common clinical signs , whereas the phenotype-specific genes may be implicated in particular pathways active in certain tissues and organs , and thereby may be responsible for characteristic manifestations seen only in one of the diseases . Further , the similarity between human and dog SLE at the genetic and functional levels demonstrated by the association of the BANK1 gene in both species indicates the common cross-species mechanisms of autoimmunity and may help identification of novel disease genes and pathways . | [
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] | [] | 2015 | Multiple Changes of Gene Expression and Function Reveal Genomic and Phenotypic Complexity in SLE-like Disease |
Human angiostrongyliasis ( HA ) is a neurological helminthic disease caused by the lung worm Angiostrongylus cantonensis . It is suspected in the combination of travel or a residence in an endemic area and eosinophilic meningitis . In Mayotte , an island in the Indian Ocean , cases are rare but regular . The main objective of our study was to describe the epidemiological and diagnosis clues of HA in Mayotte . The secondary objectives were to evaluate the contribution of Real-Time Polymerase Chain Reaction ( RT- PCR ) for the diagnosis of HA , delineate the characteristics of the local transmission and ascertain the presence of A . cantonensis in Achatina fulica , the potential vector of the disease . Between 2007 and 2012 , all cases of eosinophilic meningitis were retrospectively included and investigated by RT- PCR in the CSF . Descriptive analysis was conducted for clinical , biological and radiological features , and were analyzed for all patients together with the search for prognostic factors for mortality . Concurrently , geolocalization and temporal parameters were studied to correlate the occurrence of the cases with rainfall seasons and snails were collected to enhance a parasitic carriage with real time PCR . During the 6-year period of the study , 14 cases were identified ( 2 . 3 cases/year ) and 9 among 10 remaining CSF were positive in PCR . Among 14 cases of EM , 13 were less than 2 year-old children . The 1 year mortality rate was 5/14 ( 35 . 7% ) . Among survivors , 3/7 ( 42 . 8% ) presented neurological sequelae . Factors associated with mortality were dysfunction of cranial nerves , abnormal brain imaging , and CSF glucose level inferior to 2 mmol/l . Occurrence of cases was temporarily and spatially correlated to the rainy season . Among the 64 collected giant snails , 6 ( 9 . 4% ) were positive with A . cantonensis PCR . The likely main route of transmission was the children licking snails , carriers of the parasite . In Mayotte , HA was mainly found in paediatric cases under 2 years old , and evidenced a life-threatening disease . PCR seems to be a promising tool in the definitive diagnosis of HA . Population should be aware of the role of A . fulica , and not let the children have direct contact with the snails .
Human angiostrongyliasis ( HA ) , also called nervous angiostrongyliasis , is a parasitic disease due to the lungworm A . cantonensis . A . cantonensis has been described for the first time in a rat in China , and the first human case has been reported in Taiwan in 1945 [1 , 2] . The disease has been described progressively worldwide but is mainly endemic in China , South-eastern Asia and in the Pacific Ocean islands where outbreaks or sporadic cases have been reported [3] . Elsewhere cases are generally reported in travelers returning from endemic areas and sporadic autochthonous cases such as in the Pacific Islands [4] , Brazil [5] and Jamaica [6] . The disease is known in the Indian Ocean area as well and A . cantonensis has been found in the snail and the rat on La Reunion Island , Maurice and Madagascar [7 , 8] . Furthermore , several non confirmed human cases have been reported since 1977 on La Reunion Island [9 , 10] . On Mayotte Island , the disease was first reported in 1996 in a 11-month old child [11] . Eight supplementary probable cases have reported until 2006 , among which 5 children and 3 adults , all with a critical outcome [12 , 13] . The sources of transmission vary according to the geographical area , but are generally related to the consumption of raw or undercooked intermediate host such as slugs and snails or paratenic host referring to shrimps or crabs [14] . Some clusters have also been linked to raw vegetables contaminated with slug or snail slime [15] . Following transmission , HA pathogenesis refers to an aberrant route with a tropism to the central nervous system . Thus , the most common presentation of HA is an eosinophilic meningitis frequently accompanied with encephalitis signs , epilepsy and cranial nerves disorders [14] . The disease generally occurs in adults , due to the food-linked source of contamination , although pediatric cases have been reported [16] . Most cases are described to be mild and self-limited , even in children , although some fatal issues have been evidenced [3 , 14] . We aimed to study the specificities of the local parasitic life cycle . For this purpose , we used Real time PCR ( RT-PCR ) for diagnosis of human A . cantonensis infection a new tool published in 2010 [17] . We aimed to 1 ) evaluate the incidence of EM between 2007 and 2012 ( suspected cases of HA ) , 2 ) describe their individual clinical , biological , imaging characteristics , 3 ) determinate the variables associated to mortality , 4 ) evaluate the performance of A . cantonensis PCR in CSF for accurate diagnosis of HA , 5 ) ascertain the contextual and environmental variables , and 6 ) conduct an ancillary entomological analysis with a the use of the PCR to enhance the carriage of the parasite by Achatina fulica .
Mayotte is a French island of the Comoros archipelago , in the South-West Indian Ocean , where 217 . 000 inhabitants lived in 2012 and 50% of the population is under 17 . 5 years old ( source: http://www . insee . fr ) . The island enjoys a tropical maritime climate . There are two seasons with a hot and wet rainy season flowing in from November to April with abundant precipitations and a dry season from May to October . In Mayotte , the vectors of transmission remain unclear , although the African giant snail , A . fulica , has been incriminated , like in the other places of Indian Ocean . A cross-sectional study was conducted in the hospital of Mamoudzou , which is the main hospital of the island , and where all severe inpatients refer . EM is systematically followed-up among patients with CSF analysis since 2007 . All the patients admitted for EM in any department of the hospital from January 2007 to December 2012 were thus longitudinally identified . As usually admitted in the literature , we define presumptive cases for patients who had clinical and biological criteria corresponding to the diagnosis of HA , probable cases corresponded to those who had a positive serology , and confirmed cases were those whom A . cantonensis was isolated in the CSF , by direct microscopy or with PCR . Clinical and biological criteria were based on the association of any neurological symptom with EM , defined by the presence of more than 10 eosinophil per millimeter cubic in the cerebrospinal fluid ( CSF ) , or ≥ 10% of the total CSF leukocyte count . Patients with a false eosinophilia in the CSF due to a traumatic lumbar puncture , with blood eosinophilia from another etiology were excluded . The following variables were collected: epidemiological data ( age , gender , place of birth , place to live ) , risk factor for transmission with a Shimaore ( Comorian language of Mayotte ) translator , such as the knowledge of the contact with a mollusk , medical history , clinical presentation , biological results ( including blood and CSF ) , imaging features ( brain scan or Magnetic Resonance Imaging ( MRI ) ) , treatments , date of last contact , and outcome at 1 month and 1 year after discharge ( referred as dead , alive , with neurological sequelae or healthy ) . The median and interquartile ranges were used for most of the continuous variables . Some of biological variables were categorized following the laboratory cut-off values or using the median and they were dichotomized because of the small sample size . Association between variables and 1-year mortality was obtained comparing alive and deceased patients’ variables with Fisher’s exact test for categorical variables and with Mann-Whitney Test for continual variables . All these data were anonymized in a standardized case report form and entered in the database . Data were analyzed with Stata IC 12 . 0 , version 2 . 15 . 3 . The variables were anonymously and retrospectively collected in the medical charts . As far as ethical considerations are taken into account , the French National Commission on Informatics and Liberties authorizes the retrospective use of anonymous patient files on the site of patient care in a single hospital .
Fourteen patients with a diagnosis of EM were identified during the study period of 6 years from 2007 to 2013 , e . g . an estimated incidence of 2 . 3 cases per year , and 1 case/year/100 . 000 inhabitants on the island . Ten were considered as presumptive and 4 probable according to the previous definition . Among them , 10 CSF samples were remained , and 9 of them ( sensitivity 90% ) were positive with real time PCR while 4/10 patients ( 40% ) had a positive serodiagnosis in serum . PCR was negative for all CSF controls ( Fig 1 ) . Amplification curves of six patients positive by PCR and the positive control are represented on Fig 1 . Sequencing of the nine individual conventional PCR amplicons ( obtained from the 9 positive CSF ) was done ( Fig 2 ) . The 105 pb obtained consensus sequence ( TGCGCCCATTGAAACATTATACTTGGGTCATTAAGATTTCCTGTCAATCAGGTGTCACATGCGTATAGTAGATATGCGATGATACTATCAGTTCGCCATCCATGA ) was strictly identical between the nine patients . BLASTn homology search against a non redundant nucleotide ( nt ) NCBI database for this consensus sequence showed 100% identity with A . cantonensis 18S ribosomal RNA gene , internal transcribed spacer 1 , confirming the specificity of real-time PCR results . All 14 patients included in our series were children; thus 10 ( 71 . 4% ) were under 12 month old and 13 ( 92 . 9% ) were under 24 month old ( Tables 1 and 2 ) . Eleven ( 78 . 5% ) were male , and 13 ( 92 . 9% ) were born in Mayotte . At admission , 11/14 children ( 71 . 4% ) had fever , and 9 digestive symptoms ( abdominal pain , vomiting and/or diarrhea ) . Besides the 14-year-old previously disabled , all children presented with acute neurological symptoms ( 13/14 ) : encephalitis signs ( n = 9 ) , dysfunction of cranial nerves ( n = 6 ) , seizures ( n = 4 ) , axial hypotonia ( n = 3 ) , neck stiffness ( n = 2 ) , headaches ( n = 2 ) . Brain imaging was performed in 12 of them ( 10 CT-scan , 1 MRI , 1 CT-scan + MRI ) : seven had a normal brain imaging and 6 presented abnormalities with cerebral atrophy and abnormal enlargement of cerebral ventricles for 5 of them . Blood eosinophilia was constant with a median of 2400/mm3 , with an eosinophilia level rising above 1000/mm3 in 13 ( 92 . 9% ) patients ( Table 1 ) . Median eosinophilia in CSF was 194 ( 48% ) , with a range of 35–690/mm3 ( 12–76% ) . Other CSF analysis found a moderate protein level elevation in CSF and normal or low glucose level . A contact with mollusks was reported by the parents in 5/11 cases ( 41 . 7% ) , 4 with AGS , and 1 with a non identified slug . The lethality rate was high and accounted at 14 . 3% ( 2/14 ) at 1 month after admission and 35 . 7% ( 5/14 ) at 1 year of follow-up . Among the 9 children still alive after one year , the neurological , state couldn’t be evaluated in two of them , and 3/7 ( 42 . 8% ) presented neurological sequelae . No difference for lethality was evidenced for age , gender , blood eosinophilia , eosinophilia and protein levels in CSF and treatment used between alive and deceased children at one year ( Table 3 ) . There was a significative association between dysfunction of cranial nerves ( p = 0 . 001 ) , abnormal brain imaging ( p = 0 . 04 ) and a CSF glucose level inferior to 2 mmol/L ( p = 0 . 04 ) and the mortality at one year . A history of clinical manifestations over 7 days before admission , and eosinophilia superior to 50% of CSF cell count tended to be more frequent in deceased children but not significantly . Spatial localization highlighted that the places of residence of all cases were located on the northern part of the island . Of note , this area is the rainiest part of Mayotte ( Fig 3 ) . Date of occurrence of the cases and rainfall precipitations curves were superimposed on the same sketch arguing for a putative association between rainfall and transmission . Consistently , most cases were evidenced during the rainy season ( Fig 4 ) . Among all the snails collected in the different areas of the island , 6/64 ( 9 . 4% ) ( from 0 to 2/8 samples , depending on the village ) were positive by A . cantonensis PCR for , with a likely high parasitic load ( Table 4 ) .
On Mayotte , a small island located in the South-West part of Indian Ocean , HA cases occur mainly in very young children: our study reports 14 cases , among which 13 were children beneath 2 years old . The remaining patient was a 14-year-old disabled child suffering of sequelae of bacterial meningitis in the childhood . He was regularly playing with snails , lying on the grass all the day . Whether the incidence is very low ( 2 . 3 cases per year , e . g . approximatively , 1 case/100 , 000 inhabitants/year ) , the rate of morbidity and lethality is very high with a 1-year mortality of 38 . 5% and incapacitation rate of 37 . 5% among the remaining 8 children . Some case reports have already been published on A . cantonensis infection in Mayotte [11 , 12 , 21] . Most of these reported cases presented severe clinical pictures in infants , although some cases in adults have been described [13] . Thus , human angiostrongyliasis seems to have very high morbidity and mortality rates , never described in the medical literature , according to our knowledge . Such a severity in children has never been reported before . It is considered that most cases of human angiostrongyliasis are generally mild and self-limiting , even if death can occur in severe cases [3] . Indeed , the first pediatric case-series performed in Taiwan and published in 1991 reported a mortality rate of 4 . 9% among 82 children [16] . Of note , children included in the latter study were older than those for our study: 58 . 5% < 6 yo and 80% < 9 yo . This main issue might account for the critical issue evidenced in our series and the age of the patients has already been evocated as a possible explanation [22] . A series of 19 cases of eosinophilic meningitis due to A . cantonensis from Thailand was published in 2013 [19] . No death was reported in this publication . In this series fever and digestive signs were as frequent as in ours , but headache ( 14 . 9 vs . 100% ) and neck stiffness was more frequent . On the contrary , severe clinical presentations was wore frequent in our child patients than in the Thailand’s ones: dysfunction of cranial nerves ( 42 . 9 vs . 31 . 6% ) , encephalitis signs ( 57 . 1 vs . 0% ) . In this series , the main route of transmission was likely the consumption of raw freshwater snails while the children in Mayotte are contaminated to the contact of A . fulica . In another publication from Thailand comparing encephalitis vs . meningitis cases ( 14 vs . 80 cases respectively ) , death was strongly linked to encephalitis ( 79 vs . 0% ) [23] . In another Taiwanese study , with 37 Taiwanese patients diagnosed over an 18-year period ( two were children ) , neurological sequelae developed in only one 2-year-old child . Authors evocated that a higher worm load is relative to body size would explain the severity of the disease [24] . In our series , encephalitis signs were frequent and may explain the high mortality . The main hypothesis to explain the frequency of the encephalitis may be the high parasitic load in the snails and also the low age of the children , with a quicker progression to central nervous system infection . Recently a study was published reporting few severe cases in children in Jamaica [20] . Nevertheless , in our study , there was no difference in terms of age between alive and deceased children , even if they were all very young . Impact for age was found according to outcome . Despite the small size of our sample , some variables were associated with a higher risk of 1-year mortality such as abnormal brain imaging was associated , dysfunction of cranial nerves , although it was considered as an usual symptom in previous large series [22] and a low CSF glucose level . This finding might reflect a higher parasitic load in the CSF and an stronger immunological response , both biological factors linked to severity of diseases . Our study evidenced for the first time that Mayotte’s A . fulica was carrier of A . cantonensis . Nevertheless , the rate of carriage was quite low ( 9 . 4% ) in comparison to other places such as Hawaii ( 72 . 6% ) , São Gonçalo , a metropolitan area of Rio de Janeiro , Brazil ( 78 . 7% ) , Miami , Florida ( 36% ) , but closer to China’s investigations ( 13 . 4% ) [25–28] . Nevertheless , parasitic load reflected by real PCR results were very high , so both could explain at the same time the low incidence of the disease in our island , but the severity of the clinical picture . At last , no treatment either corticosteroid or antiparasitic therapy demonstrated a benefit on the outcome of the disease , especially in case of encephalitis and comatose [14 , 29] . In the present study , deceased children received more often corticosteroids than survivors . This observation might account for intensive suppletive cares related to severe presentation . The main hypothesis for the contamination with A . cantonensis in Mayotte is the contact of the children with A . fulica . Thus , very few cases have been described in adults , and the route of transmission was not known [13] . Thus , the route of transmission of human angiostrongyliasis remains unclear in adults in Mayotte because people from the whole Comoros archipelago don’t eat snails , slugs , nor shells and any food is very well cooked . The infections could be linked to the consumption of raw vegetables contaminated with infected gastropod’s slime . On the contrary , the route of transmission is clearer in infants who are directly in contact with the host omnipresent in the environment on the island , and they lick snails and slugs or their hands contaminated with it . This also explains why cases occur during the rainy season ( Fig 4 ) and the wettest part of the island ( Fig 3 ) , as Achatina fulica aestivates during dry season . Thus , the transmission cycle is completely different in Thailand where it mainly affects adults , by consuming raw foods , and where transmission is throughout the year and is more similar to transmission cycle as described in Taiwan [16] . Indeed , almost 10% of the local AGS were proven to be carriers of the parasite in Mayotte using the real time PCR . A . fulica is recognized to be one of the main vector of the disease in many settings [3] and it has already been evidenced in other islands of the Indian Ocean such as La Réunion and Madagascar [7] . We were able to find a close contact between children and snails in only 4/11 cases , nonetheless , reports might be limited by declarative or memory bias , due to the retrospective settings of the study . Furthermore , one mother reported that her son played with an unidentified slug rather than with snails , which might evoke that AGS is not the only vector on the Island . Many other mollusks host have been described to be infected by the parasite , like in Brazil [30] . Direct contact with A . fulica , as a route of transmission has been barely reported among adult patients [3 , 14] . Distinctly , Hwang et al , in the pediatric study published in 1991 reported an history of contact between A . fulica and the patients in more than 80% of the cases [16] . This mode of transmission may partly explain the specific epidemiological features of Mayotte , with mainly pediatric cases compared to the great endemic area , such as Southeastern Asian countries , where adults are the most infected . In those countries , the main route of transmission is the consumption by adults of the raw or undercooked host , such as shellfish or snails [14] . The ingestion of vegetables contaminated with snail slime is also often reported , such as during a cluster in travelers returning from Jamaica [6 , 31] . Yet , in the Comoros archipelago , giant African snails are not commonly eaten , unlike in some sub-Saharan continental African countries , such as Nigeria [32] , as well as fresh water shellfish . Adult cases have been rarely reported in Mayotte: only three before our study [13] , but the source of transmission was not clearly identified for these patients to the best of our knowledge . As previously stated , it would be very difficult to propose environmental preventive measures . Indeed , A . fulica , initially native to East Africa [33] , is widely spread on the island [34] , and a minority ( <10% ) seems to be infected . A . fulica is listed as one of the top 100 worldwide introduced invasive species ( 100 of the Worst Invasive Species . Global Invasive Species Database . URL: http://www . issg . org/database/species/search . asp ? st=100ss ) , so that may encourage people to try to exterminate it , as people consider it as a recent invasive species on Mayotte . Nevertheless , archaeological studies estimated that this snail would be present on the island since at least the 8th century of our era [35] . Thus , the most important preventive measures are individual and consist in advertise the population to avoid the contact with snails and slugs , and not to let their children play with them , especially during the rainy seasons . Indeed , we found that most cases occur during this period , particularly suitable for A . fulica , and in the wettest part of the island . It has been previously demonstrated that AGS are rare during the dry season as they aestivate because of the hot dry weather and they bury themselves in the soil or hide beneath stones in order to avoid exposure to direct solar radiation [36] . A . cantonensis real time PCR was an essential tool to complete the study . It demonstrated a very sensitivity ( 90% ) for the retrospective definite diagnosis of HA , and with a specificity of 100% . On the contrary , the sensitivity of serodiagnosis was very low ( 60% ) , and helminths serodiagnosis , especially for A . cantonensis , induce many false positive due to cross reaction with other helminths , such as Strongyloides stercoralis . Furthermore , no laboratory performs this test it in France anymore . There are few publications about the use of real time PCR for human cases diagnosis . Since the first publication of the biological method in 2010 [17] , it has been mainly used for the identification of the vector , especially in Hawaii , USA and Brazil [25 , 37 , 38] . Some publications reported the use of real time PCR for the diagnosis of human angiostrongyliasis , and not always successfully , in Hawaii and in Brazil [39 , 40] . We present here the most important positive case series , and demonstrate the great interest of this tool for the confirmatory diagnosis . This skill is especially of interest if we acknowledge that there are yet reliable PCR testing for the diagnosis of neurological helminthiasis , such as cysticercosis , toxocariasis , schistosomiasis , paragonimiasis , gnathostomiasis and baylisascariasis , and which are the differential diagnosis of nervous HA of eosinophilic meningitis [41 , 42] . Nevertheless , the ideal control group for PCR would have been CSF of gnathostomiasis , which is the main differential diagnosis of HA . This control would have given very high diagnostic properties . Unfortunately we were not able to find this control , because , gnathostomiasis has never been reported on Mayotte , and other causes of eosinophilic meningitis are scarce . Thus , the high diagnostic properties of PCR in this study may be due to inappropriate control group . In conclusion , in Mayotte , HA mainly affects infant under 2 years and is a highly life-threatening disease . Real time- PCR seems to be a powerful and promising tool in the definitive diagnosis of HA . Whether eradication of the vector is illusory , the population should be aware of the risk of direct contact with the African giant snail , and try to educate the children to avoid it . In endemic areas , every physician may suspect the disease in front of any febrile neurological pictures , and a lumbar puncture should be performed as soon as possible to realize the PCR on the CSF as well as brain imaging . | Human angiostrongyliasis is a neurological helminthic disease caused by the lung worm Angiostrongylus cantonensis , and most cases are reported from Asia , particularly Thailand and China . In Mayotte , an overseas French Territory in the Indian Ocean , cases are rare but regular , with life-threatening clinical pictures in very young children , though the medical literature describes this disease as mild , even in children . We aimed to study this disease in the specific context of the Indian Ocean . We found that , although this disease is quite rare: 14 cases in a study period of 6 years , with evidence among children , and mainly ( 13/14 ) those less than 2 years old , and its prognosis is poor as the 1-year mortality rate was 35 . 7% , and neurological sequelae 42 . 7% of the survivors . The real time PCR performed on the CSF was a sensitive tool . The occurrence of cases was temporarily and geographically linked to rain , and 9 . 4% of the collected Achatina fulica , the giant African snails , were positive for the parasite with high parasitic loads . The specific epidemiology is linked to the bad habit of the young children to lick the snails , which are present everywhere on the island , and have a severe presentation due to the high parasitic load . | [
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] | 2016 | Angiostrongylus cantonensis Infection on Mayotte Island, Indian Ocean, 2007-2012 |
During Yersinia pseudotuberculosis infection of C57BL/6 mice , an exceptionally large CD8+ T cell response to a protective epitope in the type III secretion system effector YopE is produced . At the peak of the response , up to 50% of splenic CD8+ T cells recognize the epitope YopE69-77 . The features of the interaction between pathogen and host that result in this large CD8+ T cell response are unknown . Here , we used Y . pseudotuberculosis strains defective for production , secretion and/or translocation of YopE to infect wild-type or mutant mice deficient in specific dendritic cells ( DCs ) . Bacterial colonization of organs and translocation of YopE into spleen cells was measured , and flow cytometry and tetramer staining were used to characterize the cellular immune response . We show that the splenic YopE69-77-specific CD8+ T cells generated during the large response are polyclonal and are produced by a “translocation-dependent” pathway that requires injection of YopE into host cell cytosol . Additionally , a smaller YopE69-77-specific CD8+ T cell response ( ~10% of the large expansion ) can be generated in a “translocation-independent” pathway in which CD8α+ DCs cross present secreted YopE . CCR2-expressing inflammatory DCs were required for the large YopE69-77-specific CD8+ T cell expansion because this response was significantly reduced in Ccr2-/- mice , YopE was translocated into inflammatory DCs in vivo , inflammatory DCs purified from infected spleens activated YopE69-77-specific CD8+ T cells ex vivo and promoted the expansion of YopE69-77-specific CD8+ T cells in infected Ccr2-/- mice after adoptive transfer . A requirement for inflammatory DCs in producing a protective CD8+ T cell response to a bacterial antigen has not previously been demonstrated . Therefore , the production of YopE69-77-specific CD8+ T cells by inflammatory DCs that are injected with YopE during Y . pseudotuberculosis infection represents a novel mechanism for generating a massive and protective adaptive immune response .
Dendritic cells ( DCs ) play a major role in protective immunity against pathogens . For example , DCs are required to prime naïve antigen specific CD8+ T cells to become effector cells that secrete cytokines and/or are cytolytic [1 , 2] . When DCs acquire endogenous antigens , e . g . , viral polypeptides synthesized intracellularly , the antigens are processed through a classical pathway . In this case , antigenic proteins are first degraded by the proteasome , then the peptide products are transported from cytosol through the endoplasmic reticulum to load onto MHC class I molecules and finally transported to the cell surface for presentation to CD8+ T cells [3] . In addition , when DCs are not directly infected , they can acquire exogenous antigens , e . g . from extracellular infectious agents , or antigens associated with other types of cells , and present them to CD8+ T cells by a mechanism known as cross-presentation . The two main intracellular pathways for cross-presentation are generally referred to as the cytosolic pathway , where the antigen is internalized and gains access to the cytosol , and the vacuolar pathway , where antigen processing and loading occurs in endocytic compartments [4] . DCs are a heterogeneous population of professional antigen presenting cells . They differ in hematological origin , migration pathway , surface marker expression and functional properties [5] . Originally DCs were identified to bear the surface marker CD11c [6] . Currently , common features of all DCs are still somewhat obscure but in general include a probing dendritic morphology , high amount of surface MHC class II molecules and T cell-stimulating activity [7] . At steady state , plasmacytoid DCs and conventional DCs are the main types . In mice , conventional DCs include lymphoid organ-resident and migratory subpopulations . The resident murine DCs can be further divided into CD8α+CD11blow and CD8α-CD11b+ cells , while the migratory DCs can be separated into CD103+CD11blow and CD103-CD11b+ cells . The CD8α+ and the CD103+ DCs are more efficient at cross-presentation in vivo and they are developmentally related . Deficiency in transcription factor Batf3 in mice results in the diminishment of both subpopulations of DCs [8 , 9] . During infection or tissue injury , another type of DC , inflammatory DC ( infDC ) , may emerge in the inflamed tissues ( reviewed in [5 , 10] ) . In mice , infDCs were initially identified as MHC II+ CD11b+ CD11c+ F4/80+ Ly6C+ [11] . However , these , as well as other markers later identified , are also expressed on other types of myeloid cells such as macrophages and monocytes . Therefore , a functional assay demonstrating the ability to activate T cells is normally required to definitively identify infDCs . When their cellular origin was investigated , infDCs were found to differentiate from CCR2-expressing inflammatory monocytes that are characterized as CD11b+Ly6Chi . These cells are recruited to sites of inflammation and in a process that requires GM-CSF , and potentially other factors , differentiate into infDCs [5] . However , depending on the model system under investigation , diverse functions have been assigned to the CCR2-expressing CD11b+Ly6Chi cells or their differentiation products . These functions include control of microbes , immuno-pathology , immuno-stimulation and immuno-suppression [12 , 13] . In bacterial infection , the CCR2-expressing CD11b+Ly6Chi cells ( and their differentiation products ) are generally required to control infection by direct killing of bacteria [13] . During Listeria monocytogenes infection , the CCR2-expressing Ly6Chi cells produced large amounts of TNFα and iNOS , and were hence termed Tip-DC [14] . Even though TipDCs were able to stimulate alloreactive T cells in vitro , they were not required to induce an LLO91-99 specific CD8+ T cell response in mice infected with L . monocytogenes . In fact , the CD8+ T cell responses to the LLO91-99 epitope in the spleens of Ccr2-/- mice infected with L . monocytogenes were larger than that observed in the corresponding spleens of wild type mice [14] . This could be due to the T cell-suppressive effect of the nitric oxide ( NO ) produced from these cells . In our previous study using a mouse model of Salmonella enterica serovar Typhimurium infection , recruitment of CD11b+Ly6Chi cells to infected spleens also depended on CCR2 , and these cells remained in an immature state in vivo , but could be differentiated further in vitro to express higher levels of MHC II and F4/80 . Furthermore , these immature CD11b+Ly6Chi cells also inhibited both CD4+ and CD8+ T cell proliferation via a NO-dependent mechanism in vitro [15] . Therefore , during bacterial infection of mice , CCR2-expressing CD11b+Ly6Chi cells can acquire DC-like characteristics and have direct antimicrobial activity , but it is unclear if these cells can differentiate into infDC and prime or activate CD8+ T cell responses to microbial antigens . A number of Gram-negative bacterial pathogens utilize type III secretion systems ( T3SS ) to inject effector proteins directly into the cytosol of infected host cells in order to overcome barriers to infection or counteract innate immune responses [16] . A well-studied T3SS that is required for virulence is encoded on a plasmid ( pYV ) in the enteric pathogen Yersinia pseudotuberculosis . From the pathogen’s viewpoint an unintended consequence of the T3SS process is that translocated effector proteins can serve as antigen for presentation by the classical class I pathway [17] . We recently showed that during primary infection of C57BL/6 mice with Y . pseudotuberculosis , an exceptionally large CD8+ T cell response is induced against the T3SS effector YopE . We consider this response as exceptionally large , because at the peak of the response , up to 50% of total CD8+ T cells in spleens are specific for H2-Kb class I MHC-restricted epitope YopE69-77 [18] . In comparison , during primary L . monocytogenes infection in mice , only 2–3% of splenic CD8+ T cells are specific for LLO91-99 at the peak of response [19] , and even at the peak of a recall response , only ~17% of all CD8+ T cells in the spleen recognize LLO91–99 [20] . Following intragastric infection of C57BL/6 mice with Y . pseudotuberculosis a large YopE69-77-specific CD8+ T ( ET ) cell response is also detected in intestinal epithelia , lamina propria , and mesenteric lymph nodes [18 , 21] . ET cells elicited by vaccination with YopE69-77 peptide can protect against Y . pseudotuberculosis and the related pathogen Yersinia pestis through secretion of the cytokines TNFα and IFNγ [18 , 22 , 23] . The epitope YopE69-77 is located in the N-terminal chaperone-binding ( Cb ) domain of the effector . The C-terminal half of YopE contains the GTPase-activating protein ( GAP ) activity that is important for Yersinia virulence . GAP catalytic activity , as well as other important molecular characteristics of YopE , including its ability to localize to membranes or to undergo ubiquitination , is not required for the large ET cell response [24] . Factors important for the large ET cell response on the host side of the interaction are unknown , however we did observe that the number of ET cells positively correlated with the number of CD11b+ cells in the spleens of Y . pseudotuberculosis-infected mice [24] . Given the unprecedented magnitude of the ET cell expansion in Y . pseudotuberculosis-infected mice , it is important to further clarify the bacterial and host factors that are important determinants of this immune response . Here we show that production of the large ET cell response depends on T3SS-mediated translocation of YopE as well as infDCs , whose recruitment from bone marrow requires CCR2 . In addition , in the absence of the large response to translocated YopE , we detected a compensatory adaptive immune mechanism in which secreted YopE appears to be cross-presented by CD8α+ DCs .
The exceptionally large ET cell response in mice infected with Y . pseudotuberculosis is similar to the magnitude of CD4+ T cell responses to superantigens . Superantigens typically induce a CD4+ T cell response that is limited in diversity with respect to the Vβ usage in αβ T-cell receptors ( TCRs ) . To determine the clonal nature of the ET cells produced during Y . pseudotuberculosis infection of C57BL/6 mice , the Vβ repertoire of these cells was investigated . The genes of functional TCRs are assembled from separate V , D , J region segments through recombination ( reviewed in [25] ) . Mice and humans carry about 20–70 germline V segments that encode about 90 amino acid residues of the mature TCR . Therefore , diversity in Vβ composition demonstrates a polyclonal nature of a T cell population , however , T cells containing the same Vβ regions are further diversified through the addition of D and/or J segments and imprecise joining . To obtain uniform infections we used intravenous ( IV ) challenges , and because it is difficult to achieve sublethal infections via this route with the wild-type bacteria , our experiments were done with the attenuated Y . pseudotuberculosis strain 32777 encoding catalytically inactive YopER144A ( mE , Table 1 ) . C57BL/6 mice were infected IV with mE , and the Vβ composition of the ET cells in spleens was determined using a panel of fluorophore-conjugated antibodies recognizing different Vβ regions in conjunction with tetramer staining and flow cytometry ( Fig 1A ) . Results obtained with an uninfected mouse analyzed in parallel as a control are shown in S1 Fig Seven days post infection ( dpi ) , when the number and/or percentage of ET cells were still increasing; or one year after infection , when only ~2% of total splenic CD8+ T cells were specific for YopE69-77 , the most prominent population was composed of Vβ8 . 1 and 8 . 2 , with an average of 25% of all ET cells in this category ( Fig 1A , left , and 1B ) . The 2nd largest population , however , differed between individual mice ( Fig 1B ) . Overall , all of the Vβ subsets tested were represented within the ET cell population in all the mice examined , ranging in average composition from 3% to 25% among all the ET cells ( Fig 1B ) . These results revealed that the Vβ usage in the ET cell composition is polyclonal and highly diverse , indicating that an antigen-presentation process , rather than a superantigen-like mechanism , is responsible for production of these cells . To begin to identify bacterial and host factors required for the large ET cell response , we first focused on the pathogen and investigated whether secretion and translocation of YopE by the T3SS is required for induction of ET cells during infection . For this purpose , deletion mutations were introduced into Y . pseudotuberculosis 32777 to inactivate yscF or yopB ( Table 1 ) . The yscF mutant ( ΔYscF ) lacks the T3SS needle and is unable to secrete or translocate YopE . Deletion of yopB in 32777 or mE resulted in strains defective for translocation of YopE or YopER144A into host cell cytosol ( ΔB and ΔBmE , respectively , Table 1 ) . It is important to note that YopB is not required for effector secretion , and as a result YopE is released into the extracellular milieu during in vitro infection of host cells with a Y . pseudotuberculosis yopB mutant [26] . In bacterial growth media , steady state levels of YopE were similar in 32777 , mE , and ΔBmE , either at high Ca2+ when YopE is produced but not secreted , or at low Ca2+ when YopE is produced and secreted , as determined by immunoblotting ( Fig 2A and 2B , lanes 1–3 , 5–7 ) . Immunoblotting for DnaK was used to control for loading ( Fig 2A and 2B ) . In contrast , steady state amounts of YopE were lower in ΔYscF especially in the low Ca2+ medium ( Fig 2A and 2B , lanes 4 and 8 ) . Next , the above strains were used to infect bone marrow-derived macrophages ( BMDMs ) from C57BL/6 mice and translocation of YopE was measured by detergent solubility and immunoblotting [27] . Immunoblotting for β-actin was used to control for loading . As compared to the control strains , ΔBmE failed to translocate YopE , as evidenced from its absence in the detergent soluble ( cytosolic ) fraction of infected BMDMs ( Fig 2C , compare lanes 7–9 ) . ΔYscF also did not translocate YopE ( Fig 2C , lane 10 ) , however , this strain also produced very low amounts of the protein during infection of BMDMs , as seen by analysis of the detergent insoluble ( bacterial ) fraction ( Fig 2C , lane 5 ) . These observations indicated that ΔBmE is selectively deficient in translocation of YopE while ΔYscF is deficient for production , secretion and translocation of YopE . BMDMs infected with the Y . pseudotuberculosis strains described above were tested for to the ability to activate ET cells in vitro . In addition to 32777 , mE , ΔYscF and ΔB , as controls we analyzed BMDMs infected with strains lacking the GAP domain of YopE ( ΔGAP ) or the virulence plasmid ( ΔpYV ) ( Table 1 ) . Co-culture of ET-enriched CD8+ T cell lines with Y . pseudotuberculosis inactivated with antibiotics in the absence of BMDMs , resulted in minimal IFNγ production ( Fig 2D ) . In contrast , BMDMs infected with 32777 , mE or ΔGAP supported secretion of IFNγ from co-cultured ET cells ( Fig 2D ) . BMDMs infected with ΔpYV , ΔYscF or ΔB , did not support elevated secretion of IFNγ from ET cells ( Fig 2D ) . These results indicate that translocation of YopE into the cytosol of BMDMs is required for activation of ET cells in vitro . C57BL/6 mice were infected IV with mE , ΔYscF or ΔBmE to determine if secretion and translocation of YopE are required for the large ET response in vivo . It was expected that ΔBmE and ΔYscF would be more attenuated than mE , because the former strains are defective for translocation of all effectors , while mE is only missing the catalytic activity of YopE . To compensate for different levels of attenuation in the mutants , mice were infected with maximum sub-lethal doses of each strain . However , even with these adjusted doses , at 7 dpi , significantly lower levels of bacteria were recovered from the spleens and livers of mice infected with either ΔYscF or ΔBmE than those infected with mE ( Fig 3A and 3B ) . Additional cohorts of mice infected as above with mE or ΔBmE were analyzed at 4 dpi and results showed that ΔBmE colonized spleen and liver at a significantly lower level than mE at this time as well ( S2 Fig ) . Next , the ET cell response was assessed at 7 dpi by tetramer staining and flow cytometry of splenocytes . As we have shown before [24] , IV infection of mice with mE results in a large ET cell response in spleens where an average of 3 . 57 million of these cells are recovered at 7 dpi ( Fig 3C and 3D ) . This ET cell number corresponds to ~30% of all CD8+ T cells in spleens of infected mice . Infection with ΔBmE resulted in a significantly reduced ET cell response both in cell number ( average of 0 . 57 million ) and percentage among the CD8+ T cells ( average of 6 . 26% ) comparing to those animals infected with mE ( Fig 3C and 3D ) . These results suggested that the large ET cell response observed during Y . pseudotuberculosis infection requires translocation of YopE . To confirm that the greatly decreased ET cell response in ΔBmE-infected mice was not due to significantly decreased bacterial load as compared to mE ( Fig 3A and 3B ) , a new strain YopEΔN15 ( Table 1 ) selectively defective for export of YopE was created by deleting the secretion signal in residues 2–15 of YopE [28] . Additionally , a strain lacking YopE ( ΔYopE , Table 1 ) was constructed and used as a negative control . YopEΔN15 was defective for YopE translocation into infected BMDMs ( S3 Fig ) . In mouse infection , YopEΔN15 colonized spleens and livers to the same extent as mE and ΔYopE ( Fig 3A and 3B ) , yet the number of ET cells in spleens didn’t increase beyond that seen in mice left uninfected or infected with ΔYopE ( Fig 3C and 3D ) . Therefore , YopE translocation is required for the large ET cell response . The ET cell response in spleens of mice infected with ΔBmE was significantly higher than that in mice infected withΔYscF or YopEΔN15 ( Fig 3C and 3D ) . However , similar numbers of total splenic CD8+ T cells were observed in mice infected with ΔBmE , ΔYscF or YopEΔN15 ( Fig 3E ) . Thus , in the absence of the large translocation-dependent response , a lower but still significant “translocation-independent” ET cell response was detected in mice infected with the ΔBmE mutant but not the ΔYscF or YopEΔN15 mutants . The translocation-independent ET cell response detected in mice infected with ΔBmE suggested the possibility that secreted YopE proteins were subject to cross presentation . Batf3-/- mice deficient in the Batf3 transcription factor , lack CD8α+ DCs and the developmentally related CD103+ DCs and are thus defective in cross-presentation of extracellular proteins [8 , 9] . To study the role of cross presentation in the translocation-independent pathway , Batf3-/- mice or C57BL/6 controls were infected IV with the maximal sub-lethal dose of mE or ΔBmE as above , followed by determination of bacterial numbers in organs , and numbers of CD8+ T cells and ET cells in spleen . At 7 dpi , comparable numbers of mE were recovered in spleens or livers of C57BL/6 and Batf3-/- mice , and the same was true for ΔBmE ( Fig 4A and 4B ) . Similarly , comparable numbers of CD8+ T cells were recovered from the spleens of the C57BL/6 and Batf3-/- mice infected with mE or ΔBmE ( Fig 4C ) . Furthermore , the ET cell numbers recovered from either C57BL/6 or Batf3-/- mice infected with mE were comparable ( Fig 4D ) . However , the number of ET cells in C57BL/6 mice infected with ΔBmE was significantly higher than in Batf3-/- mice or uninfected C57BL/6 mice ( Fig 4D ) . The number of ET cells in the spleens of Batf3-/- mice infected with ΔBmE was in fact not different from that of C57BL/6 mice left uninfected . Collectively , these results indicate that in the absence of YopE translocation , cross presentation of secreted YopE can occur , leading to a smaller , yet still significant ET cell response . Our previous results demonstrated a linear correlation between the number of ET cells and the number of CD11b+ cells in the spleens of Y . pseudotuberculosis-infected mice [24] . CD11b+ cells recruited to infected tissues are a heterogeneous population of cells that include CD11b+Ly6Chi inflammatory monocytes . These cells express CCR2 , a chemokine receptor that promotes their emigration from the bone marrow [29] . Inflammatory monocytes can differentiate into infDCs , which contribute to host protection by presenting antigen to T cells [10] . To begin to investigate the role of infDCs derived from CCR2-expressing CD11b+Ly6Chi cells in the dominant ET cell response , we infected Ccr2-/- or C57BL/6 control mice IV with mE , and measured several parameters of the infection and immune response ( mouse survival and weight , bacterial CFU and numbers of CD11b+Ly6Chi and ET cells in spleens ) . With a dose of 1000 CFU , all wild type C57BL/6 mice survived infection to at least 14 days , and their body weights gradually decreased until 7–8 dpi , then recovered ( Fig 5A and 5B ) . In contrast , the Ccr2-/- mice lost body weight faster than the age-matched wild type C57BL/6 mice and the difference in weight became significant after 6 dpi ( Fig 5B ) . Infected Ccr2-/- mice also became obviously lethargic at 7 dpi , and died between 9–13 days ( Fig 5A ) . From 5 to 7 dpi , the spleen colonization levels of mE in C57BL/6 and Ccr2-/- mice were similar , with the exception that at 6 dpi bacterial numbers were significantly lower in Ccr2-/- mice ( Fig 5C ) . As expected , the accumulation of CD11b+Ly6Chi cells observed in the spleens of C57BL/6 mice at 7 dpi was diminished in Ccr2-/- mice ( Fig 5D ) . Time course analysis of the ET cell response in spleens showed that the numbers of these cells increased between 5 and 7 dpi in both C57BL/6 and Ccr2-/- mice infected with mE ( S4 Fig ) . However , at 7 dpi the number ( Fig 5E ) of ET cells were significantly lower in Ccr2-/- mice than C57BL/6 mice . Comparable levels of ET cells were observed in the two groups of mice left uninfected ( Fig 5E ) . When infection was carried out with the translocation-deficient strain ΔBmE , similar numbers of ET cells were present in C57BL/6 and Ccr2-/- mice ( Fig 5E ) , even though the two groups of mice were colonized to different levels by this strain ( Fig 5C ) . Additionally , the numbers of ET cells in Ccr2-/- mice infected with mE was not significantly different from the level seen in C57BL/6 or Ccr2-/- mice infected with ΔBmE ( Fig 5E ) . Overall , these results indicated an important role for CCR2-expressing CD11b+Ly6Chi cells in the formation of the large ET cell response during Y . pseudotuberculosis infection . Based on these findings and additional experiments discussed below , we conclude that the CCR2-expressing CD11b+Ly6Chi cells that are orchestrating the large translocation-dependent ET cell response are equivalent to infDC , and hereafter use this terminology to refer to this cell population . The TEM1 β-lactamase reporter has been used to identify cells that are injected with YopE in mice infected with Yersinia [30 , 31] , however , it has not been shown that infDCs are targeted for YopE translocation . Therefore , we set out to monitor YopE translocation into infDC using the TEM1 β-lactamase-based fluorescence system . A 32777 strain encoding the chaperone-binding domain of YopE fused to the TEM1 β-lactamase ( YopE-TEM1 ) was created ( YopE-Bla; Table 1 ) . Upon incubation of splenocytes containing translocated YopE-TEM1 with the substrate CCF4-AM , intracellular β-lactamase will cleave the substrate causing the cell to fluoresce blue . It was determined that as few as ~100 molecules of β-lactamase can be detected in a single cell [32] . YopE-Bla was attenuated in comparison to mE in our IV mouse infection model , and therefore an infection dose of 105 CFU was used . At 6 dpi of C57BL/6 mice with YopE-Bla , approximately 16% of splenocytes were blue as a result of translocation of the YopE-TEM1 fusion protein ( Fig 6A ) . Translocation of YopE-TEM1 increased with increasing colonization levels of YopE-Bla in spleen and liver ( Fig 6B ) . Translocation largely depended on YopB because an average of only 0 . 5% of the total splenocytes were blue after infection with the control ΔB YopE-Bla strain ( Table 1 ) ( Fig 6B ) . Flow cytometry was used to quantify the percentage of CD11b+ , CD11c+ , CD4+ or CD8+ cells in splenocytes , and the percentage of these cells that were blue as a result of YopE-TEM1 translocation at 6 dpi with YopE-Bla . Consistent with our previous observation [24 , 33] , a large percentage ( ~21% ) of total viable splenocytes were identified to be CD11b+ ( Fig 6C top ) . Approximately 20% of splenocytes were CD4+ , while lower numbers ( <10% ) of splenocytes were CD11c+ or CD8+ ( Fig 6C , top ) . Among the CD11b+ cells , an average of 71% were blue as a result of YopE-TEM1 translocation ( Fig 6C bottom ) . In contrast , ~8% of CD11c+ or CD4+ cells or 14% of the CD8+ T cells , were blue as a result of YopE-TEM1 translocation ( Fig 6C , bottom ) . Next , we determined the numbers of CD11b+ cells that were Ly6Chi or Ly6Cmed , considering the former infDCs and the later PMNs , and quantified the percentages of these cells that were subject to YopE-TEM1 translocation . After infection with of YopE-Bla , ~5% of all CD11b+ cells were Ly6Chi infDC and the remainder were Ly6Cmed PMNs ( Fig 6D , top ) . This skewed increase in the percentage of PMNs most likely reflected a heightened yet unproductive inflammatory response in the mice terminally infected with YopE-Bla . Consistent with previous studies where PMNs represented the major recipients of translocated YopE [30] , a greater percentage of PMNs ( ~72% ) than infDCs ( ~39% ) were blue in mice infected with YopE-Bla ( Fig 6D , bottom , 6E and 6F , black lines ) . Smaller percentages of PMNs ( ~5% ) and infDCs ( ~0 . 75% ) were blue in mice infected with ΔB YopE-Bla ( Fig 6E and 6F , gray lines ) , confirming that translocation of YopE-TEM1 in vivo was largely YopB dependent . These results indicated that infDCs were subject to YopE translocation during Y . pseudotuberculosis infection , albeit to a lesser degree than PMNs . Next , we sought to further characterize the CCR2-expressing Ly6Chi infDCs that are present in Y . pseudotuberculosis-infected mouse spleens , and to determine if these cells can directly activate ET cells . CCR2 reporter mice , which express enhanced GFP under the control of the murine CCR2 promoter [34] , were infected as above with mE or ΔBmE . At 7 dpi , the average spleen colonization of mice infected with mE was 106 . 9 CFU , while that of mice infected with ΔBmE was 105 . 1 CFU , comparable to results in wild type mice infected with these strains of Y . pseudotuberculosis . After enrichment of splenic monocytes through negative selection , the GFP+ cells were isolated from GFP- cells by sorting and phenotypically characterized by flow cytometry before they were used in direct ex vivo antigen display ( DEAD ) assays [35] . The GFP+ cells sorted from mice infected with mE expressed high levels of CD11b and Ly6C ( Fig 7A , dark green lines ) . In addition , these cells also expressed intermediate levels of CD11c , MHC class II and F4/80 ( Fig 7A , dark green lines ) . In comparison , the GFP+ cells isolated from mice infected with ΔBmE expressed these surface markers as well , though the levels of CD11b and MHC class II were lower ( Fig 7A , light green lines ) . Thus , the GFP+ cells from mice infected with mE were phenotypically in line with the characteristics of infDCs [10] . The isolated GFP+ cells characterize above were tested for the ability to activate ET-enriched CD8+ T cell lines using DEAD assay [35] . GFP- cells were analyzed in parallel as a control . Significantly higher amounts of IFNγ were produced by the ET-enriched CD8+ T cell lines when they were co-cultured with GFP+ cells from mE-infected mice , as compared to the GFP- cells from the same mice or either cell type from ΔBmE-infected mice ( Fig 7B ) . These results indicated that CCR2-expressing infDC that are isolated from Y . pseudotuberculosis mE-infected spleens can activate ET cells ex vivo . Adoptive transfer experiments were carried out to determine if an ET response could be reconstituted in Ccr2-/- mice by adoptive transfer of CCR2-expressing infDC . GFP+ cells were isolated as above from spleens of CCR2-GFP mice infected for 4 days with ΔYopE , to ensure that these cells do not carry the YopE69-77 antigen . The GFP+ cells were adoptively transferred into Ccr2-/- mice that had been infected the day before with mE . Six days later , the percentages of ET cells among all CD8+ T cells in spleens were quantified by tetramer staining and flow cytometry . As shown in Fig 7C , the percentage of ET cells was significantly higher after adoptive transfer of GFP+ cells as compared to treatment with PBS alone as a control . Thus , an ET response was reconstituted after adoptive transfer of CCR2-expressing infDC into Ccr2-/- mice infected with mE .
Here we have provided evidence that infDCs and CD8α+ DCs contribute to the production of antigen-specific CD8+ T cells using differentially localized ( secreted vs . translocated ) YopE during Y . pseudotuberculosis infection . Our results indicate that CCR2-expressing infDCs derived from CD11b+Ly6Chi cells are required to produce the majority of ET cells in a YopE antigen “translocation-dependent” pathway , while CD8α+ DCs cross-present YopE69-77 in a “translocation-independent” manner . During Y . pseudotuberculosis infection , the translocation-dependent pathway dominates and leads to the formation of an unusually large number of ET cells . The unique combined requirements of YopE antigen delivery by the Yersinia T3SS and CD8+ T cell activation by host infDCs , resulting in the formation of large ET cell response , represents a new mechanism to generate an antigen-specific CD8+ T cell response . CCR2 was required for host protection during Y . pseudotuberculosis mE infection , and curiously , its function was more important in the adaptive response stage . With our infection dose , in both C57BL/6 and Ccr2-/- mice , adaptive response was evident 5 dpi because ET cells started to be detectable at higher levels at this time than those in the mice left uninfected ( S4 Fig ) . Yet in mE-infected Ccr2-/- mice , mice began to succumb at 9 dpi ( Fig 5A ) . Furthermore , although both C57BL/6 mice and Ccr2-/- mice lost weight post infection , it was only until 6 dpi that a significant difference was observed ( Fig 5B ) . More importantly , from 5 to 7 dpi , the average colonization levels in the spleens of Ccr2-/- mice infected with mE were not higher than those of C57BL/6 mice ( Fig 5C ) . These observations support the idea that CCR2 function was important for host protection during the adaptive response stage . These results are different from previous studies with L . monocytogenes . For example , depletion of CCR2-postive cells from CCR2-DTR mice resulted in death of the mice from L . monocytogenes infection at 3 dpi [36] . Although our data do not rule out that infDCs , or their precursors the inflammatory monocytes , participate in host protection through direct killing of Y . pseudotuberculosis , we favor the idea that infDCs exert their important host protective function through activating CD8+ T cells during infection . The CCR2-dependent CD11b+Ly6Chi cells in spleens at 7 dpi with Y . pseudotuberculosis mE were characterized as infDCs . These cells expressed MHC class II molecules , were positive for F4/80 , and CD11b , and expressed medium levels of CD11c ( Fig 7A ) . More importantly , these cells obtained from mE-infected spleens activated ET-enriched CD8+ T cell lines to secrete IFNγ ex vivo ( Fig 7B ) , and stimulated production of ET cells upon adoptive transfer into Ccr2-/- mice infected with mE ( Fig 7C ) . InfDCs have been implicated before to be important in innate host protection against Y . pestis infection in mice , and interestingly the effector YopM was shown to inhibit their recruitment to spleens [37] . Here we obtained evidence that CCR2-expressing infDCs were required to generate the large translocation-dependent ET cell response in mice infected with Y . pseudotuberculosis ( Fig 5E ) . In comparison , previous studies indicated that CCR2-expressing CD11b+Ly6ChiLy6G- cells recruited to tissues of mice infected with S . Typhimurium had features of immature myeloid cells that exhibited both protective and immunosuppressive properties through producing NO [15] . Upon ex vivo culture with OT-I or OT-II T cells the S . Typhimurium-induced immature myeloid cells were able to present OVA peptide , but at the same time inhibited the proliferation of the CD4+ or CD8+ T cells by an NO-dependent mechanism [15] . Furthermore , the infDCs characterized here are likely to be different from the Tip-DCs described during L . monocytogenes infection [14] . In the absence of the infDCs in Ccr2-/- mice infected with mE , there was a ~10-fold decrease in the number of ET cells recovered from the spleens of the infected animals ( Fig 5E ) . In contrast , even though Tip-DCs could prime naïve alloreactive T cells in vitro , in their absence in the spleens of the L . monocytogenes-infected Ccr2-/- mice , the numbers of the LLO91-99 specific CD8+ T cell were actually larger than that in the wild type mice similarly infected . This potentially suppressive effect was also likely due to the NO produced by the Tip-DCs [14] . These findings suggest that distinct features of Y . pseudotuberculosis pathogenesis and the resulting host response leads to the CCR2-dependent production of infDCs , which are functionally different from the CD11b+Ly6ChiLy6G- cells that are recruited to tissues of mice infected with S . Typhimurium ( i . e . immature myeloid cells ) or L . monocytogenes ( i . e . Tip-DCs ) . Understanding the unique features of Y . pseudotuberculosis-host interactions that lead to the production of infDCs may have an impact on future design of vaccines to prime antigen-specific CD8+ T cell responses . There are at least two possible pathways by which infDCs promote the formation of the large ET cell response . First , the infDCs may activate the ET cells in an antigen-dependent manner . As we have shown here , these cells were subjected to the T3SS-mediated injection of YopE from Y . pseudotuberculosis ( Fig 6 ) . The cytoplasmic location of YopE presumably allowed the antigenic peptide YopE69-77 to be presented through the classical pathway as has been proposed before [38] . Alternatively , infDCs ( or their inflammatory monocyte precursors ) may activate the ET cells indirectly through secretion of cytokines such as IL-18 . It has been shown that inflammatory monocytes activate both NK cells and memory CD8+ T cells through producing IL-18 and IL-15 during infection [39] . IL-18 has been shown to be required for host protection during Yersinia infection [40–42] . In addition , elevated levels of IL-18 were detected in serum during Y . pseudotuberculosis infection [33 , 43] . It is possible that infDCs activate ET cells through producing IL-18 , as well as presentation of the YopE69-77 peptide . The diverse TCR Vβ usage of the ET cells ( Fig 1 ) is suggestive of multiple independent antigen-presenting events . The levels of ET cells observed in the Batf3-/- mice infected with mE were comparable to that observed in wild type mice infected with mE ( Fig 4E ) , indicating that when the translocation-dependent pathway is operating CD8α+ DCs are not required to present YopE peptide to CD8+ T cells . CD8α+ DCs were only required to cross present YopE peptide if infection was carried out with strain ΔBmE , since the levels of ET cells in the Batf3-/- mice infected with ΔBmE decreased to the levels seen in mice left uninfected ( Fig 4E ) . Though we cannot rule out that other types of DCs present the antigenic epitope YopE69-77 to CD8+ T cells , we think it is possible that during Y . pseudotuberculosis infection , infDCs both present the antigen to the ET cells and activate them through secretion of cytokines such as IL-18 . Future studies are needed to distinguish the relative contributions of these two activities to the production of the large ET response . Another interesting finding here is the requirement of CD8α+ DCs , in eliciting the ET cells by a “translocation-independent” pathway during infection with a Y . pseudotuberculosis yopB mutant . CD8α+ DCs are required for cross-presentation in vivo [8 , 9] . During infection of cultured cells in vitro , Y . pseudotuberculosis has been shown to secrete YopE into the surrounding environment , especially in the case of infection with a yopB mutant [26] . Our results here indicated that during infection with the yopB mutant , secreted YopE is taken up and cross-presented by the CD8α+ DCs in vivo . The yopB mutant ΔBmE was unable to translocate Yops into host cell cytosol , yet was competent to secrete Yops into its environment . In contrast , the mutant ΔYscF was incompetent in both secretion and translocation . Consistently , ΔBmE elicited a smaller yet significant ET response , while ΔYscF was totally deficient in producing an ET response ( Fig 3D ) . To further strengthen the point that only secreted or translocated YopE are presented to CD8+ T cells , YopEΔN15 was incapable to secrete YopE , and infection with this strain resulted in similar levels of bacterial colonization in deep tissues ( Fig 3A and 3B ) . Yet YopEΔN15 didn’t elicited ET cells beyond what was seen in mice left uninfected ( Fig 3C and 3D ) . Cross-presentation appeared to be the sole pathway used to present the secreted YopE to CD8+ T cells during infection with ΔBmE , since ET cell numbers in Batf3-/- mice infected with this mutant were similar to those in mice left uninfected ( Fig 4D ) . Cross-presentation of YopE by CD8α+ DCs may have contributed to the formation of the large ET response during infection with the strain mE . In the Ccr2-/- mice infected with mE , the number of ET cells did decrease dramatically , but the number is not lower than that in mice infected with ΔBmE ( Fig 5E ) . This indicated that in the absence of infDCs as seen in the Ccr2-/- mice , other cells-most likely the CD8α+ DCs , presented YopE69-77 to CD8+ T cells . Nevertheless , the fact that ΔBmE elicited ET cells in a CD8α+ DC-dependent manner suggested that the yopB mutant of Y . pseudotuberculosis could be used to specifically target antigens , by a translocation-independent mechanism , toward cross-presentation pathways to elicit class I MHC-restricted immune responses .
Use of mice for the preparation of BMDMs and for infection experiments was carried out in accordance with a protocol that adhered to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ( NIH ) and was reviewed and approved ( approval #206152 ) by the Institutional Animal Care and Use Committee at Stony Brook University , which operates under Assurance #A3011-01 , approved by the NIH Office of Laboratory Animal Welfare . The Y . pseudotuberculosis strains used in this study are derived from a serogroup O:1 strain 32777 ( Table 1 ) . The mutant strains mE ( yopER144A ) , ΔB ( yopB40 ) , and ΔpYV ( cured of the virulence plasmid ) have been described [44 , 45] . The yopER144A mutation was introduced into ΔB to create strain ΔBmE using allelic exchange as described [44] . Additional mutant strains were generated by the same method after constructing appropriate allelic exchange vectors using the plasmid pSB890 . The vector used to construct ΔYscF was created using PCR and primers 5'- CAATCATCAACGCTATCCAGAAGG -3' and 5'- ( tctaga ) TTCCCACCGCTTACCAAACGAG -3' to amplify the operon yscDEFGH . A vector containing this operon was subjected to QuickChange mutagenesis using primer 5’-CATTATGTAGCAGGAGACCTAAAATAAGCTTATGAAATATAAACTCAACGTACTGTTAGC -3’ and its reverse complement to delete the coding region of yscF . To construct ΔGAP , YopEΔN15 , and ΔYopE , pSB980 vectors encoding yopE and its 5’- and/or 3’-region were modified as follows . Primer 5’-CCGGTGGTGACACCAGCTGCATGATATGGATAAAAACAAGGGGA-3’ and its reverse complement were used in QuickChange mutagenesis to delete codons 89 to 219 , which correspond to the RhoGAP domain . Primer 5’-AGCCAAGGTAATAAATAGTC ATGTCTGTGTCAGGATCTAGC-3’ and its reverse complement were used in QuickChange mutagenesis to delete codons 2–15 , which correspond to the secretion signal . Primer 5’-GTTTTAATAGCCAAGGTAATAAATAGTCTGATATG GATAAAAACAAGGGG-3’ and its reverse complement primer were used to delete the entire coding region of yopE . To construct strains YopE-Bla and ΔB YopE-Bla expressing YopE-TEM1 fusion protein , a pSB980 vector encoding codons 1–86 of yopE fused in frame to the open reading frame of TEM1 β-lactamase was constructed . The resulting vector was conjugated into 32777 and ΔB , and integration of the plasmid into yopE on pYV by homologous recombination was selected for using the gene encoding tetracycline resistance on pSB890 . Isolates generated from the desired integration events were identified by testing for T3SS-mediated secretion of the YopE-TEM1 fusion protein under low calcium growth conditions . Female C57BL/6J and B6 . 129S ( C ) -Batf3tm1Kmm/J ( stock Number 013755 , Batf3-/- ) mice were from Jackson Laboratory . CCR2-GFP mice and Ccr2-/- mice on the C57BL/6 background ( both provided by Dr . Eric Pamer ) were bred at Stony Brook University . For intravenous ( IV ) infection , over night bacterial culture grown in Luria-Bertani ( LB ) at 28°C were washed once and re-suspended in phosphate buffered saline ( PBS ) to achieve the desired CFU/ml . Then 200 μl volumes were delivered via lateral tail vein . At indicated times post infection , or when death was imminent , mice were euthanized by CO2 asphyxiation . Mouse spleens and livers were dissected aseptically and weighed . Spleens were homogenized with a 5 ml syringe plunger in 5 ml of Dulbecco’s Modified Eagle Medium ( DMEM ) . Livers were homogenized using a Stomacher80 ( Seward Lab System ) in 4 ml of PBS . Serial dilutions in LB were plated ( 100 μl ) on LB agar to determine bacterial colonization by CFU assay , and the limit of detection was 50 CFU or log10 CFU of 1 . 7 . All procedures for working with mice were approved by the Stony Brook University Institutional Animal Care and Use Committee . Two different growth conditions were used to prepare bacterial lysates . For the high calcium condition to encourage Yop synthesis but inhibit their secretion into medium , overnight cultures were diluted to OD600 of 0 . 1 into LB containing 2 . 5 mM of calcium chloride and grown at 37°C with shaking for 2 h . For the low calcium condition to encourage both synthesis and secretion of Yops , overnight cultures were diluted to OD600 of 0 . 1 into LB containing 20 mM of magnesium chloride and 20 mM of sodium oxalate and grown at 28°C for 1 h then 37°C for 2 h with shaking . After growth under one of the conditions above , the bacterial cultures were centrifuged , and the pelleted bacteria were resuspended in Hank’s Balanced Salt Solution ( HBSS ) . After a second centrifugation , the pelleted bacteria were resuspended into 2X Laemmli sample buffer . To prepare secreted Yops , bacterial cultures in low calcium conditions were grown at 28°C for 1 h then 37°C for 4 h with shaking . Yop proteins in culture supernatants were precipitated with 10% trichloroacetate , washed once in cold acetone , dried and resuspended in 1X Laemmli sample buffer . Detergent solubility assay was used to determine the amount of YopE translocated into the cytosol of BMDMs as described [24] . Briefly , bacteria were grown in the low calcium condition at 28°C for 1 h then 37°C for 2 h with shaking , washed and resuspended in HBSS . Then the bacteria were diluted into 1 ml of BMM-low medium and applied to C57BL/6L-derived BMDMs at 8X105 cells/well on 6-well plate at MOI of 50 . After incubating for 1 . 5 h , the monolayer was washed with PBS and scraped into 50 μl of 1% Triton X-100 buffer ( 10 mM Tris pH7 . 6 , 150 mM NaCl , 10% glycerol , 1% Triton X-100 ) containing protease inhibitor cocktail ( Roche ) . The lysate was centrifuged for 10 min at 12 , 000 g at 4°C to separate the supernatants ( soluble fractions ) from the pellet ( insoluble fractions ) . The resulting supernatants of pellets were mixed with or resuspended in Laemmli sample buffer . Bacterial lysates , Yop proteins and macrophage fractions were resolved by SDS-PAGE and transferred to nitrocellulose membrane , and analyzed by immunoblotting with mixture of monoclonal antibodies against YopE , or DnaK ( clone 8E2/2; Stressgen ) as described before [46] . Single cell suspensions of spleens were prepared as described before [18] . Briefly , splenocytes in suspension were incubated in additional 20 ml of DMEM containing Penn/Strep for 20 m . Then red blood cells ( RBC ) were lysed , and viable cells were counted using typan blue exclusion with Countess ( Invitrogen ) . Suspended cells ( 1X106 ) were blocked using anti-mouse CD16/CD32 ( FcgIII/II receptor ) clone 2 . 4G2 ( BD Pharmingen ) and labeled with allophycocyanin-conjugated MHC class I tetramer KbYopE69-77 , which was provided by the NIH Tetramer Core Facility ( Emory University , Atlanta , GA ) , at room temperature for 1 h and fluorophore-conjugated antibodies on ice for 20 minutes . The antibodies used were AlexaFluor488 or PE anti-mouse CD8α ( 53–6 . 7 , BD , BioLegend ) , and PerCP anti-mouse CD3e ( clone 145-2C11 , PharMingen ) . CD8+ T cells were gated as CD3+CD8+ events throughout the study . Vβ Screening was carried out with Mouse Vβ TCR Screening Panel from BD Biosciences according to manufacturer’s instructions . To stain for the intracellular activity of the translocated YopE-TEM1 fusion protein , suspended splenocytes ( 1X106 cells ) were incubated in 100 μL of complete cell medium ( Dulbecco modified Eagle medium supplemented with 10% heat-inactivated fetal bovine serum , 12 . 5 mM HEPES , 2 mM L-glutamine , 1 mM sodium pyruvate , 1 mM penicillin-streptomycin and 55 μM β-mercaptoethanol ) , then 20 μL of CCF4-AM reagent ( Invitrogen ) in 6X Substrate Loading Solution was added to the bottom of the wells and incubated for 1 h at room temperature . The cells were then washed 5 times and incubated with appropriate antibodies . Anti-mouse antibodies used to characterize the leukocytes are PE F4/80 ( BM8 ) , PerCP/Cy5 . 5 Ly6C ( HK1 . 4 ) , PE/Cy7 CD11c ( N418 ) , Alexa Fluor 700 Ly6G ( 1A8 ) , Alexa Fluor 647 I-A/I-E ( M5/114 . 15 . 2 ) , Brilliant Violet 510 CD11b ( M1/70 ) . Antibodies were from BioLegend unless indicated otherwise . Labeled cells were analyzed using a BD FACSCaliber or a Cytek DXP 8 color upgrade . Gating on side and forward scatter was used to focus on intact splenocytes . Representative examples of the gating strategy are show in S5 Fig . Data were analyzed with FlowJo software ( Tree Star ) . ET-enriched CD8+ T cell lines were generated from mice that survived IV challenge with 500 to 2000 CFU of mE as described before with modification [22] . Briefly , RBC-lysed splenocytes from naïve C57BL/6 mice were treated with 50 μg/ml mitomycin C ( Sigma-Aldrich ) for 30 min at 37°C in complete cell medium , washed with complete medium , used at 1X107 cells/well in 6-well plate as APCs . CD8+ T cells were enriched from spleens of mice infected with mE for 180 days using CD8a ( Ly-2 ) MicroBeads ( Miltenyi Biotec . ) following manufacturer’s instructions . One million enriched CD8+ T cells were mixed into each well of APCs in 4 ml of complete medium containing 160 pM YopE69-77 peptide . After 48 hours , recombinant human IL-2 ( Peprotech , Rocky Hill , NJ ) was added to the culture at a final concentration of 20 U/ml . Culture media was replenished every other day with fresh IL-2-containing medium . After 2 weeks , cells were washed into fresh medium and used for in vitro antigen presentation or DEAD assay . In general , about 5–11% of total splenocytes were enriched as CD8+ T cells; ~2% or less of the enriched cells and 10–45% of the resulting viable ET-enriched CD8+ T cell lines stained positive for YopE69-77 tetramer . For in vitro antigen presentation , BMDMs at 4X104 cells/well on 96-well plate were left uninfected or infected with various Y . pseudotuberculosis strains for 4 h at MOI of 10 , gentamicin was added to final concentration of 8 μg/ml after 2 h . To prepare bacteria for infection , bacteria were grown in the low Ca2+ condition with shaking at 28°C for 1 h and 37°C for 2 h . The bacteria were then washed and resuspended in Hank’s balanced salt solution pre-warmed to 37°C , diluted to desired CFU/ml in 100 μl BMM-low medium ( Dulbecco modified Eagle medium supplemented with 10% heat inactivated fetal bovine serum , 15% L-cell conditioned medium , 2 mM L-glutamine , 1 mM sodium pyruvate ) , applied to the BMDMs . The plate was centrifuged for 5 min at 200X g and incubated at 37°C for 2 h , next gentamicin was added to final concentration of 8 μg/ml and the plate incubated for additional 2 h at 37°C . Control wells contained bacteria but not BMDMs , and were otherwise treated identically . ET-enriched CD8+ T cell lines at 1 . 6X106/ml in 100 μl complete cell medium containing 2X penicillin-streptomycin was added to the wells containing infected BMDMs or bacteria alone . The plate was then incubated at 37°C for 48 h , and the concentrations of IFNγ in the supernatant was determined with Quantikine Mouse IFNγ kit from R&D Systems , Inc . , following manufacturer’s instructions . CCR2-GFP mice were used within 8 weeks to 3 months of age . Seven days after IV infection with either 1000 CFU/mouse of mE or 2X105 CFU/mouse of ΔBmE , RBC were lysed and monocytes were first enriched from total splenocytes using the EasySep Mouse Monocyte Enrichment kit from Stem Cell Technologies following manufacturer’s instructions . Next , GFP+ cells were sorted from GFP- cells using BD FACSAria III . These GFP+ and GFP- cells were plated on 96 well plates at 105 cells/well , then overlaid with 4X105 cloned YopE69-77 specific CD8+ T cells in complete cell medium with antibiotics . The concentrations of IFNγ in the supernatant were determined 48 h later with ELISA . Four days after IV infection of CCR2-GFP mice with 1000 CFU/mouse of ΔYopE , GFP+ cells were enriched and sorted as described above . The isolated GFP+ cells were washed three times in PBS and injected ( 1 . 8-2X106 cells/mouse ) retro-orbitally to Ccr2-/- mice that had been infected with mE at 1000 CFU/mouse mE the day before . Control mice received PBS alone by retro-orbital injection . Seven days post-infection , splenocytes were analyzed with tetramer and antibody staining followed with flow cytometry . Statistical analysis was performed with Prism 5 . 0 ( Graphpad ) software , mean and SEM were plotted . The tests used are as indicated in the Fig legends . P values of less than 0 . 05 were considered significant . | Dendritic cells ( DCs ) direct host protective adaptive immune responses during infection . How different subpopulations of DCs contribute to the formation of antigen-specific CD8+ T cells is incompletely understood . Infection of C57BL/6 mice with the bacterial pathogen Yersinia pseudotuberculosis results in the production of an exceptionally large CD8+ T cell response to an epitope in the type III secretion system effector YopE . Here , we show that this large CD8+ T cell response requires translocation of YopE into inflammatory DCs , which express CCR2 and accumulate in infected tissues . In contrast , when mice are infected with a Y . pseudotuberculosis strain that can secrete but not translocate YopE , a smaller response is seen , and under these conditions the generation of YopE-specific CD8+ T cell requires CD8α+ DCs . Our results indicate that distinct DC subsets participate in constructing the CD8+ T cell response to secreted , versus translocated , YopE . Furthermore our data indicate that inflammatory DCs are a driving force behind the massive CD8+ T cell response to a protective epitope in a bacterial virulence factor that is translocated into host cells . | [
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Environmental enteric dysfunction ( EED ) is commonly defined as an acquired subclinical disorder of the small intestine , characterized by villous atrophy and crypt hyperplasia . EED has been proposed to underlie stunted growth among children in developing countries . A collection of biomarkers , organized into distinct domains , has been used to measure different aspects of EED . Here , we examine whether these hypothesized relationships , among EED domains and between each domain and stunting , are supported by data from recent studies . A systematic literature search was conducted using PubMed , MEDLINE , EMBASE , Web of Science , and CINAHL between January 1 , 2010 and April 20 , 2017 . Information on study objective , design , population , location , biomarkers , and results were recorded , as well as qualitative and quantitative definitions of EED . Biomarkers were organized into five EED domains , and the number of studies that support or do not support relationships among domains and between each domain with stunting were summarized . There was little evidence to support the pathway from intestinal permeability to microbial translocation and from microbial translocation to stunting , but stronger support existed for the link between intestinal inflammation and systemic inflammation and for intestinal inflammation and stunting . There was conflicting evidence for the pathways from intestinal damage to intestinal permeability and intestinal damage to stunting . These results suggest that certain EED biomarkers may require reconsideration , particularly those most difficult to measure , such as microbial translocation and intestinal permeability . We discuss several issues with currently used biomarkers and recommend further analysis of pathogen-induced changes to the intestinal microbiota as a pathway leading to stunting .
One-quarter of children under the age of 5 years are stunted , defined as a height-for-age > 2 standard deviations below the median as defined by the World Health Organization growth standards . Children whose linear growth is impaired during the first 1000 days after conception have an increased risk of poor cognitive development and educational performance , lost productivity and lower adult earnings , chronic diseases , and mortality over their lifetime [1–3] . There is a well-recognized network of interacting determinants that underlie stunting [4–13] . For many years , studies have focused predominantly on nutrition-specific interventions for stunting; however , previous systematic reviews highlight that neither food quantity nor quality fully explains impaired linear growth in children [14 , 15] . Diarrhea has been proposed as a major contributor to growth failure in young children , though results are inconsistent [16–19] . While diarrheal episodes in the first few months after birth lead to increased prevalence of stunting at 24 months [20] , catch-up growth between diarrheal episodes can be sufficient for linear growth recovery in some children [21] . Environmental enteric dysfunction ( EED ) is commonly defined as an acquired subclinical disorder of the small intestine , characterized by villous atrophy and crypt hyperplasia . Previous reviews have described the history and epidemiology of environmental enteric dysfunction in detail [1 , 22–26] and have refocused attention on EED as a potential cause of stunting in developing countries . Exposure to bacteria through fecal contamination is postulated to induce morphological changes , leading to intestinal epithelial damage , increased permeability , and microbial translocation into the lamina propria . This invasion prompts an influx of inflammatory cells to the intestine and leads to local and systematic inflammation , resulting in the reallocation of resources normally directed toward child growth and development , and disruption of hormonal pathways that regulate growth plate activity in long bones . Chronic inflammation and reduced intestinal nutrient absorption are also hypothesized to affect brain development , inducing lasting negative effects on cognition , educational achievement , and linear growth [27] . There are currently no clear diagnostic criteria for EED , which presents a major problem in investigating the role of EED in stunting , and in evaluating treatment and prevention strategies . Intestinal biopsy is used to diagnose diseases with similar pathological changes , such as celiac disease [28]; however , collection of small bowel biopsy samples is technically and ethically infeasible in young children . Over recent years , studies have evaluated a range of potential biomarkers of EED , with a general agreement that these should be organized into distinct domains to measure different aspects of the pathogenic pathway that characterizes EED . Studies have included noninvasive biomarkers of intestinal damage and repair , epithelial permeability and absorption , digestion , epithelial morphology , intestinal inflammation , microbial drivers , systemic immune activation , and non-small intestine organ function . Multiple research groups have used this domain-based approach , focusing on longstanding physiologic relationships to study the complex mechanisms that may underlie EED . Here , we examine whether these relationships are supported by recent and rich new data from studies conducted between 2010–2017 , building on the review conducted by Denno et al for the time period 2000–2010 [29] . We define five contributing domains of EED to provide supporting evidence for our two aims: ( i ) to evaluate the relationships between individual EED domains; and ( ii ) to evaluate the relationships between each EED domain and stunting . We focus on stunting as the primary outcome in this review , as it is the most common outcome of the included studies and is objectively measured . Although our ultimate interest is in cognition and child development , these have not been as commonly measured , and the mechanistic pathways between stunting and neurodevelopment remain unclear [30] .
Our search strategy followed PRISMA ( Preferred Reporting Items for Systematic Reviews and Meta-Analyses ) guidelines for the reporting of systematic reviews [31] . A search for articles in any language between January 1 , 2010 , and April 20 , 2017 , was conducted using PubMed , MEDLINE , EMBASE , Web of Science , and CINAHL . Abstracts were independently screened by two reviewers ( K . H . and M . M . ) and full-text articles that were related in any way to “environmental enteric dysfunction” , “environmental enteropathy” , or “tropical enteropathy” were selected for review . The reference lists of all review articles and original publications were also screened for any relevant studies . Published study abstracts were also included in this review . Disagreements regarding study inclusion were resolved by consensus . Our population of interest included individuals of all ages for whom two or more EED domains , or at least one domain and stunting , were measured . Only human studies were included . While most studies focused on children under 5 years , no age restriction was imposed in the search criteria as some adult studies provide valuable histopathological data that is less frequently collected in young children . Studies in both developed and developing countries were included in the search , though only one study in the final selection included individuals from a developed country [32] . Studies selected for inclusion can be categorized into three groups: ( i ) observational studies in which EED was defined as either an exposure or as an outcome; ( ii ) studies investigating potential EED biomarkers and/or identification/diagnosis of EED; and ( iii ) intervention studies designed to treat or prevent EED . A standardized data abstraction form was used to extract information from each study . Abstracted data included: study objective , study design , location , subject eligibility and description of study population , inclusion or exclusion of subjects with diarrhea or human immunodeficiency virus ( HIV ) , results and final conclusions . Biomarkers and diagnostic tests were recorded , as well as any qualitative and quantitative definitions of EED provided by the authors ( S1 Table ) . To review the evidence supporting the complex mechanisms that may contribute to child stunting we have elected to organize results according to these five EED domains: ( 1 ) intestinal damage and repair , ( 2 ) permeability and absorption , ( 3 ) microbial translocation , ( 4 ) intestinal inflammation , and ( 5 ) systemic inflammation . These domains were determined by consensus from reviewing previous conceptual frameworks and descriptions [2 , 3 , 23 , 24 , 29 , 33–41] . Each domain and its respective non-invasive biomarkers are described below . Studies were included if they reported results for two or more EED domains , or at least one domain and stunting . In the situation where multiple studies included the same EED measurements on the same study subjects , only the most complete report was included in the review . For each of the 5 EED domains , we first present the number of studies that support ( or not ) the relationships of each domain to stunting . Second , we present the number of studies that report data in support of the relationship between each EED domain and the other domains , where data are available . These associations are summarized in Tables 1 and 2 .
Of the 40 reports included in this analysis , 10 ( 25% ) of the studies were conducted in Central or South America , 16 ( 40% ) were conducted in Asia , and 29 ( 73% ) were conducted in Africa . Half of the studies were conducted in rural locations , and 1 study was conducted in both urban and rural populations , while 9 ( 23% ) studies did not specify the setting . Thirty-six ( 90% ) reports included participants under the age of 5 years , and 19 ( 48% ) studies were restricted to children less than 2 years . Three studies were conducted exclusively in adults . Diarrhea was used as an exclusion criterion in 16 ( 40% ) studies; 16 ( 40% ) studies included participants regardless of diarrheal status and 8 ( 20% ) studies did not mention diarrhea in their analyses . Twenty-nine ( 73% ) reports did not specify the HIV status of participants . Seven ( 17% ) studies used HIV as an exclusion criterion and 4 ( 10% ) studies included individuals regardless of HIV status .
In this review , we evaluated individual pathways between domains within EED and between each domain and stunting using studies published between 2010–2017 . We found evidence to support the link between intestinal and systemic inflammation and stunting , but little support for the link between microbial translocation and stunting within the limits of current tests . There was conflicting evidence for the associations between intestinal damage and intestinal permeability , as well as intestinal damage and stunting . These results suggest that current biomarkers and proposed mechanisms of EED pathogenesis may need reconsideration , and future studies of pathogen-induced changes to the intestinal microbiota should investigate alternative pathways of the effect of intestinal and systemic inflammation on growth in children . | Globally , one-quarter of children under the age of five are affected by poor linear growth , known as stunting . Interventions , including giving children supplemental foods or improving hygiene to prevent diarrhea , have only been partially successful at restoring normal growth . Environmental enteric dysfunction ( EED ) is a disease characterized by damage to the lining of the small intestine and is thought to contribute to stunting , though the exact mechanism is still unclear . EED was first diagnosed by removing samples of the intestinal lining and analyzing them under a microscope; however , these procedures are costly and invasive , and recent research has focused on discovering easier ways to identify EED . These tests focus on the many interconnected aspects of EED , including damage and function of the intestinal wall , inflammation , and presence of pathogenic bacteria outside the gut . We conducted a systematic review to evaluate the evidence of relationships between each aspect of EED and stunting . We found the most evidence for the relationship between inflammation and stunting , but less evidence for the relationship between stunting and the presence of pathogenic bacteria outside the gut . Our results suggest that EED may be more complex than previously conceived and that some frequently used EED tests may need reconsideration . | [
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] | 2018 | Environmental enteric dysfunction pathways and child stunting: A systematic review |
Triatomine bugs are the insect vectors of Trypanosoma cruzi , the etiological agent of Chagas disease . These insects are known to aggregate inside shelters during daylight hours and it has been demonstrated that within shelters , the aggregation is induced by volatiles emitted from bug feces . These signals promote inter-species aggregation among most species studied , but the chemical composition is unknown . In the present work , feces from larvae of the three species were obtained and volatile compounds were identified by solid phase microextraction-gas chromatography-mass spectrometry ( SPME-GC-MS ) . We identified five compounds , all present in feces of all of the three species: Triatoma infestans , Panstrongylus megistus and Triatoma brasiliensis . These substances were tested for attractivity and ability to recruit insects into shelters . Behaviorally active doses of the five substances were obtained for all three triatomine species . The bugs were significantly attracted to shelters baited with blends of 160 ng or 1 . 6 µg of each substance . Common compounds were found in the feces of vectors of Chagas disease that actively recruited insects into shelters , which suggests that this blend of compounds could be used for the development of baits for early detection of reinfestation with triatomine bugs .
The flagellate parasite Trypanosoma cruzi ( Chagas ) , the etiological agent of Chagas disease , is transmitted to humans by vectors of the subfamily Triatominae . In part as a result of the distribution of vectors , Chagas disease occurs exclusively in Latin America where it is estimated that 90 million people are at risk of transmission , while 12 million are already infected [1] . The primary vector in the Southern Cone of South America is Triatoma infestans [2] , [3] , however other species such as Panstrongylus megistus and Triatoma brasiliensis , play an important role in transmission in some regions of Brazil [4] , [5] . Control of the sylvatic vector species at domiciliary ecotopes is particularly difficult as they readily invade households from wild ecotopes where they cannot be controlled with available methods [4] , [5] . Triatomine bugs are obligate haematophagous insects , which feed mostly on the blood of birds and mammals . During daylight hours , these insects are usually found aggregated inside protected shelters which they leave after dusk in search of food [6] . Triatomine aggregation behavior inside shelters is well documented and is mediated by chemical signals and thigmotaxis [7] . Previously two chemical signals have been implicated in the aggregation of T . infestans inside the shelters: one released from feces [7]–[9] and another present in their cuticle [10] , however no definitive chemical identifications were carried out . Pheromones are substances used by organisms to transfer information between two or more members of the same species . These can be single chemical compounds or blends of several components . To date , the pheromone blend emitted by triatomine feces has not been fully described for any species . Aggregation mediated by a fecal pheromone was demonstrated in several triatomine species , including P . megistus and T . brasiliensis [11]–[16] . T . infestans deposit their feces in and around shelters and the volatiles emitted from fecal depositions act as chemical landmarks helping the bugs to locate their refuges [7] . It has been demonstrated that only dry feces of T . infestans promote aggregation , while fresh feces induces rejection [9] . Feces become attractive three hours after being deposited and attract bugs for up to 10 days [17] . Triatoma infestans , Rhodnius prolixus and Triatoma mazzoti showed changes in their responses to the fecal aggregation signal depending on their nutritional status [8] , [17] , [18] . In fact , recently fed T . infestans do not aggregate in response to this signal until 8–10 hours after feeding [17] . Four compounds were previously identified in polar solvent extracts of feces of T . infestans and T . mazzottii [19]; however no behavioral response was reported . Subsequently it was demonstrated that adult females and fifth instar larvae of T . infestans were attracted to blends of the fecal compounds 4-methylquinazoline and 2 , 4-dimethylquinazoline [20] . In addition it has been reported that ammonia from humidified feces attracts larvae in bioassays [21] . To the best of our knowledge no previous studies have demonstrated aggregation behavior in response to synthetic compounds . Several authors have reported the occurrence of cross-aggregation responses to feces in diverse triatomine species [11]–[16] . The characterization of a common aggregation signal may allow for the development of chemical baits for monitoring multiple vector species . In the present report , we first aimed to identify common and readily obtainable compounds that promote cross species aggregation in triatomines . For this , we identified volatile compounds present in the feces of T . infestans , P . megistus and T . brasiliensis . The behavior-modifying capacity of the volatiles common to all species was subsequently tested with larvae of each of the three species . Finally , we evaluated the potential of artificial shelters baited with a blend of these fecal volatiles for promoting the aggregation of larvae of each species . We show that a synthetic blend of substances is capable of recruiting bugs into shelters , mimicking the effects of the natural aggregation signals .
Panstrongylus megistus and T . infestans colonies originated from insects captured at domestic and peridomestic refuges in Minas Gerais state , while that of T . brasiliensis came from insects from similar ecotopes in Ceará state , Brazil . These colonies were kept for at least ten years in a rearing chamber ( 4 . 5×1 . 6 mt ) with controlled temperature and a 12∶12 hour light:dark illumination cycle provided by artificial lights ( 4 fluorescent lamps , cold white light , 6400K , 40W ) . As previous reports have shown that all developmental stages of triatomines make use of fecal aggregation pheromones [9] , [11] , we chose to use immature bugs both for our chemical and behavioral experiments because they are readily available . Triatomine larvae also have the additional benefit of not emitting alarm or sexual pheromones which could interfere in case experiments were performed with adult insects [22] . Feces from third and fourth instar larvae of T . infestans , P . megistus and T . brasiliensis were collected separately in 2 ml glass vials ( 12×32 mm standard autosampler vials , Sigma-Aldrich ) by gently pressing the abdomens of bugs with forceps . A solid phase microextraction ( SPME ) fiber ( PDMS/DVB Stableflex , 65 µm , Supelco , Sigma-Aldrich ) was exposed to the headspace of the samples for 30 min at room temperature prior to analysis by gas chromatography with mass-spectrometric detection ( GC-MS Shimadzu 17A coupled to a Shimadzu 5050A ) . The desorption time in the injection port of the GC was 1 min . Helium was used as carrier gas ( 30 cm/s ) . GC injector and transfer line temperatures were 240°C and 250°C , respectively . The ionization energy was 70 eV . The oven program was 80°C for 1 min and 5°C/min to 240°C using a SupelcoWax-10 column ( 30 m×0 . 25 mm i . d . ×0 . 25 µm film; Supelco , USA ) . Tentative identification of volatiles was based on the comparison of retention indices and mass spectra with data from the literature and a spectral library ( NIST-02 ) . All tentative identifications were confirmed by co-injections with authentic standards . Samples of volatiles of T . infestans and P . megistus were first obtained three hours after the collection of feces ( i . e . , from fresh feces ) and afterwards , every 24 hours during the subsequent five days ( dry feces ) . The samples of T . brasiliensis were obtained during days 0 ( fresh feces ) , 1 , 3 and 5 ( dry feces ) . In all cases , samples of feces were collected from bugs that had been fed one week earlier . The vials with samples of feces were left open in a closed room during the study , with each species sampled separately . The relative abundance of each compound was determined using the area under the peak of the chromatogram . Empty vials without feces , placed in the same room , were used as blanks . The common substances identified in samples of feces of T . infestans , P . megistus and T . brasiliensis were selected for behavioral tests . Standards of acetamide ( Fluka ) , 2 , 3-butanediol ( Supelco ) , acetic acid ( Fluka ) , 3-methylbutyric acid and hexanoic acid ( Sigma-Aldrich ) were at least 98% pure . Individual compounds were tested with each vector species in decadic steps in dose-response assays ranging from 1 pg to 100 µg . Solutions of 2 , 3-butanediol , acetic acid , 3-methylbutyric acid and hexanoic acid were prepared in dichloromethane ( Nanograde , Mallinckrodt ) and acetamide in distilled water . All experiments were made at 25±2°C and 65±10% R . H . using a circular glass arena ( 14 cm ∅ ) where the bottom was lined with filter paper . Two pieces of flat filter paper ( 1×1 . 5 cm ) were placed on opposite halves of the arena , one impregnated with the test solution ( test ) and the other impregnated with pure solvent ( control ) . These two papers were positioned 1 cm from the edge of the arena and separated by approximately 10 cm [9] , [13] , [14] . Two control series of tests ( 32 assays per series ) were performed for each species . i ) two pieces of clean filter paper on opposite sides of the arena , in order to test for environmental asymmetries . ii ) one clean filter paper against a filter paper impregnated with dichloromethane or distilled water , in order to test for solvent effects on behavior . For each species , we performed 32 tests with each dose of the five compounds tested . In these experiments , individual insects , third instar larvae starved for 11±4 days post ecdysis , were used per test and discarded afterwards . All insects were tested during the light phase of their daily cycle , maximizing their tendency to respond to chemical signals related to shelter search . The results were analyzed by means of a binomial test . An individual insect was placed in the center of the arena using a small inverted plastic container that avoided it to climb due to its smooth surface . After 10 minutes , the insect was released by means of a string that allowed raising the container from a distance without disturbing it . One hour later , the position of the insect was recorded . Triatomines are typically found motionless when displaying aggregation inside shelters [7] and given that we aimed to evaluate the potential role of these substances for promoting aggregation , only motionless insects were considered in our analyses . A square glass arena ( 1 m2 ) lined with filter paper was used for these tests ( Figure 1A ) . Two artificial shelters made from a piece of corrugated cardboard ( 20×10 cm ) , folded to generate a 10 cm2 shelter with two lateral slits of approximately 0 . 5 cm in height [7] , were placed on opposite sides of the arena ( Figure 1 ) . In one of the shelters , a piece of filter paper ( 4×6 cm ) impregnated with a blend of the five compounds was introduced , while the other shelter contained a piece of filter paper treated with solvent as control . This shelter design ( Figure 1B ) is proven to successfully recruit triatomine bugs [6] , [7] , [23] , [24] , which tend to enter the shelters due to their strong thigmotaxis and intense negative phototaxis [25] . Fifth instar larvae starved for 11±4 days post ecdysis were used in this experiment . A group of 30 insects was released in the center of the arena two hours before the beginning of the scotophase . The illumination of the experimental room was set to a 12∶12 LL/DD regime . Two hours after the end of the scotophase , the shelters were carefully removed from the arena and the number of bugs inside each of them was recorded . Three doses of each of the five compounds ( 16 ng , 160 ng , and 1 . 6 µg ) were applied in the test shelter in three separate series of assays . These tests were performed separately for T . infestans , P . megistus and T . brasiliensis . Eight replicates were performed for each dose tested with each of the three species . Since our goal was to compare the aggregation inside baited vs unbaited shelters , insects found outside refuges were excluded from the statistical analysis because they were not aggregating . It is important to highlight that in triatomines the decision to aggregate or to remain dispersed is influenced by factors such as thigmotaxis and phototaxis , i . e . factors other than the presence of a bait inside a shelter . Therefore , an additional phenomenon would have been evaluated if all insects present in the arena were included in the analysis . The effect of the different doses of compounds on the distribution of insects in baited and unbaited refuges was analyzed by means of a Generalized Linear Model ( GLM ) using a logistic regression adapted to the binomial nature of the response variable . This analysis was followed by a Wilcoxon signed rank test with continuity correction in order to compare shelter choice data obtained with each concentration and species against a random choice between shelters . Both tests were performed in R software 3 . 0 . 2 ( R Core Team , 2013 ) .
Five compounds were common to the feces of all species studied: acetamide , 2 , 3-butanediol , acetic acid , 3-methylbutyric acid and hexanoic acid . The relative abundance and proportion of these five compounds varied during the five days of sampling for the three species: T . infestans ( Figure 2A ) , P . megistus ( Figure 2B ) and T . brasiliensis ( Figure 2C ) . In T . infestans the abundance of acetic acid and 2 , 3-butanediol increased markedly 24 h after sample collection before decreasing between the first and second day . 3-Methylbutyric acid was initially present in fresh feces and its abundance decreased until only traces were detectable after 72 h . A relatively constant low abundance of hexanoic acid was detected across the five days of sampling . Only traces of acetamide were detected ( Figure 2A ) . In samples of feces of P . megistus acetic acid was more abundant at the first and second days of sampling , a result which was consistent with our findings in T . infestans samples . This was the most abundant compound during all five days . 3-Methylbutyric acid was the second most abundant compound . For P . megistus , hexanoic acid , 2 , 3-butanediol and acetamide were detected as traces ( Figure 2B ) . In T . brasiliensis all compounds were consistently detected in very low amounts over all five days , except 2 , 3-butanediol , for which the abundance decreased steadily from the first to the fifth day ( Figure 2C ) . Each compound was tested individually for each species . All substances were capable of attracting the three species studied with the exception of 2 , 3-butanediol which did not induce any effect on P . megistus bugs ( Table 1 ) . Control tests evaluating two clean filter papers or a solvent control vs a clean filter paper presented a random distribution ( Binomial test , N . S . ) . The proportion of insects of each species found inside blend baited and control shelters is presented in Figure 3 . The proportion of insects remaining outside refuges at the end of the experiments varied according to the species . The GLM with binomial error revealed a significant effect of the bait dose on the choice for a refuge by the three species ( T . infestans: z = 3 . 11 , P = 0 . 00187 , residual deviance = 12 . 940 on 22 degrees of freedom; T . brasiliensis: z = 2 . 403 , P = 0 . 0162 , residual deviance = 25 . 084 on 22 degrees of freedom and P . megistus: z = 3 . 653 , P = 0 . 000259 , residual deviance = 13 . 669 on 22 degrees of freedom ) . Refuges associated with a mixture of 16 ng of each compound promoted the aggregation of only as many triatomines as clean shelters ( Wilcoxon signed rank test , NS , Figure 3 ) . Conversely , shelters associated with mixtures of 160 ng or 1 . 6 µg of each compound promoted a significantly stronger aggregation on T . infestans ( 160 ng V = 36; P = 0 . 0078; 1 . 6 µg , V = 36 , P = 0 . 014 ) , T . brasiliensis ( 160 ng , V = 36 , P = 0 . 0078; 1 . 6 µg , V = 28 , P = 0 . 022 ) and P . megistus ( 160 ng , V = 28 , P = 0 . 022; V = 36; P = 0 . 014 ) larvae , than clean shelters .
As initially hypothesized , the present study identified volatile compounds common to feces of three species of triatomine vectors . We combined these five substances in a blend that was capable of attracting bugs of the three species into shelters . In contrast to previous studies we focused on the identification of compounds that were readily available and common to all species . This approach was favored as it is expected to reduce the production cost of chemical baits . We found that the presence of five common compounds was consistent , but their abundance was highly variable throughout the sampling period in all cases . Previously it has been demonstrated that feces , despite this changing proportion of volatiles over time , are attractive for up to 10 days [17] . The low , dynamic abundance and high volatility of the fecal compounds warranted SPME as the sampling method . Out of the five substances selected for behavioral testing , three have not previously been identified in triatomine feces: 3-methylbutyric acid , hexanoic acid and 2 , 3-butanediol . Acetic acid and acetamide have been identified in the fecal samples of T . infestans and T . mazzotii [19] , however the biological activity of these compounds was not assessed . The results from our behavioral experiments suggest that they are all constituents of the aggregation signal from triatomine feces and reinforce the hypothesis that this aggregation signal is involved in the marking of shelters by triatomines [7]–[9] . Whether substances other than those reported here play a role in triatomine aggregation remains unclear; nevertheless this is the first report of a chemical blend identified in triatomine feces acting as an aggregation signal for Chagas disease vectors . The results obtained in shelter experiments with all the species studied in this work were very similar , which is consistent with the proposed low specificity of aggregation signals from feces of triatomines [11]–[14] , [16] . Therefore , we suggest that our five substance blend could be applied as a general triatomine bait suitable for areas with several sympatric species . Currently , the detection of domiciliary infestations in control programs is performed by manual search for triatomine bugs and/or colonization signals , such as feces , eggs and exuviae [26] . In cases of low infestation , the use of chemical dislodging agents , e . g . , 0 . 2% tetramethrin , has been introduced to induce insects to abandon their shelters and become exposed [27] . In Argentina , regularly monitored cardboard boxes that offer shelter to bugs in the walls of houses or in their peridomestic structures , have been used [28] , [29] . This type of un-baited refuge does not contain glue or insecticide in order to capture or kill visiting insects; instead the bugs find them by chance and generally choose them as a shelter due to their physical properties . The association of these devices with baits , such as the volatile mixture developed in this work , may significantly increase their detection sensitivity . Furthermore , addition of glue [30] , insecticide or pathogenic microorganisms [31] may allow transforming these devices into sensitive control tools representing a detection/capture device highly specific for triatomines . One particularly relevant application is the detection of dispersing individuals of sylvatic species that frequently re-invade houses from wild environments after insecticide spraying [32]–[38] . It is important to highlight that under the low density of current infestations in most geographic locations , detection of triatomines is extremely difficult . This limitation may affect the utilization of our mixture that needs to be evaluated under field conditions . We suggest that a long-lasting formulation which would allow a cumulative sampling of bug presence may increase the chances of effective use . In a broader context , chemical baits based on pheromones or host odors have been proposed as cost effective and environmentally benign alternative tools for detection and control of several pest insects [39] , [40] , [41] . For triatomines , an odor mixture luring the bugs into detection devices may similarly constitute a practical , economical and environmentally friendly method to monitor infestations by Chagas disease vectors . Further experiments should allow the development of a slow-release formulation for the blend , as well as demonstrate its effectiveness under field conditions . Ultimately , our blend could be developed into more advanced control tools for Chagas disease vectors , which is especially relevant where colonies have developed resistance to current insecticides [30] , [31] . | Chagas disease is a parasitic infection affecting approximately 12 million people , and is considered to be one of the most severe burdens for public health in Latin America . Control of the disease is based on attempted elimination of domestic populations of triatomine bugs , the insects transmitting the disease to humans , by means of insecticide spraying . Currently , vigilance programs monitoring triatomine reinfestation processes in houses are performed by manual search for bugs . Effective and sustainable new methods allowing continuous monitoring of domestic triatomine populations are required . Based on the fact that the insects hide in dark refuges that are marked by volatile signals emitted in their feces , we screened the feces of three species for volatile compounds common to these prominent vectors . The potential for these odors to promote triatomine aggregation was evaluated and we present evidence that a synthetic blend of these substances is capable of recruiting bugs into shelters , mimicking the natural pheromone . This blend may be used to develop a bait to monitor triatomine reinfestation processes in a similar manner as is used commonly for the monitoring of agricultural pests . | [
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] | 2014 | A Multi-species Bait for Chagas Disease Vectors |
The relationships between the infecting dengue serotype , primary and secondary infection , viremia and dengue severity remain unclear . This cross-sectional study examined these interactions in adult patients hospitalized with dengue in Ha Noi . 158 patients were enrolled between September 16 and November 11 , 2008 . Quantitative RT-PCR , serology and NS1 detection were used to confirm dengue infection , determine the serotype and plasma viral RNA concentration , and categorize infections as primary or secondary . 130 ( 82% ) were laboratory confirmed . Serology was consistent with primary and secondary infection in 34% and 61% , respectively . The infecting serotype was DENV-1 in 42 ( 32% ) , DENV-2 in 39 ( 30% ) and unknown in 49 ( 38% ) . Secondary infection was more common in DENV-2 infections ( 79% ) compared to DENV-1 ( 36% , p<0 . 001 ) . The proportion that developed dengue haemorrhagic fever ( DHF ) was 32% for secondary infection compared to 18% for primary infection ( p = 0 . 14 ) , and 26% for DENV-1 compared to 28% for DENV-2 . The time until NS1 and plasma viral RNA were undetectable was shorter for DENV-2 compared to DENV-1 ( p≤0 . 001 ) and plasma viral RNA concentration on day 5 was higher for DENV-1 ( p = 0 . 03 ) . Plasma viral RNA concentration was higher in secondary infection on day 5 of illness ( p = 0 . 046 ) . We didn't find an association between plasma viral RNA concentration and clinical severity . Dengue is emerging as a major public health problem in Ha Noi . DENV-1 and DENV-2 were the prevalent serotypes with similar numbers and clinical presentation . Secondary infection may be more common amongst DENV-2 than DENV-1 infections because DENV-2 infections resulted in lower plasma viral RNA concentrations and viral RNA concentrations were higher in secondary infection . The drivers of dengue emergence in northern Viet Nam need to be elucidated and public health measures instituted .
Dengue virus ( DENV ) infections range in severity from asymptomatic to a syndrome characterized by a haemorrhagic tendency and vascular permeability [1] . The events that precipitate endothelial cell dysfunction and vascular leak are incompletely understood . Numerous studies including several prospective cohorts [2] , [3] , [4] demonstrate that the risk of severe dengue is higher during a secondary infection with a new serotype in children [5] , [6] , [7] . Severe dengue has also been associated with high viral loads [8] , [9] , [10] , [11] , [12] , prolonged viremia [13] and high NS1 antigen levels [12] , [14] . At sub-neutralizing concentrations , dengue specific antibodies can enhance dengue virus infection of mononuclear phagocytes [15] . In addition , antibodies to pre-membrane protein appear to enhance infection of all serotypes , even when present at high concentration [16] . It has therefore been proposed that antibody-dependent enhancement ( ADE ) of viremia is a risk factor for severe dengue [1] . However , ADE may not fully account for severe dengue as several studies have found no association between severity and secondary infection in adults [7] , [17] , [18] , [19] , or severity and viral load [20] , [21] , [22] , [23] . In one study dengue virus titers were higher prior to defervescence in patients with secondary infection [8] but most other studies have found that titers are either similar or higher in primary infection [10] , [17] , [20] , [24] . Demonstration of a link between enhancing antibody levels , viral load and disease severity in humans also remains elusive . The emerging picture is that multiple factors including prior immunity , viral load , age of the patient and infecting serotype and genotype may contribute to the severity of dengue infection [2] , [3] , [25] , [26] but the nature of these interactions remains unclear . Dengue pathogenesis has largely been studied in dengue hyper-endemic regions where analysis is “confounded by a multiplicity of preexisting immunity patterns coupled with co-circulation of multiple serotypes” [3] , [27] . Studies in low transmission settings , where few dengue serotypes circulate and primary infection in adults is common , potentially offer an opportunity to better identify factors associated with severity across serotype and immunity groups . We conducted a prospective study in Ha Noi , Viet Nam , to examine the association between primary and secondary infections , serotype , plasma viral RNA concentration , and the development of dengue haemorrhagic fever ( DHF ) in a low transmission setting .
The protocol for this study was approved by the scientific and ethical committees at the National Hospital of Tropical Diseases and The Oxford University Tropical Research Ethics Committee ( OXTREC ) . Patients provided written informed consent to participate in this study . Patients were eligible for recruitment if they were admitted to the National Hospital of Tropical Diseases ( NHTD ) in Ha Noi , Viet Nam between September and November 2008 with a clinical diagnosis of dengue according to the WHO criteria [28] . These criteria were fever plus two or more of the following: headache; retro-orbital pain; myalgia/arthralgia; rash; bleeding or leukopenia . The study protocol included children but the number attending the National Hospital of Pediatrics ( NHP ) in Ha Noi during the study period was too low to warrant investigation . The NHTD is a 160 bed tertiary care center for adult patients with infectious diseases and also serves as a referral center for dengue in Northern Viet Nam . The NHP is the coordinating center for pediatric care in the country , receives patients from all Northern provinces and sees on average 40 , 000 in-patients and 350 , 000 out-patients per year . Patients were examined daily during hospitalization by a dedicated team of physicians with experience in dengue diagnosis and treatment . Signs and symptoms of hemorrhage , capillary permeability and shock along with other relevant clinical data were prospectively recorded using standardized case record forms . Ultrasound of the chest and abdomen was performed at study enrolment and additionally when clinically indicated . Full blood counts were performed daily for at least 6 days during admission , as well as at discharge and approximately 10 days after discharge . Other investigations and clinical management were at the discretion of the attending physicians . CRFs and laboratory results were reviewed to identify patients that fulfilled the WHO criteria for dengue hemorrhagic fever ( DHF ) , i . e . plasma leakage , plus thrombocytopenia and hemorrhagic signs [28] . Plasma leakage was said to be evident if pleural effusion and/or ascites were detected by ultrasound , or if a haematocrit during admission was ≥20% higher than at follow-up . If the patient did not attend follow-up , the average of follow-up values for males ( n = 60 , average = 44 ) or females ( n = 56 , average = 38 ) was used . This compares to a normal haematocrit value of 38% with a range of 35–41% set by the Ministry of Health of Viet Nam . Thrombocytopenia was defined as a platelet count less than 100 , 000/mm3 . An in-house IgM & IgG capture ELISA using antigens from DENV 1-4 and monoclonal antibodies provided by Venture Technologies ( Sarawak , Malaysia ) was performed as previously described [29] . A sample was considered IgM or IgG positive if the units were at least 6 times higher than the negative control sera . An internally controlled , serotype-specific , real-time reverse-transcriptase polymerase chain reaction ( RT-PCR ) assay [30] was used to identify the infecting serotype and determine viral RNA concentrations expressed as cDNA equivalents/ml of plasma . The sequences of the dengue serotype-specific primers and probes have been published previously [30] . They amplify and detect parts of the NS5 coding region that were first identified by Laue et al as being highly conserved within each dengue serotype [31] . The assay limit of detection was 10 cDNA equivalents per reaction . Dengue NS1 antigen was detected using a commercial ELISA ( BIO-RAD Platelia™ Dengue NS1 Ag ) according to the manufacturer's instructions . A diagnosis of confirmed dengue was made using a previously described reference algorithm [32] that has been adapted to include NS1 ELISA and remove the indirect recombinant membrane protein ELISA , which was not used in this study ( Figure S1 ) . Using this algorithm a patient is considered to have confirmed dengue if either RT-PCR or NS1 ELISA is positive , if there is an increase in the level of IgM detected by ELISA or an IgG ELISA conversion in the presence of a positive IgM ELISA . Serology was considered to be consistent with primary dengue infection if on or after day 6 of illness IgM levels were at least 1 . 78 times higher than IgG levels [33] , or with secondary infection if IgM levels were less than 1 . 2 times higher than IgG levels . Illness day was calculated from the first date that the patient recalled having fever , which was assigned as day 1 . Proportions were compared using odds ratios and Chi-Square or Fishers exact test when any expected cell count was less than 5 . Continuous variables were presented as medians and interquartile ranges ( IQR ) and compared using Kruskal-Wallis and Mann Whitney tests . Data for study patients was compared to records kept by the Ha Noi Preventive Medicine Center ( Ha Noi PMC ) , which includes age , gender , province and district of people attending government health care facilities with a clinical diagnosis of dengue by month and year . We modelled dengue severity as depending on serological definition of prior infection/immunity and infecting serotype ( and a potential interaction ) using simple and multiple logistic regression analyses . The probability of a positive NS1 result and the log-10 plasma viral RNA measurements on day 5 of illness ( i . e . the median illness day when patients were admitted ) were modelled using logistic and linear regression models , respectively , depending on serological definition of prior infection , infecting serotype and dengue severity . In a sensitivity analysis , the model was additionally adjusted for age and gender . The time from illness onset to the first undetectable NS1 and viral RNA measurement , respectively , were modelled using Weibull accelerated failure time regression models for interval censored data , i . e . patients were treated as reaching undetectable levels in the interval between their last positive and their first undetectable measurement and patients for whom the first measurement was undetectable were treated as first reaching undetectable levels between illness day 1 and the day of this first measurement . Analyses were performed with the statistical software R version 2 . 9 . 1 [34] and SPSS for Windows , Rel . 14 . 0 . 0 . 245 , 2005 ( SPSS Inc . Chicago IL . ) .
158 of 206 eligible patients consented and enrolled in an 8 week period commencing on September 16 2008 . During this time approximately 1240 clinical dengue cases attended government health care facilities in Ha Noi out of a total of 2371 for the whole of 2008 of which 975 ( 41% ) were admitted to NHTD . 139 patients were from Ha Noi province , 11 were from 4 neighboring provinces and 6 were from more distant provinces , the farthest being Nhge An , Son La and Quang Ninh which are more than 100 km from Ha Noi . None of the patients were from provinces north of Ha Noi . Laboratory diagnosis of dengue was made for 130 patients . A further 18 with probable dengue were not included in this analysis as these were recruited significantly later in their illness and 61% had already defervesced . 26% of all enrolled patients and 23% of those with confirmed dengue had been transferred from another hospital . The age and gender distribution of confirmed dengue patients ( Table 1 ) were similar to that for dengue cases attending any government health care facility in Ha Noi in 2008 ( median age 23 years , IQR 18–31 , 52% male ) . The geographic distribution was also similar to that for all reported cases in Ha Noi ( data not shown ) . Amongst confirmed cases serology was indicative of secondary infection in 61% and of primary infection in 34% ( Table 1 ) . The infecting serotype could be defined by real-time-RT-PCR for 81 patients of which 52% had DENV-1 and 48% had DENV-2 ( Table 1 ) . Viral RNA could not be detected by RT-PCR in 49 confirmed dengue patients . The median admission day for these patients was 1 day later than for those with virus RNA detectable by RT-PCR ( Table 1 ) . The E genes of 9 DENV-2 ( GenBank: GU908512- GU908520 ) and 20 DENV-1 ( GenBank: HQ591537-HQ591556 ) viruses were sequenced and belong phylogenetically to the Asian 1 genotype and Genotype I , respectively ( unpublished findings ) . Secondary infection was significantly more common in DENV-2 patients ( 79% ) compared to DENV-1 patients ( 36% , p<0 . 001 ) . Age , sex and course of illness were similar across serotype and serology subgroups ( Table 1 ) . Most patients ( 91 , 70% ) were classified as dengue fever ( DF ) and 36 ( 28% ) developed DHF , of which 5 were classified as grade I , 30 as grade II and 1 as grade III . All patients were well at discharge except one patient who was transferred to the surgical hospital . In patients with DHF platelet counts fell to their lowest levels and haematocrits increased by the greatest percentage over baseline on days 5–7 ( Figure 1 ) . DHF rates were similar for DENV-1 and DENV-2 patients ( Table 1 ) . There was a non-significant trend of higher DHF rates in patients with secondary compared to primary infection ( Odds Ratio 1 . 96 , 95% CI 0 . 80 – 4 . 85 , p = 0 . 14 ) . Results were consistent when including both serotype and serological definition of primary versus secondary infection in a logistic model and after adjusting for age and sex ( data not shown ) , and there was no evidence of an interaction between serotype and primary/secondary infection status in the development of DHF ( Likelihood ratio p = 0 . 48 ) . The proportion NS1 positive on day 5 of illness was significantly higher for DENV-1 compared to DENV-2 patients and the time to undetectable NS1 was shorter for DENV-2 ( Table 2 , Figure 2 ) . The proportion NS1 positive on day 5 was similar for patients with primary and secondary infection but the time to undetectable was shorter for secondary infection . There was no evidence of an interaction between serotype and primary/secondary infection status ( Likelihood ratio = 0 . 24 ) and effects were consistent when the analysis was additionally adjusted for age and gender . There was also no evidence of any association between NS1 and DHF ( Table 2 ) . Log10 viral RNA concentration in plasma on day 5 was estimated to be 0 . 96 lower for DENV-2 than for DENV-1 patients and the time taken until viral RNA was undetectable in plasma was estimated to be 0 . 89 times shorter for DENV-2 ( Table 3 , Figure 3 ) . Plasma viral RNA concentration on day 5 was significantly higher in patients with secondary infection , but the time taken until viral RNA was undetectable in plasma was not significantly different . Results were consistent after adjusting for age and sex ( data not shown ) and likelihood ratio tests showed no evidence of interaction between serotype and primary/secondary infection status for either viral RNA concentration on day 5 ( p = 0 . 96 ) or time taken until viral RNA was undetectable ( p = 0 . 85 ) . We couldn't establish a clear association between DHF and plasma viral RNA concentration on day 5 or time taken until viral RNA was undetectable in plasma .
Hospitalized dengue patients in Ha Noi in 2008 were predominantly adults with high rates of primary infection compared to Southern Vietnam [35] and other hyper-endemic regions [36] , [37] . DENV-1 and DENV-2 were the only serotypes identified , consistent with national dengue surveillance data for Ha Noi ( Le Quynh Mai , personal communication ) , whereas in Southern Viet Nam all serotypes were detected during clinical dengue surveillance in 2008 but DENV-1 was predominant [38] . Overall case numbers and clinical presentation were similar for DENV-1 and DENV-2 . However , while primary infection predominated amongst DENV-1 patients suggesting that a substantial proportion of adults are dengue naïve , secondary infection predominated in DENV-2 . This suggests that primary DENV-2 infections may be less likely to present clinically . A limitation of this study is that patients were recruited from only one hospital in Hanoi . However , the patients studied represented ∼12% of dengue cases seeking treatment for dengue at government health care centers in Ha Noi during the study period and had similar epidemiology . We did not include children as originally intended because only 5 were admitted to the National Hospital of Pediatrics with clinically suspected dengue while the study was being conducted . This may be expected if transmission is low such that secondary infections mainly occur in adulthood . However it is not clear why children with primary infection do not present given that 34% of adult patients had primary infection . Others report that DHF rates during primary infection are higher for adults and suggest that primary infection is more severe in adults [39] . Patients that admitted late were not precluded from this study in order to obtain a comprehensive description of clinical dengue , however this limited our ability to detect viral RNA in plasma and determine peak concentrations . The infecting serotype was unknown for 38% of confirmed dengue patients most of whom admitted 5 to 6 days after illness onset , and by day 6 viral RNA could be detected in only 39 of 100 confirmed patients tested . We suspect that DENV-2 will be the predominant infecting serotype in this group because viral RNA clearance from plasma was faster for DENV-2 than for DENV-1 patients in our study . Similar to the findings of this study , records kept since 1988 indicate that Dengue case numbers have been low in Ha Noi and that dengue has predominated in adults ( Horby P . et al , in preparation ) . This may not reflect transmission because the proportion of infections that present clinically can be low and the proportion and age-distribution depends on the prevalent serotypes and the age-associated prevalence of past infection with each serotype [3] , [40] , [41] . However , the age of clinical dengue cases generally increases with decreasing transmission intensity [42] , and the epidemiology of hospitalized dengue in this study is similar to that in Singapore where transmission has decreased due to effective vector control but the age and proportion of cases with primary dengue has increased , presumably because adults are more prone to present clinically upon primary infection [40] . Others have reported that primary DENV-2 infections are rarely symptomatic [6] , [36] , [37] , [43] , [44] . The reason for this has not been established but our data shows that viral RNA concentration is low and NS1 detection brief in the plasma of DENV-2 compared to DENV-1 patients , factors that could be considered important in disease pathogenesis leading to severe dengue . Furthermore , the relatively high prevalence of secondary DENV-2 coincided with higher plasma viral RNA concentrations in secondary infection . It is important to note that the DENV-2 viruses sequenced in this study belonged to the Asian I genotype , which has been associated with more severe disease compared to the American genotype [26] and with higher plasma viral RNA concentrations compared to Asian/American genotype [38] . While the results suggest that primary infection with DENV-1 is more likely to lead to clinically overt disease than with DENV-2 , we can not exclude the possibility that secondary infection contributes to overt DENV-1 or the possibility that DENV-2 infections are more likely to be enhanced than DENV-1 infections . The latter has been suggested elsewhere [3] and is supported by several studies showing that in children with secondary infection DHF is more common for DENV-2 compared to DENV-1 [4] , [36] . DHF was approximately twice as common in secondary compared to primary infection in our cohort , but the number of patients with DHF was small and this did not reach statistical significance or permit analysis within each serotype . However , in a study of hospitalized patients in Thailand the association between secondary infection and DHF was greater for DENV-2 than DENV-1 because DHF was less common in DENV-2 compared to DENV-1 during primary infection [34] . As in our study , this suggests that primary DENV-2 infections may be less virulent than DENV-1 . As discussed above , we suspect that DENV-2 would have been the predominant infecting serotype amongst confirmed dengue patients in which the infecting serotype was unknown . NS1 measurements and prior infection status were similar for serotype-unknown and DENV-2 patients and distinct from DENV-1 patients providing further indication that DENV-1 may be distinct in terms of virulence during primary infection . There was a non-significant trend of increased DHF in secondary infection , but 22% of DHF cases had primary infection . Thus secondary infection was not essential for DHF in this cohort . Secondary infection was associated with higher viral RNA concentration in plasma on day 5 of illness , but we did not find an association between viral RNA concentration and DHF . Interpretation of the effect of viral RNA concentration on DHF in our patients is limited by the relatively low proportion that developed DHF and perhaps also the high proportion that presented after day 3 of illness . Several of the studies that find an association between viremia and DHF recruit children within the first 3 days of illness and suggest that DHF is positively associated with peak viremia [8] , [9] , [11] . It remains controversial whether virus clearance times also differ . In one study clearance of infectious virus determined by mosquito inoculation was faster in children with DHF compared to DF [8] but studies of adults with DENV-2 or DENV-3 infection [10] , [13] and children with DENV-2 infection [24] have found longer times to virus RNA or virus-RNA containing immune complex clearance amongst those with DHF[10] , [13] . It is also possible that we did not detect an association between DHF and plasma viral RNA concentration because a relatively high proportion of our patients had primary DENV-1 and studies where DHF has been associated with viremia rarely include primary DENV-1 [9] , [10] , [11] , [13] , [14] , [24] or reported that there was no association in patients with primary DENV-1 [8] . The contribution of plasma viral RNA concentration to the development of DHF may be not be discernable in primary DENV-1 because plasma viral RNA concentration is generally high in DENV-1 , but this would imply that high viral RNA concentration alone is not sufficient to cause DHF . The time until NS1 was undetectable was longer for DENV-1 compared to DENV-2 , similar to findings of an earlier study in Southern Viet Nam [32] . We previously suggested that this reflected a predominance of primary infection in DENV-1 and that NS1 clearance is faster in secondary infection due to sequestration by IgG [32] , [45] . In the current study there were sufficient primary cases for a stratified analysis , which demonstrated that serotype is the main determinant of the sensitivity of NS1 tests , and this should be considered when interpreting NS1-based diagnostics . In conclusion our results indicate an association between secondary infection and clinically overt DENV-2 infection . Higher plasma viral RNA concentration in secondary infection may underlie the association between secondary infection and overt DENV-2 . We could not detect an association between DHF and secondary infection or plasma viral RNA concentration but this may be due to the relatively high proportion of patients with primary DENV-1 , a situation that may change if dengue emerges and the proportion and age of the population that is dengue naïve declines . The number of countries affected by dengue has increased six-fold in the last 30 years with potential for further spread through temperate , subtropical and tropical areas [46] . The Ha Noi Preventive Medicine Centre reported a 7-fold increase in the number of clinical dengue cases from 2008 to 2009 and this unforeseen epidemic overwhelmed the health system . A similar problem is faced in regions where dengue has been endemic for decades due to large multi-annual peaks in severe disease incidence [47] . Current understanding of the drivers of dengue epidemics is inadequate to predict their occurrence and inform public health prevention and preparedness measures . The dengue situation in Ha Noi provides an opportunity to further examine the roles of serotype infection sequence and prior immunity in dengue severity and emergence . | Dengue is estimated to affect 50 million people each year and can occur as explosive outbreaks that overwhelm health systems . Despite significant advances the available knowledge is not sufficient to predict the outcome of individual infections or the occurrence of epidemics . Studies from low dengue transmission settings are lacking but offer the potential to better understand the contribution of age , primary versus secondary infection and serotype because there are likely to be more adult and primary infection patients and fewer serotypes circulating compared to high transmission settings . This is the first reported study of clinical dengue in Ha Noi , the largest urban area of Northern Viet Nam . Records kept by the Preventive Medicine Center indicate that <2500 clinical dengue cases attended government health care facilities in Ha Noi each year from 1999 until 2007 . Patients in Ha Noi were older than in high transmission settings , the contribution of primary infection to overt and severe illness was greater and associations between serotype , plasma viral RNA concentration and overt and severe illness were distinct . The dengue situation in Ha Noi provides an opportunity to further examine the roles of serotype and prior immunity in dengue severity and epidemic emergence . | [
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] | 2011 | Immunological and Viral Determinants of Dengue Severity in Hospitalized Adults in Ha Noi, Viet Nam |
Neuronal firing , synaptic transmission , and its plasticity form the building blocks for processing and storage of information in the brain . It is unknown whether adult human synapses are more efficient in transferring information between neurons than rodent synapses . To test this , we recorded from connected pairs of pyramidal neurons in acute brain slices of adult human and mouse temporal cortex and probed the dynamical properties of use-dependent plasticity . We found that human synaptic connections were purely depressing and that they recovered three to four times more swiftly from depression than synapses in rodent neocortex . Thereby , during realistic spike trains , the temporal resolution of synaptic information exchange in human synapses substantially surpasses that in mice . Using information theory , we calculate that information transfer between human pyramidal neurons exceeds that of mouse pyramidal neurons by four to nine times , well into the beta and gamma frequency range . In addition , we found that human principal cells tracked fine temporal features , conveyed in received synaptic inputs , at a wider bandwidth than for rodents . Action potential firing probability was reliably phase-locked to input transients up to 1 , 000 cycles/s because of a steep onset of action potentials in human pyramidal neurons during spike trains , unlike in rodent neurons . Our data show that , in contrast to the widely held views of limited information transfer in rodent depressing synapses , fast recovering synapses of human neurons can actually transfer substantial amounts of information during spike trains . In addition , human pyramidal neurons are equipped to encode high synaptic information content . Thus , adult human cortical microcircuits relay information at a wider bandwidth than rodent microcircuits .
Human cognitive abilities clearly stand out from those of other mammals [1] . Evolutionary development of brain size , encephalization , neocortical thickening , and specialization of cortical circuits [2] , [3] most likely underlie the superior human mental capacity , but other factors may contribute as well [4] . Cognitive functions rely on appropriate relay and filtering of information and on efficient communication between brain areas . Ultimately , neuronal firing and synaptic transmission between neurons form the building blocks for coding , processing , and storage of information in the brain [5] . Synapses in particular are fundamental computational units [6]–[8] , and the increased complexity of synaptic protein networks was recently put forward as a potential correlate of mammalian cognitive ability [9]–[11] . Given the vast number of synapses in the brain , in the order of a trillion per cubic centimeter [12] , even a slight increase in efficacy of synaptic information processing could potentially translate into a substantial elevation of the brain's overall computational performance [13] . Whether human synapses are more efficient in transferring information between neurons is not known and has not been tested directly . Here , we addressed this question and studied the properties of signal transfer at unitary synaptic connections between pyramidal neurons of adult human and mouse neocortex . We then applied an information theory approach to calculate synaptic transfer performance [7] , [14] , [15] . We focused on the short-term dynamics of transmission , as synapses are not passive conveyers of information . Instead , they display prominent use-dependent plasticity , which has important roles in information processing [8] , [16] . Following chemical signal transduction at a single synapse , postsynaptic signals appear as selectively filtered versions of the train of action potentials ( APs ) that the presynaptic neuron generates [17] , [18] . Amplitudes of successive postsynaptic potentials are in fact transiently and reversibly attenuated or amplified by the context of previous pre- and postsynaptic activation . Whether or not a postsynaptic neuron fires in response to an individual presynaptic AP thus depends on the instantaneous AP frequency , on the short-term dynamical properties of each synapse , and on the previous history [8] , [17] , [19] , [20] . We found that human cortical synapses recover faster from depression than rodent cortical synapses , resulting in a substantially higher information transfer rate than in rodent synapses . In addition , we directly observed that human pyramidal neurons are equipped to encode such a high information content synaptic transmission in their output , unlike rodent pyramidal neurons , by their dynamical excitability properties .
In the rodent brain , unitary connections between neocortical pyramidal neurons show frequency-dependent short-term synaptic depression , in response to a sequence of APs [17] , [21] , [22] . To test whether excitatory connections between adult human neocortical pyramidal neurons show short-term plasticity , and whether this quantitatively resembles that in mouse neocortex , we made whole-cell recordings from synaptically connected layer 2/3 pyramidal neurons of non-pathological samples of cortex from adult human patients ( see Materials and Methods ) ( Figure 1A; Tables S1 , S2 , S3 ) [23] , [24] and mouse neocortex ( medial prefrontal cortex of young mice of 12–36 days old , and temporal association cortex of adult mice of 8–11 weeks old ) . Human monosynaptic connections showed no facilitation but only frequency-dependent depression , whose occurrence resembled that of mouse synapses ( Figure 1A–1D ) . The amount of depression during a 30 Hz presynaptic AP train did not differ between human and mouse synapses ( Figure 1D ) ( ratio last/first excitatory postsynaptic potential [EPSP]; mean ± standard error of the mean [SEM]: 0 . 38±0 . 03 human , 0 . 44±0 . 05 for young mouse synapses , and 0 . 30±0 . 04 for adult mouse synapses ) ( p>0 . 05 ) . However , at 0 . 5 seconds following the end of the AP train , the amplitude of human EPSPs had recovered to the level of the first EPSP in the train ( Figure 1B and 1E; ratio recovery/first EPSP 1 . 01±0 . 07 [500 ms between recovery and first , n = 6 , green filled diamonds] ) ; whereas mouse EPSPs were still significantly depressed ( young 0 . 67±0 . 03; adult 0 . 72±0 . 06 , p<0 . 001 ) . Moving the ninth AP closer in time to the AP train in the recordings from human unitary connections showed that already after 0 . 3 seconds the amplitude of the human EPSP had nearly recovered to the level of the first EPSP ( Figure 1E; ratio recovery/first EPSP 0 . 92±0 . 04; circles ) . To gain a full quantitative comparison of mouse and human short-term synaptic depression and recovery , we used the mathematical minimal description of activity-dependent short-term synaptic plasticity first proposed by Tsodyks and Markram [17] , [25] , and extracted best-fit model parameters for each recording ( see Materials and Methods ) . Two out of five fitted parameters were similar between mouse and human synapses ( Figure S1A and S1C ) . Those parameters that differed between human and mouse unitary synapses included a higher “U , ” which reflects the probability of synaptic release ( Figure 2A; 0 . 45±0 . 03 in human versus 0 . 25±0 . 02 and 0 . 29±0 . 03 in young and adult mice , respectively; p<0 . 001 and p<0 . 05 ) , and the cell membrane time constant , as calculated directly from the experimental traces and by the Tsodyks-Markram model ( Figure S1C and S1D ) . It is relevant to mention that the membrane time constant directly measured from experimental traces and the one calculated by the model differ slightly in definition . The model-driven observable , as extracted by the Tsodyks-Markram model , is obtained by fitting a set of two passive differential equations to the decay of the last EPSP , and for the sole instrumental aim of compensating the passive temporal summation of the successive EPSPs . Its link to the membrane biophysical properties is only indirect , its estimate confidence lower , and it has been included for the purpose of completeness . By far , the largest difference between adult mouse and human synapses was a shorter first-order kinetic time constant , which reflects the recovery from short-term synaptic depression ( Figure 2B; 144±13 ms in human versus 536±40 ms and 483±91 ms in young and adult mice , respectively; p<0 . 001 ) . These data indicate that human synapses recover at least three times faster from use-dependent synaptic depression . Similarly fast time constants of recovery have only been reported for facilitating synapses in the ferret prefrontal cortex [26] . Instead , purely depressing synapses in ferret neocortex also have long time constants of recovery of 500 ms up to 900 ms [26] , similar to those in mouse and rat neocortex [17] , [22] . Furthermore , the time constant of recovery from synaptic depression in adult human synapses ( average age 45±11 years ) did not change with age during adulthood ( Figure 2C; Pearson's correlation coefficient rho = −0 . 36 , p = 0 . 1 ) . A 3-fold faster recovery from frequency-dependent depression of synaptic connections is likely to affect information transfer between two connected neurons , when repeatedly activated during spike trains [8] , [16] , [27] . In the neocortex of awake primates , neurons fire irregularly and the instantaneous frequency of each AP varies [28] , [29] . We therefore tested whether fast recovery from depression would improve information transfer between two neurons during irregular AP trains with variable firing frequencies ( Figure 3 ) . In synaptically connected pairs of pyramidal neurons in mouse neocortex , repeated firing of the presynaptic neuron resulted in a marked reduction of the amplitude resolution by which individual EPSPs could be discerned ( Figure 3A ) . Consequently , some presynaptic APs resulted in very weak postsynaptic voltage changes ( Figure 3A and 3C ) . In contrast , in connected pairs of human cortical pyramidal neurons , all presynaptic APs led to corresponding EPSPs during repeated firing ( Figure 3B and 3C ) , and each EPSP peak amplitude remained well defined during the AP train . Plotting the relative EPSP amplitude at the same AP in the train for mouse and human synapses shows that the peak amplitude of human EPSPs remains better resolved throughout the AP train than mouse EPSPs ( Figure 3C ) . Using the mathematical model and the best-fit parameters , obtained from the short EPSP trains ( Figure 2A and 2B ) , we simulated the response to the irregular synaptic transmission with the exact same AP sequence as applied in the actual recordings: the reduction of synaptic resolution observed in the mouse experiments was replicated ( Figure 3A and 3B ) . With a faster time constant of recovery ( 144 ms instead of 500 ms ) , the simulated postsynaptic response resembled the human unitary synaptic responses , whose peak amplitude resolution was maintained throughout the AP train ( Figure 3B ) . Synapses with faster recovery from depression respond more reliably to presynaptic APs during trains of activity ( Figure 3A and 3B ) , and they may also have a larger dynamic range when signaling abrupt variations in presynaptic firing rate . To investigate this , we tested in model simulations whether fast recovering synapses show larger responses to sudden changes in the frequency of presynaptic AP trains . We simulated 1 , 500 identical , independent excitatory synaptic afferents impinging on the same postsynaptic neuron , which was modeled as a passive membrane compartment [18] , [30] . These virtual synapses were activated asynchronously by independent homogenous point processes to engage short-term synaptic plasticity . Subsequently the average activation frequency was step-changed as in a burst , to test how well synapses would detect and respond to phasic presynaptic activity [30] . Synapses with fast recovery from depression indeed conferred a higher dynamic range of synaptic transmission , as well as an increased sensitivity to small changes in presynaptic network activity time course ( Figure 3D–3F ) . As we swept through different intra- ( Figure 3E ) and inter-burst frequencies ( Figure 3F ) , the faster recovery from depression always provided the postsynaptic neurons with a larger sensitivity to their synaptic inputs . These results indicate that synapses that recover faster from depression , as we observed in human neocortical synapses , are equipped to relay fine variations in the instantaneous firing frequency , more reliably than synapses that slowly recover . As synapses that recover quickly from depression operate with an increased bandwidth during repeated activation , they may be able to relay more information than synapses that slowly recover from depression . To test this , we numerically calculated the mutual information between the amplitude of the postsynaptic membrane potentials and the length of the inter-spike intervals of a train of corresponding presynaptic APs . Using the mathematical model and the best-fit parameters ( Figure 2 ) , the Shannon's formalism of information theory [31] applied to depressing synapses [7] provided a quantitative measure for the information transfer through a synapse ( see Materials and Methods ) . We found that synapses that recover quickly from depression convey approximately four times more information at peak levels ( Figure 3G ) . The average presynaptic firing frequency , corresponding to the optimal information transfer [7] , was higher in quickly recovering synapses ( 9 . 1 Hz ) compared with slowly recovering synapses ( 4 . 5 Hz ) . Quickly recovering synapses were consequently able to sustain larger information transfer rates at higher firing frequencies ( Figure 3H ) , and information transfer rate saturated less prominently at higher frequencies than for slowly recovering synapses . These findings suggest that human neocortical depressing synapses that show fast recovery from depression may relay more information than depressing neocortical synapses found in the mouse brain . Adult human neocortical neurons receive thousands of excitatory synapses , with estimates for adult layer 2/3 pyramidal neurons as high as 30 , 000 , about twice as many as rodent layer 2/3 pyramidal neurons [32] . When each of these synapses operates with high resolution at high bandwidth and maintains reliability during bursts of activity , as our findings suggest , the question arises whether human pyramidal neurons can actually encode fast-varying temporal inputs in the AP train . To test whether human pyramidal neurons can precisely time their AP firing to rapidly changing inputs , we measured the temporal modulation of the neuronal output firing probability of human pyramidal neurons , during somatic injection of sinusoidal currents in whole-cell recordings ( Figure 4 ) [33]–[35] . Neurons simultaneously received an additional , randomly fluctuating , current component ( Figure 4A; see Material and Methods ) that induced an irregular firing regime with low average rate ( 13 . 3±1 . 6 Hz , CVISI = 1 . 06±0 . 02 , n = 13 human and 11 . 9±0 . 6 Hz , CVISI = 0 . 8±0 . 02 , n = 14 adult mouse neurons ) . While the fluctuating component per se resulted in a uniform probability of AP firing in time , the superimposed weak amplitude small sinusoidal currents modulated in time the instantaneous firing probability , with the same period of the input ( Figure 4B–4D ) . Under these conditions , the timing of AP firing in human neurons was more strongly modulated both by large and small input periods , going up to 1 , 000 cycles/s ( Figure 4E ) , indicating that human neurons could encode finer and rapidly changing temporal features of their input into AP timing . The data in Figure 4E represent typical band-pass behavior for both human ( n = 13 ) and murine cells ( n = 14 ) , where the continuous black and grey lines represent an equivalent passive analogue electronic filter . The pass band of human neurons was shifted to higher Fourier frequencies ( low frequency “pole” and “zero” cut-off located at 52 and 82 Hz , respectively , compared to 9 and 20 Hz for mouse cells ) showing a higher level of selectivity ( high frequency “pole” cut-off located at 524 and 565 Hz in humans and rodents , respectively ) . Additionally , the multiplicity but not the location of the high Fourier frequency pole differed between human and mouse cells: a value of 2 for the latter group implies that the negative slope of the Fourier transfer function , above a cut-off frequency of ∼500 Hz , is larger in mouse than in human neurons . This transfer function for adult temporal cortex mouse neurons is consistent with what was previously found for layer 5 pyramidal cells of the primary somatosensory cortex of juvenile rats [31] . Taken together , these results suggest that human neurons are more selectively tuned for high Fourier frequency components of their inputs and that their attenuation while relaying very fast signals , with Fourier components beyond 500 cycles/s , decays significantly less rapidly than in adult mouse cells . From theoretical considerations it was predicted that tracking of fast input frequencies by neurons depends on the rate of onset of APs [35] , [36] . We tested whether human neocortical pyramidal neurons have substantially faster AP onset kinetics than mouse pyramidal neurons . Single APs of human ( 26–47 years , n = 23 neurons ) and adult mouse ( 10–11 weeks , n = 12 neurons ) temporal cortex pyramidal neurons had similar waveform and duration , but different kinetic features ( Figure 5A ) . However , APs fired in trains with varying instantaneous frequencies showed strong differences between human and mouse pyramidal neurons . In particular , the rising phase of APs fired by mouse neurons slowed down more with increasing firing frequency than APs generated by human neurons ( Figures 5B , S4E , and S4F; p<0 . 005 ) . At higher firing frequencies the threshold for AP generation was elevated in mouse pyramidal neurons compared to human neurons ( Figure 5E ) . Importantly , at instantaneous firing frequencies of 1 to 30 Hz , mouse APs had reduced onset rapidity compared to APs fired by human pyramidal neurons ( Figure 5D and 5F , p<0 . 005 for all frequencies ) . A recent study reported that in order for neurons to track fast varying inputs , with Fourier components up to 1 , 000 cycles/s , the AP onset rapidity needed to be above 30 mV/ms per mV [35] . APs fired by human pyramidal neurons had mean onset rapidity values above 32 mV/ms per mV for all firing frequencies tested ( Figure 5E ) . These results show that APs generated by human neurons have a sufficiently fast onset to account for the ability of these neurons to track very fast inputs , with Fourier components up to 1 , 000 cycles/s . Our findings show that synaptic communication between human neocortical pyramidal neurons has higher bandwidth due to fast recovery from depression and that these neurons are equipped to track fast input Fourier components and encode these into timing of their spikes . Transfer of information between neurons through chemical synaptic transmission is elementary to cognition , and processes of short-term plasticity at these synapses encode information [16] . Studies on rodent excitatory cortical synapses show that short-term facilitation of synaptic strength may optimize information transfer in particular during spike bursts [27] , [37] . Based on findings in the rodent brain , it is assumed that purely depressing synapses may be better suited to transmit information for single spikes or short bursts rather than for trains of APs [16] . In contrast to these observations , we show here that purely depressing synapses in the human brain can actually transfer substantial amounts of information during spike trains , because recovery from depression is fast . We find that information transfer at depressing synapses with fast recovery is optimal at alpha band frequencies ( 8–12 Hz ) , and information transfer rate increases well into the beta and gamma band frequency range , suggesting that these synapses can be involved in active cortical computation , during cognition . This may unveil a fundamental difference with purely depressing synapses that slowly recover from depression in the neocortex of mice and other laboratory animals , which we find to have optimal frequencies of information transfer in the lower theta band range with no increase in information transfer rate at higher frequencies . These synapses may be better suited for a different range of cortical processes [38] . In our study , we did not include polysynaptic events that have been described previously [39] , and we restricted our attention to monosynaptic connections between pyramidal cells from L2/3 in the anterior medial temporal cortex , where polysynaptic events did not seem to play a prominent role . In rodent synapses , the amount and speed of short-term synaptic depression and its recovery are dependent on temperature as well as divalent ion concentrations [40] , [41] . It is at present unknown to what extent synaptic depression and recovery in human synapses depend on temperature and divalent ion concentrations . Combined with a lack of information on the actual calcium and magnesium concentrations and temperature at synapses in the brain of awake behaving mice and humans , it is not feasible to predict what the speed of recovery from depression in mouse and human cortical synapses will be in the intact brain during behavior . Nevertheless , we show here that under defined experimental conditions in which temperature and extracellular divalent ion concentrations are controlled , human and mouse temporal cortex synapses show marked differences in the speed of recovery from synaptic depression . This suggests that differences in protein complexity of synaptic protein networks between mouse and human synapses [10] may translate into different functional properties of short-term synaptic plasticity . Postsynaptically , the outcome of short-term synaptic plasticity processes is translated into AP firing to relay information [13] . The brain not only keeps track of the number of spikes occurring in large windows of time , but spike timing can have meaning down to millisecond precision [42] . Spike timing with a temporal resolution smaller than the time scales of sensory and motor signals , even at sub-millisecond levels , can encode significant amounts of visual information [42] . Cortical pyramidal neurons time their AP firing in relation to the timing of synaptic input [43] , [44] . However , populations of rodent pyramidal neurons fail to time their spiking based on rapidly varying inputs components that change faster than 200–300 cycles/s . This may suggest that , during ongoing synaptic membrane potential fluctuations , rodent neurons do not regularly encode and transmit downstream information with sub-millisecond precision . We find that populations of human pyramidal neurons can regularly time AP firing with sub-millisecond precision and that these APs maintain rapid onset kinetics , which can account for such precision of spike timing . Rapid onset kinetics of somatic APs are predicted by Hodgkin-Huxley-based models for AP generation when the spatially extended morphology of neurons and AP propagation from the axon initial segment to the soma are taken into account [45] . The observed fast onset rapidity of APs in adult human neurons can indeed partly be explained by human pyramidal neuron morphology [46] . In particular , the electrical load imposed by the large dendritic tree of adult human layer 2/3 pyramidal neurons compared to adult rodent pyramidal neurons on the axon initial segment induces a larger onset rapidity of the AP and higher frequency tracking capabilities . In conclusion , our data show that elementary circuits in the human neocortex of connected pyramidal neurons that underlie cognition can operate at a substantially higher bandwidth and temporal resolution for information encoding than rodent neurons in response to the high information content synaptic transmission they receive .
All procedures on human tissue were performed with the approval of the Medical Ethical Committee of the VU University Medical Center and in accordance with Dutch licence procedures and the Declaration of Helsinki . Human slices were cut from anterior medial temporal cortex that had to be removed for the surgical treatment of deeper brain structures for epilepsy or tumors with written informed consent of the patients ( aged 18–61 years ) prior to surgery . Anaesthesia was induced with intravenous fentanyl 1–3 µg/kg and a bolus dose of propofol ( 2–10 mg/kg ) and was maintained with remyfentanyl 250 µg/kg/min and propofol 4–12 mg/kg . Immediately following removal from the brain , neuropathologists assessed whether it was normal or diseased tissue and only those samples that were designated as normal were used in the present study . After resection , the neocortical tissue was placed within 30 seconds in ice-cold artificial cerebrospinal fluid ( aCSF ) slicing solution which contained in ( mM ) : 110 choline chloride , 26 NaHCO3 , 10 D-glucose , 11 . 6 sodium ascorbate , 7 MgCl2 , 3 . 1 sodium pyruvate , 2 . 5 KCl , 1 . 25 NaH2PO4 , and 0 . 5 CaCl2 ( 300 mOsm ) [23] , [24] , [45] and transported to the neurophysiology laboratory , which is located within 500 m from the operating room . The transition time between resection of the tissue and the start of preparing slices was less than 15 minutes . Neocortical slices ( 350–400 µm thickness ) were prepared in ice-cold slicing solution , and were then transferred to holding chambers in which they were stored for 30 minutes at 34°C and for 30 minutes at room temperature before recording in aCSF , which contained ( in mM ) : 126 NaCl; 3 KCl; 1 NaH2PO4; 1 MgSO4; 2 CaCl2; 26 NaHCO3; 10 glucose ( 300 mOsm ) , bubbled with carbogen gas ( 95% O2/5% CO2 ) . All procedures were approved by the VU University's Animal Experimentation Ethics Committee and by the Ethics Committee of the Department of Biomedical Sciences of the University of Antwerp . C57Bl6 mice ( 2–11 weeks of age ) were decapitated prior to slice preparation , in accordance with Dutch and Belgian licensed procedures . Brains were rapidly removed and dissected using the same solutions for slicing and storage as used in preparation of human brain slices . Coronal slices ( 300–450 µm thickness ) were cut from the prelimbic region of the medial prefrontal cortex ( mPFC ) ( P12–36 ) or the temporal association cortex ( TC ) ( 8–11 weeks ) . As during preparation of human brain slices , slices were allowed to recover for 30 minutes at 34°C followed by 30 minutes at room temperature in the same solution used for recording . Slices of adult mice were allowed to recover for 15 minutes at 34°C in the same solution used for slicing and then transferred to a chamber containing the same solution used for recording , at room temperature . Neocortical slices were visualized using either infrared differential interference contrast ( IR-DIC ) microscopy or Hoffman modulation contrast . After the whole cell configuration was established , membrane potential responses to steps of current injection were used to classify each cell electrophysiologically . Cells were loaded with biocytin through the recording pipette for post hoc identification . All experiments were performed at 32°C–35°C . None of the neurons recorded from showed spontaneous epileptic-form spiking activity . All experiments were performed in the absence of blockers of GABAergic synaptic transmission . Recordings were made using Multiclamp 700A/B amplifiers ( Axon Instruments ) sampling at intervals of 4 to 100 µs , and low-pass filtered at 10 to 30 kHz . Recordings were digitized by pClamp software ( Axon ) , by LCG software [47] , or custom written scripts in Igor Pro , and later analyzed off-line using custom written Matlab scripts ( The Mathworks ) . Patch pipettes ( 3–5 MOhms ) were pulled from standard-wall borosilicate capillaries and filled with intracellular solution containing ( in mM ) : 110 K-gluconate; 10 KCl; 10 HEPES; 10 K-phosphocreatine; 4 ATP-Mg; 0 . 4 GTP , pH adjusted to 7 . 2–7 . 3 with KOH; 285–290 mOsm , 0 . 5 mg/ml biocytin . Post hoc visualization and neuron identification using biocytin labelling was performed as described previously [48] , [49] . Pyramidal neurons were classified based on morphological and electrophysiological criteria . Input resistances were calculated from the steady state response to hyperpolarizing current pulses ( mean ± SEM ) : human Rin = 70±6 MΩ ( n = 27 ) , young mouse Rin = 84±3 MΩ ( n = 45 ) , adult mouse Rin = 102±7 MΩ ( n = 26 ) ( adult mice significantly different from human , p<0 . 01 ) . Resting membrane potentials ( not corrected for liquid junction potentials – mean ± SEM ) : human Vrest = −73 . 2±0 . 8 mV ( n = 27 ) , young mouse Vrest = −69 . 6±0 . 7 mV ( n = 45 ) , adult mouse Vrest = −74 . 5±1 . 1 mV ( n = 26 ) ( adult mice and human significantly different from young mice ( p<0 . 02 ) , but not different amongst each other ) . These numbers were taken into account when injecting current to test whether human pyramidal neurons can time their AP firing to high frequency inputs . The baseline current injected was set to keep iso-frequency firing close to 10–15 Hz . The model of Tsodyks and Markram [17] , [22] , [25] was employed to quantitatively characterize use-dependent short-term depression of EPSPs amplitude in response to defined trains of presynaptic APs . This description refers to the existence of generic resources for neurotransmission , without distinguishing between presynaptic ( e . g . , the ready-releasable pool of vesicles ) and postsynaptic biophysical components ( e . g . , desensitization of AMPA receptors ) . The model is identified by five numerical parameters [50]: A , the absolute synaptic efficacy; U , the fraction of resources consumed by each AP; , the time constant of recovery from exhaustion of available resources; , the synapses' time constant to transit between active and inactive states; , the membrane time constant , as defined in a leaky integrate-and-fire model . The peak amplitude of the nth postsynaptic response , indicated by En , is given by En = ( A U Rn ) , where the dynamical variable R is the running value of the available resources . Indicating the times of successive APs , as , the model responses are obtained by upon numerical iteration: . The same numerical method was employed for both simulating model responses , as well as to search for parameters {A , U , τ} that optimally reproduce the experimental data after least-square fitting . A passive R-C circuit was mathematically defined to mimic temporal integration of postsynaptic responses in a point-neuron with membrane time-constant of 10 msec . Then , 1 , 500 identical synaptic afferents impinging on this neuron were activated , each by an independent realization of an identical Poisson point-process . The mean frequency of this random process was step-changed from f1 to f2 , after several seconds of simulation lifetime . Each individual model synapse relayed the occurrence of a presynaptic AP in a use-dependent manner , according to the Tsodyks-Markram model described above . Without losing any generality , simulated postsynaptic responses were expressed and plotted in arbitrary units , normalizing voltage responses to the product of the ( unspecified ) neuronal input resistance and maximal synaptic efficacy A . As an alternative to the Tsodyks-Markram model , we considered its non-deterministic formulation , which combines the classical quantal model [51] , [52] with use-dependent short-term depression as in Fuhrmann and colleagues [7] . We considered n = 5 release sites [24] and the average quantal content A/N , with A being the maximal synaptic efficacy of the deterministic Tsodyks-Markram model . The last choice implies that on the average of many repeated trials , the non-deterministic model responses quantitatively correspond to the predictions of the Tsodyks-Markram deterministic formulation . The coefficient of variation of the quantal content was set to 0 . 4 , choosing its standard deviation as 0 . 4 A/N . The coefficient of variation's value was taken from an example in the literature [7] and its numerical value scales proportionally the mutual information calculations and thus does not affect our conclusions . To demonstrate the previous statement we explored different values of CV of the simulated quantal content ( i . e . , 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 ) ( see Figure S2 ) . The parameter has , therefore , no qualitative effect , but only a scaling effect . As opposed to a classic quantal model , the probability of release is non-stationary , and it is computed as the product between the fixed probability that a release site contains a vesicle ( U ) and the probability Pv ( t ) that a vesicle is available at a given time t . In the lack of any presynaptic AP , Pv ( t ) recovers exponentially to 1 with a recovery time-constant τ , while immediately after an AP this probability is decreased by a proportional amount , . This model allows one to apply information theoretical methods [15] , extended to probabilistic synaptic transmission , and lead to quantify mutual information between the set of postsynaptic responses to a train of presynaptic spikes , and the corresponding set of interspike intervals [7] . The last are assumed to act as a source of ( arbitrary ) temporal information . Several average presynaptic firing frequencies were considered ( 0 . 01–100 Hz ) . For each average frequency , a realization of a Poisson point-process was generated to simulate the time of occurrence of 10 , 000 presynaptic spikes fired at such an average frequency . The marginal probability density of the postsynaptic amplitudes was estimated , and the corresponding conditional probability density , given the instantaneous probability of release , derived under the same assumptions of Fuhrmann and colleagues [7] . Conditional entropies were computed according to the definition of Shannon [31] , and mutual information computed as their difference . To evaluate the dynamical transfer properties of neuronal discharge , in response to rapidly varying inputs , a sinusoid of amplitude I1 and frequency F ( 1–1 , 000 cycles/s ) was applied simultaneously to a DC baseline I0 and to a randomly fluctuating waveform , under current-clamp stimulation: ( 1 ) The fluctuating component Inoise ( t ) was synthesized as an exponentially filtered Gaussian white-noise realization , mimicking at the soma the consequences of a barrage of balanced background excitatory and inhibitory irregular synaptic inputs , as described previously [26] , [31] , [32] , [53] . Inoise ( t ) had zero-mean , variance s2 and correlation length τI = 5 msec , and was generated by means of the LCG software [47] iterating the following expression at the same rate of the sampling interval ( i . e . , 1/dt = 20 kHz ) , ( 2 ) where {ξt} is a sequence of independent pseudo-random numbers with normal distribution [54] . Depending on the cell input resistance and rheobase , the current DC baseline I0 and the random fluctuation amplitude s were adjusted so that for I1 = 0 pA , neurons responded ( i ) with low mean rate ( ∼10–15 Hz ) , ( ii ) highly irregular inter-spike intervals , and ( iii ) subthreshold voltage random fluctuations ( 5–10 mV ) as observed in cortical recordings in vivo [53] . Finally , I1 was chosen as a fraction of I0 ( e . g . , I1 = 50 pA , I1 = 400 pA ) and each stimulation I ( t ) lasted 50 s and was followed by a long recovery time of at least 50 s . Raw voltage traces were recorded for different values of F and offline processed in MATLAB ( The Mathworks ) . Individual spike times {tk} , k = 1 , 2 , 3 , … , occurring across subsequent input oscillation cycles , were extracted by a peak-detection algorithm and then normalized to the corresponding oscillation period: i . e . , tk→ ( tk % F−1 ) , where % indicates the remainder of integer division . Peristimulus time histograms ( PSTH ) with 30 bins , were then computed and normalized to represent the instantaneous discharge rate . Three free parameters r0 , r1 , and φ of the sinusoidal function ( 3 ) were optimized to best-fit in the least-squares sense each PSTH by r ( t ) , through the Levenberg-Marquardt algorithm [54] . The same procedure was repeated on surrogate spike-train data , obtained randomly shuffling the interspike-intervals { ( tk+1−tk ) } to obtain the minimal level of significance for the estimates of r1 and φ . To fit experimentally measured amplitude and phase response data , we used a linear model , as described in [33] , reminiscent of a passive analogue electronic filter . Briefly , the modulation depth r1 ( f ) /r0 and the phase were taken as the magnitude and phase of the impulse response of a linear dynamical system described , in the Fourier domain , by the following rational complex function: ( 4 ) where the polynomials roots and are known as “poles” and “zeros” cut-off of the transfer function , respectively , and A is the low-frequency gain . The transfer function in Equation 4 was used to fit the magnitude and phase responses over the population of cells , as shown in Figure 4E , and the fit was weighted by the inverse of the standard deviation of each data point . The two datasets , corresponding to human and adult mouse cells , were fit with functions containing a single zero ( i . e . , M = 1 ) , and either two or three poles ( i . e . , N = 2–3 ) . In both cases , N>M accounts for the power-law decay of the magnitude of the transfer function at frequencies above the cut-off frequency according to the relation ( 5 ) With . To model the fact that the phase response does not saturate for high Fourier frequencies at integer multiples of [34] , we included in our formulation a constant propagation delay , which takes into account , among other things , the time it takes for the spike to travel from its originating zone to the point at the soma where it is recorded ( see [33] , [34] for a discussion ) . In experiments aimed at examining AP-waveform ( Figure 5 ) , data were acquired with 4 or 10 µs sampling intervals , low-pass filtered at 30 kHz , and filtered offline at 15 kHz . Bridge balance was adjusted manually and pipette capacitance was compensated for . Recordings were excluded if bridge balance exceeded 12 MOhm . For analysis , all APs with instantaneous firing frequencies up to 30 Hz were pooled and binned for all recordings from a cell . Traces with resting membrane potentials above −60 mV and APs where the linear fit to obtain onset rapidity had R∧2 values <0 . 95 were excluded from analysis . The various AP parameters were calculated for each AP in a train , as follows: the AP threshold was defined as the membrane potential at the point that the velocity of the AP exceeded 10 mV/ms [55] . The AP peak voltage was determined as the absolute membrane potential measured at the peak of the AP waveform . The AP amplitude was calculated as the difference in membrane potential between the AP threshold and the AP peak voltage . Maximum rate of rise was defined as the maximum dV/dt value reached during the AP ( calculated between adjacent points ) . Onset rapidity was defined as the slope of a linear fit to the AP phase plot ( dV/dt versus V , with unit 1/ms ) at dV/dt = 30 mV/ms . The first AP in a train was considered a single AP . For all APs that followed , the instantaneous AP firing frequency was calculated as: 1/ ( time since previous spike ) . For subsequent analysis , APs with instantaneous firing frequencies up to 30 Hz were binned in frequency bins of 5 Hz . For each neuron , the mean value of a given AP parameter in a frequency bin was then obtained by averaging over all APs falling in that frequency bin . AP amplitude adaptation was calculated by dividing the mean amplitude of APs in a frequency bin by the mean amplitude of single APs . Maximum rate of rise adaptation was calculated by dividing the mean maximum rate of rise of APs in a frequency bin by the mean maximum rate of rise of single APs . Threshold variance was calculated as the standard deviation of AP thresholds for all APs within a frequency bin . Differences in AP features between human and mouse neurons were tested for significance using independent samples t-tests , with a Bonferroni corrected p-value to account for family-wise error rate . The surgeon obtained tissue samples from human temporal cortex in variable forms , in a patient-dependent manner . We could reliably determine the location of the pia and the white matter to adjust the slice angle to maintain the apical dendritic tree within the slice , but had less control of the slicing orientation on the coronal/sagittal axis and relative to individual gyri ( unlike in mouse brain , where landmarks such as midline or corpus callosum help in positioning the sample ) . Given these factors , we have not conducted a systematic analysis of connection probability between mouse and man to provide reliable estimates and comparisons of synaptic connectivity ratios between species . Rather , we focused only on finding direct monosynaptic connections for investigation and subsequent analysis . Data discussed in this paper has been deposited in the Dryad repository: http://doi . org/10 . 5061/dryad . 3723p [56] . | Our ability to think , memorize information , and act appropriately depends on circuits of connected neurons in the brain . In these circuits , neurons pass information to each other using electric pulses ( action potentials ) that cause the release of chemical neurotransmitters , which alter the membrane electric potential of receiving neurons . Based on the inputs neurons receive , they decide whether to transmit action potentials to other neurons in the circuit to pass on information . During sequences of repeated information transfer , synaptic connections between two neurons temporarily become weaker by synaptic depression . Our knowledge of neuronal information transfer is based on rodent neurons . The properties of synaptic information transfer and synaptic depression in humans are not known . Here , we show that adult human neurons can transfer information with up to ten times higher rates than mouse neurons , because of a three to four times faster recovery from depression . Furthermore , we found that human neurons can respond faster to synaptic inputs , owing to faster initiation of action potentials . Human neurons can thereby reliably encode high input frequencies in their output . Thus , neuronal information transfer can have a substantially higher bandwidth in human neocortical circuits than in rodent brains . | [
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] | 2014 | High Bandwidth Synaptic Communication and Frequency Tracking in Human Neocortex |
The Plasmodium falciparum erythrocyte membrane protein 1 ( PfEMP1 ) family plays a central role in antigenic variation and cytoadhesion of P . falciparum infected erythrocytes . PfEMP1 proteins/var genes are classified into three main subfamilies ( UpsA , UpsB , and UpsC ) that are hypothesized to have different roles in binding and disease . To investigate whether these subfamilies have diverged in binding specificity and test if binding could be predicted by adhesion domain classification , we generated a panel of 19 parasite lines that primarily expressed a single dominant var transcript and assayed binding against 12 known host receptors . By limited dilution cloning , only UpsB and UpsC var genes were isolated , indicating that UpsA var gene expression is rare under in vitro culture conditions . Consequently , three UpsA variants were obtained by rosette purification and selection with specific monoclonal antibodies to create a more representative panel . Binding assays showed that CD36 was the most common adhesion partner of the parasite panel , followed by ICAM-1 and TSP-1 , and that CD36 and ICAM-1 binding variants were highly predicted by adhesion domain sequence classification . Binding to other host receptors , including CSA , VCAM-1 , HABP1 , CD31/PECAM , E-selectin , Endoglin , CHO receptor “X” , and Fractalkine , was rare or absent . Our findings identify a category of larger PfEMP1 proteins that are under dual selection for ICAM-1 and CD36 binding . They also support that the UpsA group , in contrast to UpsB and UpsC var genes , has diverged from binding to the major microvasculature receptor CD36 and likely uses other mechanisms to sequester in the microvasculature . These results demonstrate that CD36 and ICAM-1 have left strong signatures of selection on the PfEMP1 family that can be detected by adhesion domain sequence classification and have implications for how this family of proteins is specializing to exploit hosts with varying levels of anti-malaria immunity .
Plasmodium falciparum erythrocyte membrane protein 1 ( PfEMP1 ) is a clonally variant adhesion protein that mediates binding of infected erythrocytes ( IE ) to blood microvasculature and other host cells [1] . Adherence of IEs to microvascular endothelium is a major virulence factor and , in conjunction with the related phenomenon of rosetting with uninfected erythrocytes , prevents parasitized erythrocyte circulation to the spleen where parasites may be destroyed [2] . Each parasite strain encodes ∼60 PfEMP1 proteins , or var genes , which are expressed in a mutually exclusive fashion [3] , [4] . Switches in var gene expression enable infected erythrocytes to evade host immunity and may modify disease manifestations by changing parasite binding tropism [5]–[7] . Efforts to unravel the role of PfEMP1 proteins in disease are complicated by the vast diversity of var genes . Each parasite has a diverse repertoire of genes , and there is limited overlap of repertoires between parasite genomes [8]–[10] . However , genes can be classified into three main subfamilies denoted Groups A , B , and C [11] , plus three unusual strain-transcendent variants ( var1csa , var2csa , and type 3 var ) [12]–[15] . The var gene subfamilies possess distinctive upstream flanking regions termed UpsA , UpsB , and UpsC and are found in characteristic locations in the subtelomeric or central regions of chromosomes [4] , [9] , [11] , [12] . It has been hypothesized that var gene organization may contribute to a gene recombination hierarchy that influences gene function and evolution [1] . A number of studies have sought to correlate specific parasite adhesion traits with disease outcome [16]–[19] . To date , at least 12 host receptors have been reported to mediate P . falciparum IE binding [20] . CD36 binding is the most common adhesion trait in the parasite population , followed by intercellular adhesion molecule 1 ( ICAM-1 ) [17] , [19] . These two receptors can synergize under flow conditions to mediate infected erythrocyte binding to microvasculature endothelium [21]–[23] . Most other binding properties appear to be rarer or have not been studied in more than one or a few parasite isolates . ICAM-1 binding has been associated with cerebral malaria in some studies [17] , [24] , but not in others [19] , [25] . In addition , infected erythrocyte rosetting , or binding of parasitized red blood cells to uninfected red blood cells , has been associated with disease severity in African children [26]–[28] . The clearest disease association is placental malaria , in which parasites express the unusually strain-transcendent VAR2CSA PfEMP1 protein and adhere to chondroitin sulfate A ( CSA ) in the placenta [14] , [29] . VAR2CSA is a leading candidate for a pregnancy malaria vaccine and a paradigm for syndrome-specific anti-disease vaccine efforts . Although the molecular basis for other adhesion-based complications of P . falciparum is less established than for pregnancy malaria , several observations suggest the antigenic diversity of severe malaria isolates may also be limited . For instance , immunity to severe malaria appears to be acquired after relatively few infections [30] , [31] . In addition , isolates from severe malaria cases appear to express a relatively restricted variant antigen surface repertoire [32]–[34] . Furthermore , seroepidemiological and var transcriptional profiling studies suggest that UpsA variants are more commonly expressed in young African children with limited immunity and in severe malaria infections [35]–[39] . Therefore it is possible the UpsA group has become specialized to exploit individuals with limited anti-malaria immunity , and it is important to understand what may account for this expression profile . To gain insight into PfEMP1 binding properties , sequence classification has been performed [40] . The extracellular binding region of PfEMP1 proteins is comprised of 2–7 receptor-like domains called Duffy Binding-Like ( DBL ) and Cysteine Rich Interdomain Region ( CIDR ) [41] , [42] . DBL and CIDR domains are classified into different major types ( α to ε ) and sub-types by sequence criteria [10] , [40] . PfEMP1 proteins can be further subdivided by protein architecture into small proteins with a four-domain extracellular binding region ( DBL-CIDR-DBL-CIDR ) and large proteins with a more complex domain composition [43] . By comparison to other groups , nearly all of the UpsA proteins are in the large protein category [9] , [10] . The best characterized binding interactions are between CIDR::CD36 and DBLβ::ICAM-1 [44]–[46] . In a repertoire-wide binding comparison with CIDR recombinant proteins , the majority of proteins encoded CD36 binding function , except for the UpsA group , which had different CIDR sequence types than the UpsB and UpsC groups [11] , [12] , [46] . UpsA proteins may also be under less selection to bind ICAM-1 , as 7 of 23 DBLβ domains from the IT4 parasite strain bound ICAM-1 , but none of the 9 DBLβ domains tested from the UpsA group were ICAM-1 binders [44] . However , using a different binding analysis in a BioPlex system , only a single DBLβ recombinant protein from the 3D7 parasite strain bound ICAM-1 , and it was from an UpsA protein [45] . UpsA proteins have also been reported to bind ICAM-1 ( PFD1235w ) and PECAM-1 ( PF11_0008 ) [47] . Taken together , sequence and binding analysis suggest the UpsA group forms a preferential gene recombination group that is under less selection to bind the primary microvasculature receptor CD36 . Furthermore , it is possible UpsA genes may have evolved specialized binding properties that contribute to their preferential expression in the malaria non-immune . While sequence and binding analysis of isolated domains have provided significant insights into PfEMP1 function , few binding predictions have been confirmed for native proteins at the IE surface , and it is not yet established whether binding differences truly exist between var gene subfamilies . Furthermore , it is possible that recombinant protein binding properties may be modified by adjacent domains [48] or may not extrapolate to the native PfEMP1 molecule [49] . Thus , there remain significant uncertainties in our ability to predict IE binding , and there is still limited understanding of how host selection is shaping the PfEMP1 variant antigen repertoire for parasite survival and transmission . For this study , we generated a large panel of cloned parasite lines from the cytoadhesive IT4/FCR3 parasite strain and selected three highly enriched UpsA parasite lines with specific monoclonal antibodies . This panel was employed to both investigate the major host selection binding pressures operating on the protein family and to evaluate binding predictions based on sequence information and isolated domain binding assays .
To create a panel of parasites for phenotypic analysis , parasites were cloned from a long-term , continuous culture of the IT4/25/5 clone A4 ( Figure 1 ) [6] . The IT4/25/5 ( IT4 ) parasite genotype is unusual because the parasite maintained its cytoadhesion capabilities after in vitro adaptation [50] , [51] , making it a primary model for this virulence determinant . A set of 54 var genes has been reported from the IT4 parasite genotype [9] , [10] . The A4 cloned parasite line expresses a var gene ( A4var/IT4var14 ) that has an unusually high switch frequency ( ∼1–2% per generation ) [6] , [52] , resulting in PfEMP1 heterogeneity at the population level in the long term culture . After 70 parasite divisions in continuous culture , the long-term A4 culture had completely switched away from the A4var gene ( IT4var14 ) and expressed a mixture of different var genes at low levels with IT4var26 , IT4var31 , and IT4var37 predominating ( Figure 2 ) . Both IT4var31 ( previously referred to as C18var ) and IT4var37 ( previously referred to as AFBR6 ) were also found to be common switch events in two previous studies of var gene switching within the A4 parasite lineage [7] , [52] , suggesting that these particular genes may have high “on” rates in unselected cultures . Initially , 17 subclones were isolated from the long-term A4 parasite culture by limited dilution cloning ( Figure 1 ) . From var transcription profiling , 6 of the subclones transcribed IT4var31 as either the primary or secondary var transcript , 8 transcribed dominant var gene transcripts distinct from each other , and a dominant var transcript ( present at greater than 50% of the total var transcripts ) could not be identified in 3 of the subclones by qRT-PCR analysis ( Table 1 , and data not shown ) . Ten subclones that primarily expressed single dominant var transcripts , including two that expressed IT4var31 , were selected for phenotypic analysis ( Figure 3 ) . Of interest , there was negligible UpsA transcription in the long-term A4 culture ( Figure 2 ) , and none of the isolated subclones expressed an UpsA var gene ( Figure 3 ) . To attempt to enrich for UpsA variants , the long-term A4 culture was panned on CD36 receptor protein and non-adherent parasites were selected . Although the var transcriptional profile was modified after CD36 negative selection , this approach did not enrich for UpsA variants . Instead , the frequent switch variant IT4var31 was the resulting major transcript ( data not shown ) . This again indicates that UpsA genes are rare switch events in long-term A4 cultures . To create a more representative panel for phenotypic analysis , six previously isolated parasite lines from the IT4/FCR3 strain and three UpsA parasite lines from different parasite strains ( IT4/FCR3 , Palo Alto 89F5 , and 3D7 ) ( Figure 1 ) were included in the binding studies . The three UpsA parasite lines ( R29 , VarO , and Pf13 ) were isolated by rosette enrichment and selected for high purity using specific monoclonal antibodies to the respective PfEMP1 proteins [53] . Altogether , 19 parasite lines were examined representing all three major var gene groups . Three of the parasites in the panel expressed an UpsA protein as the dominant var transcript , ten expressed an UpsB var gene , three expressed an UpsC var , one expressed the unique UpsE linked transcript ( IT4var4 , var2CSA ) , and for one parasite , the Ups category of the dominant var transcript has yet to be determined ( Figure 4 ) . The remaining parasite in the panel , 2G2 , is knobless and was employed as a negative binding control ( Table 1 ) [54] . Most parasites in the panel expressed distinct dominant var transcripts , except two subclones ( P6G2 and P5B6 ) expressed IT4var31 , and two others ( P6A8 and 4E12 ) expressed IT4var37/AFBR6 as either the dominant or secondary var transcript ( Figure 3 and Table 1 ) . To confirm the presence of knobs on the IE surface , which are known to be important in PfEMP1 anchoring and infected erythrocyte binding [41] , [54] , [55] , parasites were tested for transcription of the knob associated histidine rich protein ( kahrp+ ) by RT-PCR and floated by gelatin sedimentation ( gelatin+ ) . All parasites in the panel were positive in both assays , except for the negative control 2G2 parasite line , which lacks kahrp and therefore sedimented in gelatin . In addition , the three rosette-forming UpsA parasites all transcribed kahrp but sedimented in gelatin because they were originally isolated on the basis of their property to sediment in Ficoll ( Table 1 ) . To confirm the identity of var gene transcription at the time of binding assays , RNA was harvested within the same growth cycle that binding assays were performed . For these assays , thawed parasite stabilates were grown for 4 to 5 cycles to generate sufficient parasite material , and parasites were generally analyzed a total of 18–20 cycles from initial parasite cloning . In general , the dominant var transcript did not change between the initial qRT-PCR characterization performed after limited dilution cloning and the second round of -typing done at the binding assay ( Table 1 ) . In only one parasite line , P6G2 , the previous dominant transcript was replaced by the secondary var transcript that was present before freezing ( Table 1 ) . At the time of the binding analysis , the average fold transcription of dominant var transcripts relative to the asl housekeeping gene was 14 . 2 ( range 2 . 8–28 . 1 ) . Furthermore , most parasite lines were significantly enriched for a single predominant var transcript ( Figure 3 ) , and only 8 parasite lines contained a secondary var transcript at greater than 5% of the total var transcripts ( Table 1 ) . In most cases , the secondary transcript was present at much lower levels than the dominant var transcript . Thus , var gene transcription was stable over the short-term culture period used to perform these assays . For the three UpsA variants , PfEMP1 expression was established by flow cytometry with specific monoclonal antibodies to be 79% or higher using conservative gating criteria ( Figure S1 ) . Furthermore , all three lines formed rosettes in O-type RBCs: R29 ( rosetting rate = 37% , 89% mAb reactivity R29 ) , VarO ( rosetting rate = 73% , 79% mAb reactivity VarO ) , Pf13 ( rosetting rate = 52% , 94% mAb reactivity Pf13_0003 ) . Therefore , all of the parasites in the panel were highly homogenous for one or two var transcripts , and UpsA parasite lines were highly pure for a single expressed PfEMP1 variant . To investigate whether infected erythrocyte binding to CD36 could be predicted from sequence information and binding studies of isolated CIDR domains [46] , the complete panel of parasite lines was analyzed for binding to both CHO745-CD36 and immobilized CD36 recombinant protein . Because rosettes of uninfected red blood cells can interfere with binding or make bound IEs more susceptible to disruption during washing stages , the rosettes of the three UpsA variants were first disrupted using heparin sulfate prior to binding analysis . Previous work has shown that sulfated glycoconjugates can enhance binding to CD36 on cell surfaces [56] . Therefore , as a control for the three rosetting parasite lines , all of the parasites in the panel were treated with heparin sulfate and tested for binding to immobilized CD36 recombinant protein . Heparin sulfate treatment greatly diminished rosette formation in the three UpsA parasite lines ( <10% ) ( Figure S2 ) , but had minimal effect on infected erythrocyte binding to immobilized CD36 recombinant protein . Overall , parasites had comparable binding levels in the presence or absence of heparin sulfate ( Figure S2 ) . In addition , two non-rosetting , CD36 binding parasite lines ( A4ultra and ItG-ICAM-1 ) were tested for binding to CHO745-CD36 cells in the presence or absence of heparin sulfate . Similar to what has been reported previously [56] , sulfated glycoconjugates increased IE binding to CHO745-CD36 ( Figure S3 ) . Because heparin sulfate may slightly enhance IE adhesion to CHO-CD36 and did not modify IE adhesion to immobilized CD36 , the binding assay was then repeated for all of the non-UpsA parasites in the absence of sulfated glycoconjugates . In contrast , binding of the three UpsA lines to CHO-CD36 and immobilized CD36 was repeated in the presence of sulfated glyconjugates to prevent infected erythrocyte rosetting interfering with the binding results . Overall , there was a significant correlation between CHO745-CD36 and spotted CD36 protein formats ( Figure 5 , Spearman's Rho = 0 . 75 , p<0 . 001 ) . Although the level of CD36 binding varied between parasite lines , most of the parasites bound CD36 , with the exception of UpsA/E groups ( Figure 6 ) . The three UpsA parasites were at the lower spectrum of CD36 binding in both cell and recombinant protein binding assays , and were basically indistinguishable from the negative control , knobless parasite line , and the UpsE parasite line that does not bind CD36 ( Figure 6 ) . Furthermore , CD36 binding was highly predicted by the type of CIDR1 domain in the PfEMP1 head structure ( Figure 4 ) . Indeed , only two parasites in the panel that were predicted to bind CD36 did not bind to CHO-CD36 cells . However , both exceptions ( P4H12 and P3G5 ) bound at a low level to 50 µg/mL rCD36 , but not to 5 µg/mL rCD36 ( Figure 5 ) , and therefore may be lower affinity CD36 binders . In group-wide comparisons , UpsB and UpsC had a higher mean CD36 binding level than UpsA . This difference was significantly different in the immobilized CD36 binding assay and between the UpsC and UpsA groups in the CHO-CD36 assay , and just missed significance between the UpsB and UpsA groups in the CHO-CD36 assay ( Figure 6 ) . Taken together , infected erythrocyte binding was highly predictable based on the type of CIDR domain ( Figure 4 ) , and the UpsA group appears to be under less selection to bind CD36 . To test whether ICAM-1 binding was associated with larger PfEMP1 proteins containing DBLβ domains [44] , the parasite panel was analyzed for binding to CHO745-ICAM-1 and recombinant ICAM-1 protein . Again , to prevent rosettes from interfering with the binding analysis , the three UpsA parasite lines were treated with sulfated glycoconjugates prior to binding analysis , and as a control , two non-rosetting , ICAM-1 binding parasite lines ( A4ultra and ItG-ICAM-1 ) were tested for ICAM-1 binding in the presence or absence of sulfated glycoconjugates . Sulfated glyconjugates reduced binding of A4ultra in the CHO745-ICAM-1 assay and binding of both parasite lines to spotted ICAM-1 recombinant protein ( Figure S3 ) , similar to what has been reported before [56] . Because of the potential for sulfated glyconjugates to interfere with ICAM-1 binding in the cell and recombinant protein assays , the three UpsA parasite lines were not considered in the ICAM-1 binding analysis . In the cell binding assay , two parasite lines bound at a high level ( >2 IEs/CHO745-ICAM-1 ) , three bound at moderate level ( 0 . 5–2 IEs/CHO745-ICAM-1 ) , and the remaining parasite lines bound at a low level or did not bind ICAM-1 ( Figure 5 ) . While there was good consistency between the cell and recombinant protein assays for the two high level ICAM-1 binders , there was more discordance for weaker ICAM-1 binders ( Figure 5 ) . Only three parasite lines bound ICAM-1 in both platforms ( 3G8 , ItG-ICAM-1 , and A4ultra ) , and two parasite lines that bound at a moderate level to CHO745-ICAM-1 did not bind to immobilized ICAM-1 protein ( Figure 5 ) . Notably , both parasite lines express the IT4var31 transcript , which has been suggested to be a weaker ICAM-1 binding variant that is trypsin-resistant [57] , [58] . To confirm whether binding was trypsin-resistant , P5B6-infected erythrocytes expressing IT4var31 were treated with 1 mg/mL trypsin prior to ICAM-1 binding analysis . Trypsin treatment reduced CD36 binding and increased binding to recombinant ICAM-1 ( Figure S4 ) , and therefore may have cleaved or truncated the PfEMP1 head structure . The increase in ICAM-1 binding could be blocked by anti-ICAM-1 antibody ( mAb 15 . 2 ) and not by anti-CD36 isotype control antibody ( FA6-152 ) ( Figure S4 ) . In contrast , identical trypsin treatment of 3G8 ( IT4var1 ) and ItG-ICAM-1 parasite lines ( IT4var16 ) abolished binding to both CD36 and ICAM-1 ( data not shown ) . Thus , as predicted from binding of the isolated DBLβ domain [58] , IT4var31 was associated with ICAM-1 binding , but the cell binding assay was more sensitive than immobilized protein in detecting this interaction . Two of the parasite lines also bound at a low level to immobilized ICAM-1 recombinant protein but did not bind CHO745-ICAM-1 . Thus , there may be differences in the sensitivity of the two platforms to detect lower affinity ICAM-1 interactions , or some of the low level binding interactions may not have been specific . Overall , ICAM-1 binding was strongly associated with larger PfEMP1 proteins that contained a DBLβ domain . Seven of the ten parasites lines that expressed a dominant var transcript containing a DBLβ domain bound to ICAM-1 in either the cell or recombinant protein platform ( Figure 4 ) , and parasite lines without a DBLβ either bound extremely weakly or did not bind ICAM-1 ( Figure 6 ) . This difference was significant in the immobilized ICAM-1 assays ( 1-tailed t-test , p = 0 . 020 ) and just missed significance in the CHO745-ICAM-1 assay ( 1-tailed t-test , p = 0 . 103 ) . Recently , there has been a reclassification of DBL and CIDR domains into additional subtypes based on a comparison of 7 parasite genomes in which DBLβ domains were subclassified into 13 sub-types [10] . Of interest , all three parasites that bound in both the CHO-ICAM-1 and immobilized ICAM-1 assays expressed a DBLβ5 domain ( Figure 4 ) . To investigate if DBLβ5 could be a marker for ICAM-1 binding , we reanalyzed the recombinant DBLβ-ICAM-1 binding data [44] . In the IT4 parasite genotype , 7 of 23 DBLβ domains bound ICAM-1 . Of the 7 ICAM-1 binders , 6 were DBLβ5 sequences , and there were no DBLβ5 domains that did not bind ICAM-1 ( Figure 7 ) . Significantly , an ICAM-1 binding parasite from India ( JDP8-ICAM-1 , AY028643 ) [59] also uses a DBLβ5 domain to bind ICAM-1 ( Figure 7 ) . The fact that ICAM-1 binding was 100% predictable in the IT4 parasite genotype , and that a different parasite isolate from India also uses DBLβ5 for binding , strongly supports this domain as a marker for ICAM-1 binding . There are also two DBLβ3 sequences that bound ICAM-1 , one from the IT4 parasite genotype and one from the 3D7 parasite genotype [45] , but several other DBLβ3 sequences did not bind ICAM-1 as recombinant proteins ( Figure 7 ) . Taken together , ICAM-1 binding was strongly associated with the DBLβ domain , and the DBLβ5 marks a category of larger PfEMP1 variants that encode this adhesion property . Infected erythrocytes have been reported to bind a number of host receptors [20] , but for the most part binding has only been tested on one or a few parasite lines . Using transfected cells or recombinant proteins , the 19 parasite lines were assayed against 8 additional receptors: Endothelial Leukocyte Adhesion Molecule 1 ( E-selectin ) , Vascular Cell Adhesion Molecule 1 ( VCAM-1 ) , CHO receptor “X” , Hyaluronan Binding Protein 1 ( HABP1 ) , Platelet Endothelial Cell Adhesion Molecule-1 ( CD31/PECAM-1 ) , Thrombospondin-1 ( TSP-1 ) , CSA , and Fractalkine . Whereas a few parasite lines bound at a low level to TSP-1 and CHO-ELAM-1 , there was negligible binding to most receptors tested ( Figure 8 ) . Two of the UpsA parasites ( Pf13 and VarO ) bound at a low level to HABP1 , CD31 , and CSA . However , binding of UpsA parasites was performed in the presence of sulfated glycoconjugates to disrupt rosettes , and they also had higher background binding to bovine serum albumin ( BSA ) employed as a blocking agent for binding assays ( Figure 8 , and data not shown ) . As expected , the strongest CSA-binder in the panel was the CS2 parasite line in both the CHO-K1 cell and CSA spot formats ( Figure 8 ) . CS2 expresses the VAR2CSA PfEMP1 protein that has been shown to be the primary PfEMP1 variant associated with CSA binding [60] , [61] . Most of the other receptors tested did not support strong adhesion of infected erythrocytes in these binding assays and it is questionable whether all of the observed weak interactions are physiologically relevant .
PfEMP1 proteins/var genes are classified into three main subfamilies ( UpsA , UpsB , and UpsC ) that have different host expression profiles [35]–[37] , [39] . Both binding strength and specificity of IEs are likely to influence disease severity during an infection; therefore , it is important to understand whether PfEMP1 subfamilies have evolved specialized properties for distinct host/biological niches . Studies of malaria during pregnancy have demonstrated how a specific PfEMP1 variant can precipitate severe disease in otherwise immune women by altering IE tropism for the placenta [14] , [29] , [62] . Although VAR2CSA appears to be unique in its ability to confer high-affinity binding to CSA in the placenta [60] , [61] , [63] , it offers a paradigm for the role of specific PfEMP1s in disease . UpsA classified PfEMP1 proteins are frequently observed in young children with limited anti-malaria immunity or experiencing severe malaria [35]–[39] . Unlike VAR2CSA , the adherence characteristics of UpsA proteins are poorly understood and limited largely to predictions of binding based on studies of isolated adhesion domains [44]–[46] . To investigate a correlation of PfEMP1 binding specificities with disease outcome , the binding characteristics of at least a representative sample of the three main subgroups ( UpsA , UpsB and UpsC ) have to be known . In this study , we employed a panel of different PfEMP1 types to test binding predictions based upon studies of single PfEMP1 domains . While UpsA variants appear to be commonly expressed in early childhood infections and non-immune individuals [35]–[39] , very little is known about what may account for this preferential expression in the malaria naïve . Investigation is hampered because most P . falciparum infections contain a mixture of PfEMP1 variants and even minor parasite subsets may obscure binding analysis . In addition , gene silencing of UpsA variants has been observed upon in vitro adaptation [64] . In long term in vitro adapted parasite cultures grown without selection for specific var gene expression , UpsA variants were expressed at a low level , and an UpsB ( IT4var31/C18var ) and an UpsC ( IT4var37/AFBR6 ) var gene appeared to be the most common switch events . Both were also found to be frequently activated in previous clonal analyses in this strain background [7] , [52] and thus may have a higher “on” rate under in vitro culture conditions . One study found that var genes in central chromosome regions had lower switch rates than those in telomeric regions [65] , but inherent differences were not consistently observed in a different parasite line [52] . The chromosome positions of IT4var31 ( UpsB ) and IT4var37 ( UpsC ) have not been mapped and therefore we cannot comment on whether this observation held true in our study or not . However , our findings indicate that promoter type is not the main determinant of var gene “on” rate as far as UpsB and UpsC type var genes are concerned . In the case of UpsA variants , the promoter type did seem to determine var gene expression rate by significantly reducing it . To overcome these problems , we used specific monoclonal antibodies to generate three distinct UpsA parasite lines of high purity for the parasite panel . In epidemiological studies , CD36 and ICAM-1 binding are the most common adhesion traits in the parasite population [17] , [19] , but their distribution among different members of the PfEMP1 family is only partially understood [44]–[46] , [58] , [66] . In the parasite panel , CD36 was by far the most common binding partner , followed by ICAM-1 and TSP-1 . CD36-binding was nearly 100% predictable and was always associated with a CIDRα type domain in the protein head structure , while the three UpsA variants had different sequence types ( CIDRγ and CIDRδ ) and did not bind CD36 or only bound at a low level . Thus , in the absence of a CIDRα domain , other potential CD36 ligands [67] , [68] were unable to compensate for infected erythrocyte binding . Moreover , the level of CD36 binding differed between isogenic parasites expressing different PfEMP1 variants , suggesting that PfEMP1 sequence variability or surface expression levels have an important role in influencing the overall binding affinity of infected erythrocytes . The UpsA group contains three different types of CIDR1 sequences ( α1 , γ , or δ ) [10] , [12] , [40] , [46] . Although the three UpsA parasites in the panel were all selected for rosetting , “rosetting” and “non-CD36 binding” can exist as independent phenotypes . For instance , the non-CD36 binding CIDR domains identified in this study may potentially be found in non-rosetting group A genes , and there is evidence that CD36 is able to act as a host receptor for rosetting in the Malayan Camp parasite strain and some field isolates [69] . This parasite panel did not contain any representation of the CIDRα1 subtype , which is found in approximately half of UpsA proteins [10] . However , it has previously been shown that recombinant CIDRα1 subtype domains do not bind CD36 [46] , and CD36 selection led to loss of expression of an UpsA gene in a mixed parasite culture that expressed a CIDRα1 subtype [70] . Taken together , the results suggest the UpsA group is not under strong selection for CD36 binding , and it will be interesting to determine if the UpsA protein head structure is selected for specific binding properties that support microvasculature sequestration by a mechanism different from CD36 binding . Part of this selection may be for infected erythrocyte rosetting [71] , [72] , but the UpsA group may encode other adhesion properties [47] . After CD36 , ICAM-1 is one of the most common adhesion properties , and the two receptors synergize to mediate infected erythrocyte binding under flow [22] , [23] . ICAM-1 is upregulated on brain endothelium during malaria infections and has been proposed to be a potential cerebral sequestration receptor [24] . ICAM-1 binding has previously been mapped to the DBLβ domain [44] , [45] , [58] , [59] , [73] . Our study confirms this association as the DBLβ5 domain was 100% associated with ICAM-1 binding in both parasite lines and recombinant proteins . It also shows that not all DBLβ domains bind to ICAM-1 . In future work using patient samples it may be interesting to investigate how well transcription of var genes containing a DBLβ5 domain can predict ICAM-1 binding . Overall , this study identifies a category of large UpsB and UpsC PfEMP1 containing CIDRα and DBLβ5 subtype domains that were 100% associated with CD36 and ICAM-1 binding . In a comparison of var gene repertoires from 7 parasite strains , the CIDRα and DBLβ5 domains were always found together in tandem arrangement ( 27 of 399 full or partial length var genes ) , and the DBLβ5 domain was never associated with a predicted “non-CD36 binding” CIDR domain . This suggests the association has not evolved by chance and that the CIDRα-DBLβ5 domain combination may be under dual selection for binding to CD36 and ICAM-1 . Both receptors are co-displayed on many of the same cell types ( endothelial , monocyte , and dendritic cells ) and may provide the parasite opportunities to manipulate host cells [74] , [75] , thus contributing to their strong selection in the PfEMP1 repertoire . There were also a few DBLβ3 domains that bound to ICAM-1 , but these were found in association with both CD36 binding and non-CD36 binding CIDR domains . Thus , CD36 and ICAM-1 have left strong signatures of selection detectable by PfEMP1 adhesion domain sequence classification , despite the extensive sequence diversity in the family . Other PfEMP1 adhesion properties examined appear to be much rarer or may only play an additive role in overall binding affinity . Nearly all PfEMP1 proteins have four or more extracellular domains . In addition to undefined binding properties , other PfEMP1 domains may also function as “spacers” to position the PfEMP1 head structure and adjacent DBLβ away from the IE surface in order to engage CD36 and ICAM-1 [76] . A potential caveat is that binding was performed under static adhesion conditions , and individual host recombinant proteins were employed in the protein binding assays . However , all host receptors examined were originally defined under similar static adhesion conditions . Furthermore , static adhesion assays are capable of detecting host receptor interactions that support both rolling ( ICAM-1 , TSP-1 ) and stationary ( CD36 ) cytoadhesion of infected erythrocytes under flow conditions [21] . Cooperative binding is likely necessary to mediate firm adhesion under flow [21]–[23] , but from this analysis CD36 binding is under greatest selection and contributes the greatest binding avidity in different PfEMP1 proteins . These results reveal a fundamental difference in CD36 binding between Ups groups that has important implications for how parasites establish infections in individuals of varying levels of immunity . UpsA proteins are more commonly expressed in children with low immunity [35] , [36] , [39] . Later , as malaria immunity develops , it may be significant that the proportion of non-UpsA types and CD36 binding variants increases . It is interesting to speculate that non-CD36-binding parasites may experience a selective advantage over their CD36-binding counterparts in patients with limited exposure to malaria . CD36-binding parasites are thought to manipulate both host innate and adaptive immune responses by interacting with monocytes and dendritic cells [74] , [75] , [77] , [78] . In the malaria naïve , these interactions may be less important , or UpsA variants may possess other advantages or means of host manipulation . While UpsA variants have not been clearly associated with disease in all studies [79] , they are more abundant in patients with severe malaria [80] , [81] and have been associated with cerebral malaria infections in children in Mali [38] . A greater proportion of UpsA variants in early infections could potentially contribute to why CD36 binding levels are very low in children with severe malaria anemia [17] , [19] , or these variants could alter the pattern of sequestration to microvascular beds , such as brain endothelium , where CD36 binding levels are extremely low [24] . Therefore , it will be important to learn more about this group of proteins . In conclusion , the PfEMP1 protein family has diversified under dual selection to evade host immunity and mediate infected erythrocyte binding . The development of a parasite panel enriched for distinct PfEMP1 expression from the major Ups groups has facilitated the testing of binding predictions , and may have potential applications for investigating immune acquisition to the family of proteins . This comparative analysis demonstrates the predictability of P . falciparum-IE binding to the two major cytoadhesion receptors CD36 and ICAM-1 and provides new insight into how natural selection may be shaping the PfEMP1 binding repertoire to exploit distinct host niches of varying anti-malaria immunity .
Human blood was used for P . falciparum culture in this study . Donor blood was obtained from healthy volunteers under a minimal risk , standardized , Institute protocol ( protocol number HS013 ) that was approved by the Western Institutional Review Board . Written informed consent was obtained from all blood donor study participants . The three UpsA variants were isolated by gelatin sedimentation followed by positive selection with specific monoclonal antibodies against the respective NTS-DBLα domain . The VarO parasite clone was generated from the Palo Alto strain as described by rosette enrichment and selection with monoclonal antibody D15–50 [82] . The R29 parasite ( IT4 parasite strain ) has been described previously [6] , [7] , [83] . Highly enriched parasite cultures expressing the R29 PfEMP1 protein and Pf13 ( 3D7 strain ) were isolated by similar methodologies to the VarO parasite line using rosette enrichment and specific monoclonal antibodies against the R29-DBLα domain ( 3B13C5 ) or the Pf13_0003-DBLα domain ( J3 . 21 ) [53] . The ItG-ICAM-1 parasite line was derived by ICAM-1 selection [18] , CS2 by CSA selection [84] , and the 3G8 , 4E12 , and 2G2 parasite lines by limited dilution cloning [52] . The remaining parasite lines were derived from IT4/25/5 clone A4 [6] by limited dilution cloning . Infected erythrocytes were cultured under standard conditions using human O red blood cells ( RBCs ) in RPMI-1640 medium ( Invitrogen ) supplemented with 10% pooled human A+ serum and an atmosphere of 5% CO2 , 5% O2 , and 95% N2 at 37°C . Synchronization of parasite growth was achieved by treatment with 5% sorbitol in PBS . Gelatin sedimentation assays were performed in RPMI-1640 medium containing 0 . 7% porcine gelatin ( Sigma ) for 45 minutes at 37°C . Enrichment of infected erythrocytes ( IE ) in the gelatin supernatant was determined by counting >300 methanol-fixed , Giemsa-stained RBCs under 1000X magnification . Rosette formation was visualized after infected red blood cell nuclei were stained by ethidium bromide . The rosetting rate was calculated by determining the percentage of rosette-forming infected cells in the mature parasite population . CHO-K1 , CHO745 , and CHO745 transfectants expressing CD36 , ICAM-1 , E-selectin , or VCAM-1 were cultured in F-12 Kaighn's medium supplemented with 10% fetal calf serum and 0 . 5 mg/mL geneticin ( Gibco ) . The CHO745 transfectants were described in Buffet et al . [85] . Recombinant protein surface expression was monitored by flow cytometry on a monthly basis using receptor-specific monoclonal antibodies ( R&D Systems ) , and cells were replaced if the percentage of transfected cells or mean fluorescence intensity diminished by greater than 20% . An A4 parasite clonal line [6] was grown continuously under standard conditions for more than 70 growth cycles in the absence of overt selection . IEs were periodically enriched for knob expression by floatation in 0 . 7% porcine gelatin ( Sigma ) dissolved in RPMI-1640 ( Invitrogen ) at 37°C . Prior to limited dilution cloning , RNA was collected and a profile of var transcription was determined by quantitative real-time polymerase chain reaction ( qRT-PCR ) using a primer set designed to amplify unique sequence tags within the repertoire of IT4 var genes [86] . Individual infected erythrocytes were obtained on two separate occasions by limited dilution cloning after more than 78 and 84 cycles of continuous parasite growth , respectively , at a seeding rate of 0 . 5 infected erythrocytes per well . Initial frozen stabilates were collected after approximately 14–15 cycles of growth and parasite lines were typed for var gene expression by qRT-PCR . The determination of var gene transcription profiles was performed using primers and PCR conditions as previously described [86] . In brief , RNA was extracted in Trizol LS ( Invitrogen ) from ring stage parasites at ∼6–12 hours post-invasion and purified on RNeasy Micro columns with on-column DNaseI treatment ( QIAGEN ) according to manufacturer's protocols . cDNA was synthesized from 4 µg total RNA using Multi-Scribe reverse transcriptase ( Applied Biosystems ) and one half of this material was used for each real-time reaction against the complete set of primers . Real-time reactions were performed on an ABI Prism 7500 thermocycler at optimized final primer concentrations of 0 . 05 µM-0 . 5 µM using Power-SYBR Green Master Mix in 20 µL reaction volumes under the following PCR conditions: 50°C for 1 min , 95°C for 10 min , then 40 cycles of dissociation , annealing , and extension at 95°C for 15 sec , 52°C for 15 sec , and 60°C for 45 sec , respectively . Relative transcription was determined by normalization to the adenylosuccinate lyase ( ASL , PFB0295w ) control housekeeping gene . After optimizing primer efficiencies , residual primer bias was corrected by calculating the average difference in CT values between each optimized IT4 var primer pair and ASL using genomic DNA as template to provide a final normalized correction . Parasite RNA was collected and binding assays performed within the same growth cycle to accurately assess var transcription at the time of the binding assay . For binding assays , individual CHO cell lines were grown to subconfluent levels on 60-mm tissue culture-treated dishes ( BD Falcon ) and recombinant proteins were immobilized by overnight incubation onto 60-mm polystyrene dishes ( Corning ) . The following proteins were analyzed: CD36-Fc ( R&D Systems ) , ICAM-1-Fc ( R&D Systems ) , HABP1/gC1qR-6x HIS ( R&D Systems ) , Fractalkine-6x-HIS ( R&D Systems ) , CD31/PECAM-1 ( R&D Systems ) , TSP-1-10x HIS ( R&D Systems ) , and CSA ( Sigma ) . All proteins and CSA were applied at 50 µg/mL except for CD36 and ICAM-1 , which were additionally applied at 5 µg/mL and 100 µg/mL . On the day of the assay , dishes containing CHO cells were washed twice with pre-warmed cell binding medium ( BMcell: RPMI-1640 medium containing 0 . 1% bovine serum albumin , pH 7 . 2 ) and protein spots were blocked with 2% bovine serum albumin for 45 min at 37°C , then washed twice with pre-warmed protein binding medium ( BMprotein: RPMI-1640 medium containing 0 . 1% bovine serum albumin , pH 6 . 8 ) . Infected erythrocytes ( 3-8% parasitemia ) were washed and resuspended to 1% hematocrit in either BMcell or BMprotein then overlayed onto CHO cells or spotted onto immobilized proteins , respectively , and incubated for 1 hr at 37°C . Prior to binding assays , rosettes in the three UpsA parasite lines were disrupted in binding medium containing 100 Units/mL heparin sulfate ( Sigma ) . The same conditions were used when testing the effect of heparin sulfate on all of the parasites in the panel . In additional assays to test the effect of sulfated glycoconjugates on IE binding , either 10 µg/mL dextran sulfate ( MW >500 , 000; Sigma ) or 100 Units/mL heparin sulfate were included during binding assays . Non-binding erythrocytes were removed by gently flooding each dish with warm binding medium , rocking the dish back and forth several times to resuspend non-binding erythrocytes , then pouring off and replacing the medium . The initial washing procedure was performed on CHO745 cells and 2% BSA spots and was repeated until non-binding erythrocytes were sufficiently removed by observation under 400X magnification . The remaining cells and spots then received the same number of washes . For quantification , dishes were fixed in 1% glutaraldehyde for 20 min at room temperature , then stained with 1X Giemsa for 15 minutes . Binding was quantified by determining the number of IE adhering to at least 300 cells under 1000X magnification or the number of IE per mm2 in 4 random fields under 400X magnification . All binding assays were repeated in duplicate . Trophozoite stage infected RBCs were incubated for one hour at room temperature with specific monoclonal mouse antibodies against R29var NTS-DBLα ( mAb 3B13C5 , 1∶500 ) Pf13_0003 NTS-DBLα ( mAb J3 . 21 , 1∶20 ) , or VarO NTS-DBLα ( mAb D15-50 , 1∶20 ) . Antibody labeling was detected with goat anti-mouse IgG-R-Phycoerythrin ( Sigma ) ( 1∶20 ) for 30 minutes . Infected erythrocyte nuclei were detected with SYTO 61 DNA dye ( Invitrogen ) ( 1∶1000 ) added with the secondary antibody . Stained cells were washed in PBS and analyzed on an LSRII FACS machine ( BD Biosciences ) . Analysis was performed using FlowJo 8 ( Tree Star , Inc ) . | The malaria parasite Plasmodium falciparum persists in the human host partly by avoiding elimination in the spleen during blood stage infection . This strategy depends principally upon members of the large and diverse PfEMP1 family of proteins that are exported to the surface of infected erythrocytes . PfEMP1 proteins are important targets for host protective antibody responses and encode binding to several different host receptor proteins . Switches in PfEMP1 expression allow parasites to evade host antibodies and may precipitate severe disease when infected erythrocytes accumulate in brain or placenta . Consequently , the severity of malaria infection may depend on the type of PfEMP1 protein expressed . In this study , we employ a representative panel of distinct PfEMP1 types and host receptor proteins to demonstrate that CD36 and ICAM-1 binding properties of full-length PfEMP1 are highly predicted by their domain composition . We also find that CD36 binding is under strong selection in many PfEMP1 proteins , but that a group of PfEMP1s associated with more severe infections does not bind CD36 and may utilize alternative means to sequester infected erythrocytes . These findings have implications for understanding the molecular basis for severe malaria . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [
"emerging",
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"biology",
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] | 2011 | Investigating the Host Binding Signature on the Plasmodium
falciparum PfEMP1 Protein Family |
The conserved target of rapamycin ( TOR ) pathway integrates growth and development with available nutrients , but how cellular glucose controls TOR function and signaling is poorly understood . Here , we provide functional evidence from the devastating rice blast fungus Magnaporthe oryzae that glucose can mediate TOR activity via the product of a novel carbon-responsive gene , ABL1 , in order to tune cell cycle progression during infection-related development . Under nutrient-free conditions , wild type ( WT ) M . oryzae strains form terminal plant-infecting cells ( appressoria ) at the tips of germ tubes emerging from three-celled spores ( conidia ) . WT appressorial development is accompanied by one round of mitosis followed by autophagic cell death of the conidium . In contrast , Δabl1 mutant strains undergo multiple rounds of accelerated mitosis in elongated germ tubes , produce few appressoria , and are abolished for autophagy . Treating WT spores with glucose or 2-deoxyglucose phenocopied Δabl1 . Inactivating TOR in Δabl1 mutants or glucose-treated WT strains restored appressorium formation by promoting mitotic arrest at G1/G0 via an appressorium- and autophagy-inducing cell cycle delay at G2/M . Collectively , this work uncovers a novel glucose-ABL1-TOR signaling axis and shows it engages two metabolic checkpoints in order to modulate cell cycle tuning and mediate terminal appressorial cell differentiation . We thus provide new molecular insights into TOR regulation and cell development in response to glucose .
The conserved TOR signaling pathway controls cell growth and proliferation across taxa and disease states by regulating metabolic processes in response to available nutrients and energy [1–4] . Amino acids signal to TOR directly via a class of small GTPases that , in mammalian cells , recruit TORC1 to the lysosome for activation under nitrogen/ amino acid sufficiency [5 , 6] . During energy stress , the AMPK/Snf complex negatively regulates TOR pathway branches via phosphorylation of the downstream TOR component Raptor/Kog1 [7] , although this is indirect in yeast [4] . In contrast to amino acids and energy , little is known about the molecular mechanisms underlying cellular glucose control of TOR signaling [3 , 8] . In yeast [1] and mammals [9] , TOR kinase forms two complexes , TORC1 and TORC2 , with different roles in cell growth . TORC1 is rapamycin sensitive and exerts temporal control on cell growth by regulating ribosome biogenesis in addition to protein , lipid and nucleotide biosynthesis . TORC1 inhibits autophagy when active . TORC2 is rapamycin insensitive and governs actin cytoskeleton organization during the cell cycle [1 , 10 , 11] . Loss of TORC1 components , or rapamycin treatment , results in cell cycle arrest at G1/G0 [1 , 12 , 13] and autophagy induction [1 , 14] . Temperature-sensitive yeast Kog1 mutants have shown that under nutrient-poor or rapamycin treatment conditions , TORC1 first delays the cell cycle at G2/M and induces autophagy before progressing mitosis to G1/G0 arrest [11 , 14] . The fungus Magnaporthe oryzae causes blast , the most devastating disease of cultivated rice ( Oryza sativa ) [15–17] . Fungal spores attached to rice leaf surfaces elaborate specialized appressorial cells at germ tube tips that function to penetrate into underlying tissues where M . oryzae grows as parasitic invasive hyphae ( IH ) . Recently , we have demonstrated status-dependent roles for TOR signaling that are critical for disease progression by M . oryzae: inactive or downregulated TOR signaling ( TORoff ) permits appressorial development on the rice leaf surface; activated TOR ( TORon ) facilitates proliferation in rice cells [18 , 19] . Considering TOR kinase is likely to be intrinsically active [20] , identifying factors that maintain TORoff during appressorium formation is thus an essential but enigmatic component of our understanding of the rice infection process . Two opposing signaling pathways , cAMP/PKA and TOR , regulate appressorial development . cAMP/PKA signaling is a positive-acting determinant of appressorial development [21] , whereas TOR signaling is a negative-acting regulator of appressorium development that blocks cAMP/ PKA signaling downstream of cPKA when active [19] . When cAMP/PKA signaling is "on" and TOR signaling is "off" , incipient appressorial development is accompanied by a single round of mitosis and autophagic cell death of the conidium [22–24] . A mature appressorium accumulates hydrostatic turgor that is directed onto a thin penetration peg , forcing it through the rice epidermis into underlying rice cells . There , M . oryzae elaborates bulbous IH and spreads undetected as a biotroph for the first four to five days of infection before necrotic lesions form [15 , 17] . TOR status switches from "off" to "on" as the fungus transitions from the nutrient-free leaf surface to the nutrient-rich rice cell . This is conditioned by a metabolic shift from lipid metabolism during appressorial turgor generation to glucose metabolism through the pentose phosphate pathway ( PPP ) during early in planta growth [18] . The bona fide glucose-6-phosphate ( G6P ) sensor trehalose-6-phosphate synthase 1 ( Tps1 ) facilitates the shift to glucose metabolism by coordinating the genetic response to cellular glucose via an NADPH-dependent glucose-signaling pathway [25–28] . Tps1-dependent glucose metabolism via the PPP and transketolase ( Tkl1 ) provides NADPH for antioxidation [29] and ATP to activate TOR [18] . The resulting TORon state is necessary both for the timely migration of appressorial nuclei into IH , and to promote mitosis and subsequent IH proliferation during biotrophy [18] . Thus , TOR engages one or more metabolic checkpoint in response to ATP production from glucose metabolism in order to promote fungal growth in rice cells . Where TOR regulates the cell cycle is not known . This study was motivated by a desire to identify additional glucose signaling components in M . oryzae . By mining differential proteomic data sets from wild type ( WT ) and Δtps1 mutant strains , we identified a glucose-induced gene , ABL1 , and determined that it functions to inhibit TOR activity in the absence of glucose . We proceeded to uncover a novel glucose-ABL1-TOR signaling axis connecting cellular glucose to TOR activity , cell cycle tuning , and terminal appressorial cell differentiation .
We identified a candidate glucose signaling factor for functional characterization by mining previously generated proteomics data [28 , 30] based on our rationale that unknown components of the glucose signaling pathway in M . oryzae might be responsive to glucose and dependent on Tps1 under axenic growth conditions . A protein encoded at locus MGG_00987 [31] was detected in wild type ( WT ) but not Δtps1 mycelial samples following growth on optimal 1% ( w/v ) glucose minimal media ( GMM ) with nitrate as the sole nitrogen source [28] . MGG_00987 encodes a previously uncharacterized AMP-activated protein kinase ( AMPK ) β subunit-like protein ( Abl1 ) . BLAST analysis suggests the 522 amino acid Abl1 protein carries an N-terminal glycogen-binding domain ( GBD ) that is associated with the catalytic domain of AMPK β subunits , but lacks the iteration domain carried by canonical AMPK β subunits such as the M . oryzae MoSip2 protein [32] . PSORTII analysis suggests the protein localizes to the cytoplasm . Under axenic shake conditions , ABL1 expression was downregulated 9-fold in WT when grown under glucose starvation conditions compared to growth on GMM with nitrate , and was downregulated 25-fold in Δtps1 mutant strains compared to WT on GMM with nitrate ( Fig 1A ) . In contrast , ABL1 gene expression was not affected by growth in GMM lacking a nitrogen source ( S1A Fig ) . Thus , ABL1 is expressed in response to G6P sensing by Tps1 but is not responsive to nitrogen . To understand what role ABL1 might play in glucose signaling , fungal physiology and/ or host infection , we deleted the ABL1 gene from the M . oryzae genome . The resulting Δabl1 mutant strain sporulated like WT following growth on GMM with nitrate ( S1B Fig ) . Despite being expressed in a glucose- and Tps1-dependent manner , ABL1 was not required for axenic growth on GMM ( S1C Fig ) . Moreover , whereas the yeast AMPK/Snf complex is required for the expression of glucose-repressed genes under glucose-limiting conditions [33] , Δabl1 mutant strains grew like WT on low concentrations of glucose ( S1C Fig ) and on the non-preferred sugars maltose and sucrose in addition to the derepressing carbon source xylose ( S1D Fig ) . Δabl1 mutant strains could also grow like WT on minimal media containing acetate as the sole carbon source ( S1D Fig ) , which is in contrast to M . oryzae AMPK/Snf complex mutants , including Δmosip2 , that are impaired for lipid and acetate metabolism [32] . These results taken together show that ABL1 is not required for carbon utilization during axenic growth and is thus functionally distinct from components of the AMPK/ Snf complex in M . oryzae and yeast [32] . Further evidence that TPS1 is likely epistatic to ABL1 is shown in S2A Fig . We deleted the ABL1 gene from the genome of the Δtps1 mutant strain and determined that , whereas the Δabl1 single mutant strain could grow like WT on GMM with nitrate as the sole nitrogen source , the Δtps1 Δabl1 double mutant was , like the Δtps1 parental strain , nitrate non-utilizing . Δtps1 is poorly sporulating [25] , but sporulation by the Δtps1 Δabl1 double mutant strain was completely abolished on complete media ( no spores were counted after harvesting five 10 day old plates of Δtps1 Δabl1 mutant strains ) . Also , S2B Fig shows that , compared to WT , TPS1 gene expression was not affected in Δabl1 strains following growth in GMM with nitrate . We conclude that ABL1 acts downstream of TPS1 and is not involved in feedback regulation of TPS1 . To determine if ABL1 was important for pathogenicity , conidia of Δabl1 mutant strains were applied to the leaves of whole rice plant seedlings . Compared to WT and the Δabl1 ABL1 complementation strain , loss of ABL1 severely attenuated fungal pathogenicity on rice leaves ( Fig 1B ) , demonstrating ABL1 is essential for causing rice blast disease . To understand why ablating ABL1 impacted pathogenicity , we next examined infection-related development by Δabl1 mutant strains and found that the observed loss of rice infection was in major part due to impaired appressorium formation and function ( Fig 1C ) . Spores of WT and Δabl1 mutant strains were applied to artificial , appressorium-inducing hydrophobic surfaces . After 24 h post inoculation ( hpi ) , at 22°C , approximately 90% of WT spores had germinated and elaborated mature appressoria at germ tube tips ( S3A Fig; Fig 1C ) . The remainder of WT spores either failed to germinate or formed short germ tubes without appressoria . In contrast , only approximately 34% of germinating Δabl1 spores formed appressoria ( S3A Fig ) . WT rates of appressorium formation were restored in the Δabl1 ABL1 complementation strain ( S3A Fig ) . Moreover , germinating Δabl1 spores consistently produced four distinct morphotypes ( Type I-IV ) by 24 hpi ( Fig 1C and 1D; S1 Table ) . Type I Δabl1 morphotype ( formed by 33% of spores ) was distinguished as having expanded germ tubes by 24 hpi , compared to WT , with no appressoria . Type II morphotype ( 9% of spores ) was similar but produced appressoria . Type III morphotype ( 33% of spores ) produced undifferentiated swellings at or near germ tube tips . Type IV Δabl1 morphotype ( 25% of spores ) was indistinguishable from WT . Because the majority of germinated Δabl1 spores ( 66% ) did not form appressoria by 24 hpi on artificial hydrophobic surfaces ( Fig 1C and 1D; S1 Table ) , we conclude that ABL1 is required for appressorium formation . On rice leaf surfaces , by 24 hpi , only approximately 20% of germinated Δabl1 spores formed appressoria compared to 90% for both WT and the Δabl1 ABL1 complementation strain ( S3B Fig ) . Moreover , less than 20% of Δabl1 appressoria that formed on rice leaf surfaces were observed penetrating the cuticle into underlying epidermal cells compared to > 90% for WT and the Δabl1 ABL1 complementation strain ( S3C Fig ) . Those Δabl1 appressoria that penetrated formed severely restricted IH that failed to grow into adjacent rice cells by 44 hpi ( S3D and S3E Fig ) . Thus , in addition to appressorium formation , ABL1 is required for host penetration and in planta growth . Subsequently , the loss of ABL1 affected appressorial differentiation and function , increased the incidences of elongated germ tubes , and impaired biotrophic growth in rice cells . In order for WT to form functional appressoria , the apical nucleus of each germinating spore undergoes one round of mitosis in the germ tube . One daughter nucleus then migrates to the incipient appressorium , the other returns to the conidium and undergoes degeneration during autophagic cell death of the spore [22–24] . To understand how these processes might be affected in Δabl1 mutant strains , we deleted ABL1 from the genome of a Guy11 strain expressing histone H1 fused to red fluorescent protein ( RFP ) [23] to create the Δabl1 H1:RFP mutant strain . This strain recapitulated the original Δabl1 mutant phenotype such that Δabl1 H1:RFP strains were reduced in pathogenicity on whole rice leaves compared to the Guy11 strain carrying H1:RFP ( denoted WT H1:RFP to indicate the parental background ) , displayed Type I-IV morphotypes in proportions similar to the original Δabl1 mutant strain on hydrophobic surfaces , and were impaired for growth in rice cells ( S4A–S4C Fig; S1 Table ) . Fig 2A shows that by 24 hpi on artificial hydrophobic coverslips , germinating WT H1:RFP spores carried a single appressorial nucleus . In contrast , the majority of germinating Δabl1 H1:RFP spores underwent multiple rounds of mitosis in germ tubes and were impaired for conidial nuclear degeneration ( Fig 2A and 2B ) . To quantify the affect of Δabl1 on mitosis and autophagy , Fig 2B shows that at 0 hpi , WT H1:RFP and Δabl1 H1:RFP spores carried three nuclei . By 8 hpi , germinating WT H1:RFP spores had undertaken a single round of mitosis and carried 4 nuclei ( 3 in the conidium and 1 in the incipient appressorium ) . By 24 hpi , autophagy and conidial nuclear degradation was complete and > 90% of germinated WT H1:RFP spores carried a single appressorial nucleus . In contrast , the majority of germinating Δabl1 H1:RFP spores had undergone one round of mitosis by 4 hpi and a second round of mitosis by 8 hpi ( Fig 2B ) . A third round of mitosis occurred after 12 hpi in about 20% of germinating Δabl1 H1:RFP spores . By 24 hpi , the majority of germinated Δabl1 H1:RFP spores ( displaying Type I-III morphotypes ) carried 5 nuclei ( three in the conidium and 2 in the germ tube; Fig 2A and 2B ) and a significant fraction ( >20% ) carried 6 nuclei . These results describe how mitosis was accelerated and nuclear degradation impaired in the majority of germinating Δabl1 H1:RFP spores . Additional evidence that autophagy was misregulated in Δabl1 mutant strains is shown in Fig 3 . Monodansylcadaverine ( MDC ) staining at 24 hpi shows autophagosomes were concentrated in the appressorium of WT but were dispersed throughout the germ tube in Δabl1 mutant strains . Moreover , glycogen mobilization is an important feature of appressorium morphogenesis [34] . S5 Fig shows how glycogen mobilization , like autophagy , was severely impaired in Type I-III morphotypes of Δabl1 mutant strains compared to WT and was delayed in Type IV morphotypes . We next discovered that ABL1 controls appressorium development , mitosis and autophagy via the TOR signaling pathway . This was achieved by determining the relationship of ABL1 to characterized signaling pathways that regulate appressorial morphogenesis . Loss of the cAMP/PKA signaling pathway [21] , or activation of TOR-signaling to inhibit the cAMP/PKA signaling pathway downstream of cPKA [19] , abolishes appressorium formation . Appressorium formation can be remediated in strains carrying mutations upstream of cPKA by cAMP treatment , and in TOR-activated mutants by treatment with the specific TOR kinase inhibitor rapamycin ( Rap ) . Fig 4A and 4B , S4B Fig and S1 Table show that Rap treatment , but not treatment with cAMP , induced autophagy and morphologically normal ( Type IV ) appressorium formation in Δabl1 and Δabl1 H1:RFP mutant strains . These data strongly suggest TOR signaling is inappropriately activated in Δabl1 mutant strains during germination to block cAMP/ PKA signaling downstream of cPKA . Consistent with this notion , Rap and cAMP both induced appressorium formation in WT on non-inductive hydrophilic surfaces , as noted previously [19] , but only Rap induced appressorium formation in Δabl1 strains on hydrophilic surfaces ( Fig 4C ) . Evidence that ABL1 functions as an upstream inhibitor of TOR signaling is shown in Fig 4D . Rbp35 is an M . oryzae RNA-binding protein involved in processing RNA transcripts essential for host infection [35] . Δrbp35 mutant strains form appressoria but are downregulated for TOR pathway activity [35] . We reasoned that if perturbed appressorium formation in Δabl1 mutant strains was due to constitutively active TOR signaling , then the loss of RBP35 in Δabl1 mutant strains might restore appressorium formation in this mutant background . Fig 4D shows that Δabl1 Δrbp35 double mutant strains had WT appressorial formation rates on hydrophobic surfaces . Fig 4E summarizes the deduced relationships between ABL1 , TOR signaling , the cAMP/PKA pathway , and appressorium formation . Additional and direct evidence that TOR is inappropriately activated in Δabl1 mutant strains is shown in Fig 4F . TOR signaling controls ribosomal gene expression [36] , and the ribosomal genes RS2 and RS3 are expressed in M . oryzae when TOR signaling is active and downregulated when TOR is inactive [18] . Following growth under glucose starvation conditions , we observed by qPCR that RS2 and RS3 were significantly ( **P<0 . 001 ) more highly expressed in Δabl1 mutant strains than in WT . Taken together , our results indicate ABL1 acts as a novel upstream inhibitor of TOR function to promote appressorium formation , induce autophagy and arrest mitosis during appressorium morphogenesis . Conversely , activated TOR signaling in Δabl1 mutant strains inhibits appressorium formation downstream of cAMP/PKA , promotes mitosis and germ tube elongation , and abolishes autophagy . In yeast and mammalian cells , TOR inactivation terminates protein synthesis resulting in G1 arrest [12] and autophagy [37] . To determine whether G1 arrest was necessary and sufficient to account for the induction of appressorium formation and autophagy in Rap treated Δabl1 mutant strains , we treated germinating spores of WT H1:RFP and Δabl1 H1:RFP with cycloheximide ( CHX ) . Similar to TOR inactivation , CHX induces G1 arrest by inhibiting protein synthesis and reducing the translation of cyclins [12 , 38] . Spores of each strain were applied to coverslips at 22°C , treated with CHX at the indicated time points , and viewed at 24 hpi ( Fig 5A and S2 and S3 Tables ) . Adding CHX at 0 hpi abolished germination in both strains when viewed at 24 hpi , indicating that protein synthesis is a requisite for spore germination in M . oryzae . Note that adding Rap at 0 hpi does not affect germination ( Fig 4A ) , indicating TORoff does not arrest in this first G1 phase . CHX treatment at 1 hpi shows Δabl1 H1:RFP is accelerated for germination compared to WT H1:RFP . Adding CHX to germinating WT H1:RFP spores at 4 hpi prevented mitosis and appressorium formation , indicating WT apical conidial nuclei have not passed START and are not committed to mitosis by 4 hpi . In contrast , adding CHX to germinating Δabl1 H1:RFP spores at 4 hpi after one round of mitosis ( or adding CHX to Δabl1 H1:RFP spores at 8 hpi after the second round of mitosis ) arrested the cell cycle in the next G1 phase but did not induce appressorium formation . These results demonstrate , firstly , that Δabl1 H1:RFP apical conidial nuclei have passed START and exited the first G1 phase by 4 hpi , consistent with accelerated mitosis in Δabl1 strains ( Fig 2A and 2B ) . Secondly , these results provide strong evidence that G1 arrest alone is not sufficient to induce appressorium formation and autophagy in Δabl1 mutant strains . Arresting the cell cycle at S-phase by adding the DNA synthesis inhibitor hydroxyurea ( HU ) at 0 hpi also did not induce appressorium development or autophagy in WT or Δabl1 mutant strains ( Fig 5B; S2 and S3 Tables ) . Note that HU treatment at 0 hpi permitted spore germination , but nuclear migration into the germ tube did not occur for either strain indicating that an S-phase checkpoint has to be cleared for apical nuclei to migrate into the germ tube before mitosis . Moreover , in WT , this S-phase checkpoint has to be cleared in order for appressorium formation to be induced [22] . However , adding HU to germinating Δabl1 H1:RFP spores at 4 hpi arrested mitosis at the second S-phase after the first round of mitosis but did not induce appressoria or autophagy . Cell cycle progression through DNA replication is therefore necessary but not sufficient to induce appressorium formation or autophagy . We next treated spores with the G2 inhibitor benomyl ( Ben ) . Fig 5C and S2 and S3 Tables show that Ben treatment of germinating Δabl1 H1:RFP spores induced the formation of aberrant appressorium formation with short germ tubes and complete nuclear degeneration if added at 0 hpi before the first G2 phase in Δabl1 strains , or at 4 hpi before the second G2 phase following the first round of mitosis ( Fig 2B ) , but not if added at 8 hpi ( after which further mitosis is delayed or mostly arrested in this mutant strain , Fig 2B ) . These results strongly imply the novel proposition that ABL1-dependent cell cycle arrest at the G2/M checkpoint is required for appressorium and autophagy induction . To determine if the appressorium-inducing G2 arrest occurred via TOR , we treated Δabl1 H1:RFP spores with Rap at different time points and viewed the effect on appressorium morphogenesis at 24 hpi . Fig 5D and S2 and S3 Tables show that treating germinating Δabl1 H1:RFP spores with Rap before the first ( at 0 hpi ) or second ( at 4 hpi ) G2 phase , but not later ( at 8 hpi ) , induced appressorium formation and autophagy and progressed the cell cycle to G1 arrest , resulting in normal appressorium formation . Treating Δabl1 H1:RFP spores with Rap at later time points arrested mitosis in G1 ( confirming ABL1 acts via TOR in G1 arrest ) but did not induce appressoria formation or autophagy . Thus , TORoff is essential for G2 arrest and the induction of appressorium formation , autophagy and cell cycle progression to G1 . The previous results suggested that a G2 arrest followed by cell cycle re-progression to G1 are required for proper appressorial development by untreated WT or Rap treated Δabl1 spores . Similarly in yeast , reduced TOR activity under nutrient limiting conditions or following Rap treatment leads to cell cycle arrest at the G2/M transition . This is followed by the induction of autophagy . Autophagy-dependent activation of TOR then restarts mitosis and allows cell cycle re-progression to G1 arrest [11 , 14] . To determine if cell cycle re-progression to G1 after G2 arrest was dependent on autophagy in M . oryzae , we treated spores with the autophagy inhibitor 3-Methyladenine ( 3-MA ) . Compared to single Ben treatment , which resulted in complete nuclear degeneration , treatment with both Ben and 3-MA inhibited autophagy of conidial nuclei ( Fig 5E ) . This demonstrated that autophagy and appressorium induction coincided with G2 arrest , but autophagy was not required for the induction of appressorial development . Treatment with 3-MA alone resulted , by 24 hpi , in apical nuclei migrating into the incipient appressorium without undergoing mitosis , indicating autophagy is required for the completion of the cell cycle following G2 arrest . When considered along with evidence showing G1 arrest alone does not induce autophagy in Δabl1 H1:RFP strains ( Fig 5A and 5D ) , the data in Fig 5E supports the notion that ABL1-TOR dependent G2/M arrest co-induces appressorium development and autophagy with the latter process required for cell cycle re-progression to G1 arrest and the production of functional appressoria . Considering ABL1 is a glucose-responsive gene ( Fig 1A ) , we next sought to determine the relationship between glucose and the ABL1-TOR signaling pathway described above . Treating germinating WT H1:RFP spores with 1% ( w/v ) glucose ( Glc ) phenocopied Δabl1 . Specifically , the development of germinating WT H1:RFP spores treated with Glc at 0 hpi was indistinguishable from untreated Δabl1 H1:RFP spores by 24 hpi: both produced long germ tubes , most without appressoria ( S1 Table ) , underwent multiple rounds of mitosis and were abolished for autophagy ( Fig 6A ) . Adding Rap to Glc treated WT spores at 0 hpi overrode the glucose mitotic proliferation signal and , like Δabl1 spores treated with Rap only , restored appressorium formation and autophagy by 24 hpi . In addition , septation was misregulated in both Glc treated WT H1:RFP strains and untreated Type I-III Δabl1 H1:RFP morphotypes ( but not Type IV ) , and these developmental defects were also remediated by Rap treatment ( Fig 6B and S6A Fig ) . These results place glucose signaling , like ABL1 , upstream of TOR . S6B Fig confirms that glucose signaling is transmitted via TOR . Although Rap treatment could remediate appressorium formation by glucose treated WT spores , it could not induce appressorium formation by glucose treated Δfpr1 spores . FPR1 encodes FKBP12 , a component of the FKBP-rapamycin complex that physically and specifically interacts with TOR to inhibit its activity; loss of FKBP12 renders Δfpr1 mutant strains insensitive to rapamycin [19] . By 24 hpi , glucose treated WT spores produced morphotypes ( Fig 6C; S1 Table ) and nuclei ( S6C Fig ) in similar proportions and numbers to those observed for untreated Δabl1 spores . Mitosis was also accelerated in glucose treated germinating WT spores ( S6D Fig ) . However , glucose treatment did not affect the phenotype of germinating Δabl1 spores compared to untreated Δabl1 controls ( Fig 6C; S6C Fig; S1 Table ) . These results indicate that phenotypes resulting from glucose treatment or ABL1 deletion are not additive and confirm that the loss of ABL1 mimics glucose treatment during appressorial differentiation , thus placing glucose signaling upstream of ABL1 . Although ABL1 is expressed in the presence of glucose in response to G6P sensing by Tps1 ( Fig 1A ) , a functional ABL1 gene is required to inhibit TOR in the absence of glucose ( Fig 6A ) . To resolve this paradox , we considered that the ABL1 gene was expressed in the presence of glucose when its protein product was not required in order to prime the cell with Abl1 in order to rapidly inactivate TOR and delay mitosis if glucose became limiting . If so , we predicted that although ABL1 expression was inducted by glucose and downregulated in glucose starvation conditions , the Abl1 protein should be detectable under both conditions . In support of this notion , S7A Fig shows that Abl1 protein was detected in samples of mycelia grown either in either the presence or the absence of glucose . We complemented Δabl1 with the ABL1 gene , under its native promoter , that was fused at the 3’ end to the gene encoding green fluorescent protein ( GFP ) . Δabl1 ABLGFP strains expressing the Abl1GFP fusion protein were , like the complementation strain discussed above , restored for pathogenicity and appressorium formation , indicating Abl1GFP was functional . However , Abl1GFP fluorescence was not detected above background autofluorescence , suggesting the protein was present in low amounts or in a configuration that precluded detection by confocal microscopy . Nonetheless , probing Western Blots with anti-GFP identified bands of Abl1GFP at the predicted 63 kDa size in protein samples extracted from mycelia grown in CM for 48 h before switching to CM with or without glucose for 2 h ( S7A Fig ) . Moreover , when quantified relative to the loading control α-Tubulin , Abl1RFP was more abundant following growth in glucose-limiting ( -Glc ) conditions than in glucose-sufficient ( +Glc ) conditions ( S7B Fig ) . When considered with Fig 1A , these results suggest ABL1 is expressed , and the Abl1 protein accumulates , in the presence of glucose , but the protein remains abundant when glucose is depleted and ABL1 expression is downregulated . Additional evidence that the Abl1 protein is functional under glucose-limiting conditions when ABL1 gene expression is downregulated is shown in S7C , D . After 2 h growth in liquid media lacking glucose ( following 48 h growth in CM with glucose ) , the mycelia of Δabl1 H1:RFP strains carried significantly more nuclei ( p ≤ 0 . 05 ) than WT H1:RFP mycelia . Also , consistent with increased mitosis in Δabl1 mutant strains , the mass of dry weight mycelia was also increased in Δabl1 H1:RFP mutant strains under glucose starvation conditions compared to WT ( S7E Fig ) . Thus , the Abl1 protein functions as a brake on mitosis and growth when cellular glucose becomes limiting . To be an efficient brake , we next predicted that although ABL1 expression was induced by glucose , the Abl1 protein would be maintained in an inactive state by glucose or a downstream metabolite . Fig 6D shows how treatment of WT spores with the glucose analogue 2-deoxyglucose ( 2-DOG ) mimicked glucose treatment or the loss of ABL1 function by inducing long germ tubes and inhibiting appressorium formation . 2-DOG is phosphorylated to the non-metabolizable G6P analogue 2-DOG-P , resulting in glycolysis inhibition and ATP depletion . This suggests glucose or G6P , but not downstream metabolites , activate TOR by inactivating Abl1 and confirms Abl1 as a glucose-responsive , negative-acting regulator of TOR . Together , we can confidently propose that glucose or G6P ( directly or indirectly ) inhibits Abl1 function in order to activate TOR and drive mitosis . ABL1 deletion activates TOR in the absence of glucose signaling ( Fig 6E ) . Abl1 thus transmits inhibitory glucose depletion signals to TOR . Δtps1 strains are non-pathogenic on rice plants [39] . Because Tps1 was shown to be epistatic to ABL1 ( S2A Fig ) , and necessary for ABL1 gene expression ( Fig 1A ) , we reasoned that treating Δtps1 mutant strains with rapamycin ( or overexpressing ABL1 ) might improve virulence . However , although defects in Δtps1 appressoria function have been reported in the literature [39] , S2C Fig shows that on rice leaf surfaces , Δtps1 mutant strains formed appressoria at the same rate as WT regardless of whether or not spores were first treated with Rap . Moreover , S2D Fig shows that Δtps1 appressoria penetrated at the same rate as WT regardless of Rap treatment . Nonetheless , S2E Fig shows that regardless of Rap treatment , Δtps1 growth was attenuated in the host rice cell . This initially suggested to us that reduced ABL1 expression in Δtps1 strains was not equivalent to the loss of ABL1 function in Δabl1 strains . However , we urge caution with the interpretation of these results . Δtps1 mutant strains , unlike Δabl1 strains , are severely attenuated for sporulation rates [25] . For WT and Δabl1 mutant strains , we required only one CM plate to provide enough spores for inoculating rice leaf sheaths . In contrast , 200 CM plates we were needed to provide enough Δtps1 spores to perform the rice leaf sheath assays . This prolonged harvesting of Δtps1 spores might bias our observations towards Type IV Δtps1 morphotypes , or , despite extensive washing of the spores , could introduce a contaminating metabolite from the media that enhances appressorium function in Δtps1 relative to Δabl1 mutant strains . Thus , while the genetic connection between G6P sensing , TPS1 and ABL1 under axenic shake conditions is clear ( Fig 1A ) , the poor sporulation rate of Δtps1 might affect our study of the Tps1 –Abl1 interaction on rice surfaces . Fully exploring the relationship between Tps1 , Abl1 and fungal virulence is a challenge that will require developing a system to induce sporulation in Δtps1 mutant strains , an ongoing endeavor of our group . We next confirmed that glucose controlled mitosis at G2 and G1 via TOR . Fig 7A shows that Ben treatment induced appressorium formation in glucose treated WT H1:RFP spores . This places G2 arrest downstream of glucose signaling . Fig 7B shows that when glucose was added to germinating WT H1:RFP spores before but not after G2 , appressorium formation and autophagy were abolished , and mitosis accelerated . Similarly , Fig 7C shows that Rap added before G2 to glucose-treated germinating spores could override the glucose signal and induce appressorium formation and autophagy , but adding Rap after G2 at 8 hpi prevented further rounds of mitosis ( by arresting at G1 ) but did not induce appressoria formation or autophagy . Together , these results indicate glucose activates TOR to prevent G2 and G1 arrest . This work supports the notion that in the absence of glucose , G2 arrest is a commitment step towards appressorium formation that is succeeded by autophagy and cell cycle re-progression to G1/G0 arrest . Rather than being arrested at G1 , we next discovered that appressorial nuclei were maintained in a reversibly quiescent/ G0 state dependent on the glucose-ABL1-TOR signaling axis . This was determined by asking if treating terminally differentiated appressoria with glucose activated TOR and re-started the cell cycle . We added 1% ( w/v ) Glc ( or water as a no treatment control ) to WT H1:RFP strains at 18 hpi . At this time point , autophagic degeneration of conidial nuclei was complete and a single daughter appressorial nucleus remained ( Fig 8 ) . When viewed again at 48 hpi , no further rounds of mitosis were evident in untreated samples , indicating terminally differentiated appressorial cells had exited the cell cycle . In contrast , by 48 hpi , samples treated with glucose at 18 hpi demonstrated several phenotypes , including hyphal branching from the germ tube , that were all characterized by carrying more than one nucleus . These results demonstrate how appressorial nuclei can re-enter mitosis in the presence of glucose . To confirm that the observed glucose-dependent cell cycle re-entry acted via TOR , Fig 8 shows that simultaneous treatment with glucose and Rap at 18 hpi did not induce mitosis in appressorial nuclei by 48 hpi . To confirm that appressorial nuclei were held in a quiescent state at G1/G0 ( rather than at G2/M ) Fig 8 shows that simultaneous treatment with glucose and HU at 18 hpi also did not induce mitosis in appressorial nuclei by 48 hpi . Thus , glucose treatment reversed the G1/G0 quiescent state of terminal appressorial cell nuclei and induced mitosis . These results are consistent with our understanding that , following transition into the host , ATP production from glucose metabolism activates TOR and promotes fungal mitosis in rice cells [18] . These results are also interesting in light of Fig 7B which shows that adding glucose at 8 hpi did not prevent mitotic arrest by 24 hpi , suggesting both that appressorial nuclei become committed to exiting the cell cycle before mitosis and following G2 arrest , and that passage through G0 and back into the cell cycle in the presence of glucose requires more time than the 16h between 8 hpi and 24 hpi ( Fig 7B ) . This would be consistent with reports indicating that the transition from G0 to S phase is longer than the transition from G1 to S phase in mammalian cells [40] , supporting the notion that appressorial nuclei have exited the cell cycle into a resting quiescent/ G0 state . To further resolve cell cycle phase boundaries during WT and Δabl1 mutant strain spore germination , S8A Fig shows that , whereas Ben treatment of germinating WT H1:RFP spores at 8 hpi permitted the formation of normal appressoria populated with a single appressorial nucleus ( Fig 5C ) , Ben treatment at 6 hpi resulted in aberrant appressorium formation and complete nuclear degradation . This indicates that the G2 checkpoint in germinating WT spores occurs between 6 hpi and 8 hpi ( S8A Fig ) . In addition , to more accurately determine when the first G1 phase is completed in germinating Δabl1 H1:RFP spores , spores were treated with CHX at 2 hpi and 3 hpi . S8B Fig shows that in germinating Δabl1 mutant strains , G1 is completed after 2 hpi and before 3 hpi . When all our data are considered together , we propose the models in Fig 9 to account for cell cycle progression during spore germination and appressorium formation . Fig 9A shows that in untreated WT spores , or rapamycin treated WT and Δabl1 spores , TORoff arrests the cell cycle at G2 after 6 hpi resulting in the induction of appressorial formation and , independently , autophagy . Consistent with reports in yeast [11 , 14] , an autophagy-mediated TORon state then enables cell cycle re-progression through mitosis to TORoff and , in M . oryzae , glucose-dependent reversible quiescence at G1/G0 in mature appressoria ( Fig 9A ) . Consistent with the importance of TORon for mitosis , we note that active TOR prevents M phase delay in yeast [41] . Fig 9B shows that in Δabl1 or glucose-treated spores , constitutive TORon results in multiple rounds of accelerated mitosis during germination ( compared to untreated WT spores ) . The loss of cell cycle arrest under these conditions results in the loss of appressorial development and autophagy . Autophagy at G2 arrest can liberate TOR activating substrates such as glucose ( through gluconeogenesis ) , ATP , and glutamine [14 , 42] ( Fig 9A ) . Consistent with the notion that autophagy-liberated substrates promote TORon , S9A Fig shows how treatment of WT spores with exogenous glutamine partially impaired appressorium formation and resulted in elongated germ tubes . S9B Fig shows that glutamine treated WT spores displayed the Type I , II and IV morphotypes observed for germinating Δabl1 and glucose treated WT spores ( Fig 6C ) , but in different proportions , and absent Type III . This suggests glutamine and glucose might activate TOR by different pathways and/or with different efficiencies and/ or at different stages of the cell cycle . The large proportion of glutamine treated germinating spores displaying the Type IV morphotype by 24 hpi ( S9B Fig ) , compared to glucose treatment ( Fig 6C ) , might reflect reduced uptake of glutamine from the spore suspension due to its poor solubility in water . To better understand the role of intracellular glutamine in cell cycle progression , appressorium formation and autophagy , we turned our attention to the Δasd4 mutant strain . ASD4 encodes a GATA-family transcription factor involved in regulating the expression of genes required for nitrogen assimilation and glutamine metabolism through the GS-GOGAT pathway . Loss of ASD4 in Δasd4 mutant strains misregulated target genes resulting in elevated intracellular glutamine levels that , during Δasd4 spore germination , activated TOR and prevented appressorium formation [19] . Appressorium formation was restored in Δasd4 mutant strains by the treatment of germinating spores with rapamycin . We show here that in addition to abolished appressorium formation , Δasd4 mutant strains were also impaired for autophagy as determined by MDC staining at 24 hpi ( S10A Fig ) ; were impaired for glycogen mobilization ( S10B Fig ) ; demonstrated increased septation in germ tubes as determined by calcofluor white staining at 24 hpi ( S10C Fig ) ; and carried nuclei in germ tubes at 24 hpi that might result from additional rounds of mitosis , as determined by SYTO green fluorescent nucleic acid staining ( S10D Fig ) . However , while the phenotypes of Δasd4 and Δabl1 are similar , they are not equivalent because Δasd4 strains never exhibit Type IV or Type II morphotypes ( S10 Fig; [19] ) . Our interpretation of these results is that glutamine , like glucose , is a modulator of TOR activity but acts on TOR at a different stage of the cell cycle , as predicted in Fig 9A . Consequently , glutamine liberation following autophagy in germinating WT spores might rescue the cell cycle delay at G2/M .
During WT spore germination at 22°C , ABL1 mediates TORoff to arrest the cell cycle at G2 , resulting in the induction of appressorial formation and , independently , autophagy . Autophagy can liberate TOR activating substrates such as glutamine . An autophagy-mediated TORon state then enables cell cycle re-progression through mitosis to TORoff and glucose-dependent reversible quiescence at G1/G0 in mature appressoria ( Fig 9A ) . Consistent with the notion that autophagy-liberated substrates promote TORon , we noted that elevated intracellular glutamine levels ( resulting from impaired glutaminolysis ) inhibited appressorium formation in M . oryzae Δasd4 mutant strains by constitutively activating TOR [19] . However , Δasd4 [19] and glutamine treated WT spores did not display the full range of morphotypes associated with glucose treatment or ABL1 loss , suggesting glucose and glutamine converge to activate TOR at different times or via different pathways that remain to be resolved . Inactivating ABL1 function through ablation or glucose treatment compromised TOR inhibition and caused constitutive TORon status in germinating spores ( Fig 9B ) . G2 and G1 metabolic checkpoint arrests were subsequently eliminated under these conditions resulting in shortened G1/S and G2/M phases and multiple , accelerated rounds of mitosis . G1 was completed around 2 hpi , indicating TORon rapidly advances G1 to START , the point beyond which cells are committed to mitosis [43] . G2/M was completed by 4 hpi without inducing autophagy or appressorium formation . G2/M is the primary cell size control point in fission yeast [44] , suggesting a short G2 phase might obviate appressorial elaboration by preventing extended germ tube tip growth . Appressorium formation could be remediated in Δabl1 mutant strains by rapamycin treatment or by deleting RBP35 . Thus , biochemical and genetic data provide strong evidence that ABL1 controls TOR status to modulate cell cycle tuning in response to glucose signaling . Taken together , this work revealed ABL1 as a critical lynchpin connecting: 1 ) glucose and terminal appressorial cell differentiation , 2 ) glucose and TOR activity , and 3 ) glucose and cell cycle regulation . In yeast , AMPK/Snf1 represses downstream TOR pathway branches under low energy conditions in order to activate the glucose-starvation response [45] . Here , we showed conversely that ABL1 acts upstream of TOR in order to inactivate the glucose-sufficiency response . Although the ABL1 gene was expressed in response to G6P sensing by Tps1 under glucose-replete conditions , the Abl1 protein was detected under both glucose-limiting and glucose-sufficient growth conditions . However , our 2-DOG results demonstrated that glucose or G6P ( rather than ATP/AMP levels ) inhibited Abl1 protein activity in order to activate TOR under glucose-sufficient conditions . Considered together , these results are consistent with Abl1 operating as a fast acting brake on TOR function under glucose-limiting conditions . Genes encoding AMP/Snf1 kinases and their associated β- and γ- subunits are found in the genomes of many fungi , including M . oryzae [32] . However , Abl1 is not likely an AMPK β- subunit because the Δabl1 phenotype does not resemble that of the M . oryzae AMPK β- subunit deletion strain Δmosip2 , which forms appressoria [32] , and Abl1 lacks the iteration domain of AMPK β- subunits . Moreover , the Abl1 protein sequence aligns most closely in yeast ( 39% identity ) with MDG1/YNL173C [46] , a little characterized membrane bound S . cerevisiae protein that also carries the AMPK β-like GBD domain but not the AMPK β-subunit iteration domain and is involved in a complex genetic pathway linking pheromone signalling and cell polarity . Interestingly , MDG1/YNL173C physically interacts with ATG1 on a proteome chip , although the mechanism and physiological significance is not known [47] . ATG1 is a downstream substrate of TOR , but evidence from Drosophila suggests ATG1 can also be an upstream regulator of TOR [48] . MDG1/YNL173C is also a component of the yeast eisosome [49] , a membrane microdomain that interacts with TORC2 [50] . Although Abl1 , according to PSORTII , is not predicted to be membrane localized , these potential links between MDG1/YNL173C and TOR in yeast might give tantalizing hints about how Abl1 , and thus glucose , regulates TOR in M . oryzae . Exploring the biochemical connections between glucose/G6P , Abl1 and TOR will be a future goal . A surprising outcome of this work has been deducing the role of glucose in M . oryzae cell cycle regulation . In contrast to genotoxic stresses , the molecular mechanisms underlying glucose cell cycle checkpoints are not well understood [51] . Here , we showed how ABL1 regulates TOR and tunes the cell cycle in order to determine cell morphogenesis in response to glucose or G6P ( Fig 9 ) . Interestingly , the effect of glucose treatment or ABL1 loss on cell cycle progression was stochastic such that , in contrast to Fig 9A which holds true for all germinating WT spores under glucose starvation conditions , the scheme in Fig 9B is inherently unstable: some germinating Δabl1 or glucose treated WT spores undertook more rounds of mitosis and septation than others while 25% of such spores formed morphologically normal ( Type IV ) appressoria with single nuclei and one septation event . Moreover , multiple rounds of septation and misregulated ( rather than delayed ) autophagy and glycogen mobilization were cell type-specific defects associated with Type I-III Δabl1 morphotypes rather than resulting directly from the loss of ABL1 . We thus propose that glucose treatment or the loss of ABL1 increases cell cycle variability such that cell cycle sharpness is lost and appressorium development becomes stochastic rather than deterministic . This stochasticity or loss of coherence is reminiscent of sporadic unbudded arrest in cln2Δ cln1Δ double mutants of yeast , where 26% of cells failed to bud due to the loss of a positive feedback loop ensuring robust cell cycle entry [52] . Similarly , ABL1 might be required to eliminate stochastic variability and boost the robustness of cell cycle regulation by providing feedback reinforcement during appressorial differentiation . This could be achieved using a bistable switch that toggles between two discrete states ( ie . TORoff and TORon , Fig 9A ) and prevents cell cycle slippage by promoting settling into mutually exclusive interphase and M-phase states [53] . Hysteresis ( memory ) is a defining feature of a bistable switch [53] . The commitment of appressorial nuclei to G0 following G2 arrest , even if glucose is added between these two phases , indicates hysteresis because one state ( TORoff at G0 ) is dependent on a previous state ( TORoff at G2 delay ) . Thus , ABL1 might create a bistable switch to enforce robust cell cycle entry under glucose starvation conditions . Conversely , glucose or glucose mimicking Δabl1 mutants would perturb the putative bistable switch ( and hysteresis ) by facilitating a constitutive TORon status resulting in the stochastic loss of cell cycle robustness . Treatments that increase cell cycle variability could thus be leveraged as a means to eliminate appressorium formation by important phytopathogens . This study unveiled a novel glucose-mediated signaling axis and showed it engaged metabolic checkpoints at G2/M and G1/G0 in order to modulate cell cycle tuning and control appressorium development . Mechanistically , Abl1 negatively regulated TOR function in response to the absence of glucose or G6P , demonstrating a novel connection between cellular glucose and TOR activity that sheds light on how TOR receives inhibitory signals . We also demonstrated direct connections between glucose and cell cycle regulation by elucidating how TOR can tune mitosis in response to the presence or absence of cellular glucose . Collectively , this study demonstrates the utility of using M . oryzae to enhance our understanding of the factors controlling cell differentiation . We suggest that this relatively simple system could be leveraged towards further fundamental discoveries in development that might impact our understanding of the pathologies resulting from TOR and/or cell cycle dysregulation . This might be achieved by exploiting , for example , appressorial differentiation as an adjustable TOR readout .
Strains were grown at 24°C on complete medium ( CM ) or Cove's minimal nitrate media ( MM ) [28 , 54] , unless otherwise noted . WT and mutant strains used in this study are listed in S4 Table . Fungal spores were isolated from 12–14 day-old plate cultures and resuspended at a rate of 1×105 conidiospores/ ml in 0 . 2% gelatin . Three-week-old seedlings of susceptible rice ( Oryza sativa ) cultivar , CO-39 , were used for spray assays as described previously [28] . Lesion formation was examined 5-day post-inoculation . Images of the infected leaves were taken by using an Epson Perfection V550 scanner at a resolution of 600 dpi . Live-cell imaging was performed at 22°C as described previously [55] using 3 cm-long leaf sheath segments from three week-old rice plants and injecting one end of the sheath with a spore suspension of 1 x 105 spores/ml in 0 . 2% gelatin . At the time points indicated , leaf sheaths were trimmed and observed using a Nikon Eclipse 50i microscope and a Nikon D100 digital net camera . The average rates of appressorium formation and penetration , and IH cell-to-cell movement from the first infected cell , were determined for each strain , in triplicate , by analyzing 50 spores or appressoria per rice cuticle [55] . All targeted gene deletion mutants were generated using the split marker approach described by Wilson et al [26] , in which a selectable marker replaces all or part of the native gene of interest . The ABL1 gene was replaced in Guy11 ( WT ) , Guy11 H1:RFP ( WT H1:RFP ) , Δtps1 , and the Δrbp35 parental backgrounds using the ILV1 gene conferring resistance to sulphonyl urea [26] . Primers were designed to amplify a 1 kb sequence upstream and a 1 kb sequence downstream of ABL1 ( S5 Table ) . The Δabl1 mutant strain was complemented with the full length ABL1 gene , or ABL1 fused to GFP , using plasmids that were constructed by the yeast GAP-repair approach described in Li et al . [54] , and the primers listed in S5 Table . Constructs were transformed into Δabl1 protoplasts and transformants were selected by hygromycin resistance [19 , 26] . WT and Δtps1 strains were grown for 48 h in CM before switching to 1% glucose minimal media , or glucose starvation media , as indicated , for 16 h . Mycelia were frozen in liquid nitrogen , and lyophilized for 36 hrs . RNA was extracted from fungal mycelium using the RNeasy mini kit from Qiagen . RNA was converted to cDNA using the qScript reagents from Quantas . Real time quantitative PCR was performed on an Eppendorf Mastercycler Realplex using the recommended reagents with the specific primers for ABL1 and TUB1 showed in S5 Table . qPCR data was analyzed using the Realplex software and the ΔΔCt method [56] . Values are the average of three results from at least two independent biological replicates . Thermocycler conditions were: 10 min at 95°C , followed by 40 cycles of 95°C for 30 sec , 63°C for 30 sec and 72°C for 30 sec . Fungal spores were collected from 12–14 day-old plate cultures and resuspended at a rate of 1×105 conidia/ml . 200 μl of the spore suspensions were inoculated onto hydrophobic plastic coverslips ( Fisherbrand ) and/or hydrophilic glass slides ( Fisherbrand ) to evaluate appressoria formation at 24 hpi . All treatments were performed at 22°C . Appressorium formation rates were determined by counting the number of appressoria formed from 50 spores per coverslip or slide , repeated in triplicate for each strain and treatment [19] . Nuclei number was determined from 100 spores per coverslip , repeated in triplicate for each strain and treatment . The following treatments at the respective final concentrations were added to the spore suspensions at the indicated time points and analyzed at 24 hpi: 100–200 nM Rapamycin ( Rap; LC Laboratories , USA ) , 10 mM monobutyryl cyclic AMP ( cAMP; Sigma-Aldrich , USA ) , 50 mM hydroxyurea ( HU; Fisher Scientific , USA ) , 30 μM benomyl ( Ben; Fisher Scientific , USA ) , 2 mM cyclohexamide ( CHX; Sigma-Aldrich , USA ) , 1% ( w/v ) glucose ( Glc; Fisher Scientific , USA ) , 5 mM autophagy inhibitor 3-MA ( Fisher Scientific , USA ) , 0 . 02% ( w/v ) calcofluor white ( Sigma Aldrich , USA ) , 100 mM Monodansylcadaverine Crystallin ( MDC; Sigma-Aldrich , USA ) , 0 . 5% ( w/v ) 2-deoxyglucose ( 2-DOG; Fisher Scientific , USA ) and 5 mM L-glutamine ( Fisher Scientific , USA ) . Images were taken using a Nikon A1 laser scanning confocal mounted on a Nikon 90i compound microscope ( software version: NIS Elements 4 . 13 Build914 ) at the University of Nebraska-Lincoln Morrison Microscopy Core Research Facility . Transmitted light and fluorescence for td tomato were imaged with a 561 . 5 nm laser . td tomato fluoresce was detected at 570–620 nm . MDC and calcofluor white fluorescence was detected at 425–475 nm . | TOR kinase coordinates cell development with nutrient availability . Due to its roles in many debilitating diseases , it is important that all aspects of TOR regulation are understood . Nonetheless , how cellular glucose controls TOR function and downstream activities is largely unknown . Here , we discovered and characterized a negative-acting TOR regulator , encoded by ABL1 , necessary for infection-related development of the rice pathogen Magnaporthe oryzae . During spore germination , in the absence of glucose , the Abl1 protein inactivates TOR at two cell cycle checkpoints . This results in one round of prolonged mitosis that promotes autophagy and induces differentiation of the specialized rice-infecting appressorial cell . In contrast , glucose or glucose-6-phosphate ( but not downstream metabolites ) inactivates the Abl1 protein . This in turn activates TOR signaling to promote multiple , brisk rounds of mitosis that abolishes the cell cycle checkpoint delays necessary for inducing appressoria . We thus reveal a novel glucose-ABL1-TOR signaling axis controlling appressorial differentiation by modulating cell cycle progression in response to glucose . Together , our results are significant because they provide molecular insights on three poorly elucidated aspects of cell growth and development in any system: how glucose can regulate TOR , how glucose can regulate the cell cycle , and how TOR receives inhibitory signals . | [
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] | 2017 | Glucose-ABL1-TOR Signaling Modulates Cell Cycle Tuning to Control Terminal Appressorial Cell Differentiation |
Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions . In this work we use as constraints a small set of experimental metabolic fluxes , which reduces the region of feasible metabolic states . Once the region of feasible flux distributions has been defined , a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated . These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes . The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change ( transcriptional regulation ) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations ( metabolic regulation ) . The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae . The enzymes with transcriptional regulation showed enrichment in certain transcription factors . This has not been previously reported . The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases .
Systems Biology aims to use mathematical models to integrate different kinds of data in order to achieve a global understanding of cellular functions . The data to be integrated differ both in their nature and measurability . The availability of DNA microarrays allows for the comparative analysis or mRNA levels between different strains and conditions . These data provide genome-wide information , and changes in expression at different conditions are expressed in statistical terms such as p-values or Z-scores that quantify the level of significance in transcriptional changes . The availability of annotated genome-scale metabolic networks allowed mapping of the transcriptional changes in metabolic genes on to their corresponding metabolic pathways and defining significantly up or down regulated sub-networks [1] . Even though this allows for identification of transcriptional hot-spots in metabolism , this does still not provide information about whether there are any changes in metabolic fluxes in these pathways , as it has been shown that in general there is no clear correlation between gene expression and protein concentration [2] or metabolic flux [3] , [4] . Metabolic fluxes are the result of a complex interplay between enzyme kinetics , metabolite concentrations , gene expression and translational regulation . Metabolic fluxes can be directly measured using 13C labeling experiments [5] . However , flux data obtained using this approach differ from gene expression data in two main features: 1 ) their determination is only possible for a relatively small subset of all the reactions in a genome-scale metabolic network and 2 ) they are indirect data in the sense that the fluxes are quantified obtained by fitting measured labeling patterns using a simple metabolic model . The complexity of the mRNA-flux dependence and the disparity in the nature of both kinds of data make their integration an important challenge . In this paper we propose a method to integrate gene expression data with flux data by transforming a limited amount of quantitative flux data into a genome-scale set of statistical scores similar to the one obtained from DNA microarrays . In order to do that , a set of experimental exchange fluxes are fixed for each of the studied conditions or for each of the strains investigated , and a sampling algorithm is then used to obtain a set of flux distributions satisfying the experimental values . This approach allows for obtaining means and standard deviations for each flux in the genome-scale network . From the mean and standard deviation it is possible to derive statistical scores for the significance of flux change between conditions [6] , [7] . Random sampling in the region of feasible flux distributions has been previously used to study the statistical distribution of flux values and determine a flux backbone of reactions carrying high fluxes [8] as well as to define modules of reactions whose fluxes are positively correlated [9] , [10] . Also mitochondria related diseases have been analyzed using random sampling [11] . All the works published so far used the Hit and Run algorithm to perform the sampling [7] . By dividing the average difference among two conditions ( e . g . carbon sources or mutant strains ) by its standard deviation , it is possible to obtain Z scores for each metabolic flux . These scores can be transformed into p-values that measure the significance of change of each flux ( see methods ) . By comparing these p-values with the p-values derived from gene-expression arrays , the enzymes in the network can be classified as: 1 ) enzymes that have a significantly correlated change both in flux and expression level ( reactions showing transcriptional regulation ) , 2 ) enzymes that show a significant change in expression but not in flux ( we will refer to them as showing post-transcriptional regulation ) and 3 ) enzymes that show significant changes in flux but not a change in expression ( metabolic regulation ) . Hereby we provide a framework that allows for global classification of reaction fluxes into those that are transcriptionally regulated , post-transcriptionally regulated and metabolically regulated ( see Fig . 1 ) . This will have substantial impact on the field of metabolic engineering where changes in gene-expression are often used as the key means to alter metabolic fluxes . In the paper we show the use of the presented framework for the analysis of the yeast Saccharomyces cerevisiae grown at different growth conditions and for the analysis of different deletion mutants . The combined use of random sampling of genome-scale metabolic networks and expression data allows for global mapping of reactions that are either transcriptionally or metabolically regulated . This information can be used to guide the engineering of microbial strains or as a diagnosis tool for studying metabolic diseases in humans . In particular we should highlight that reactions in which there is no relation between gene transcription level and metabolic flux are not suitable targets for flux increase via gene over-expression . Through analysis of different data sets the method revealed that many changes in gene expression are not correlated with a corresponding change in metabolic fluxes . The use of gene-expression data alone can therefore be misleading . However , our method allowed for identification of many specific reactions that are indeed transcriptionally regulated , and we further identified that the expression of these enzymes is regulated a few key transcription factors . This fact suggests that the regulation of metabolism has evolved to contain a few flux-regulating transcription factors that could be the target for genetic manipulations in order to redirect fluxes .
Here we propose a sampling method that finds extreme solutions among the feasible flux distributions of the metabolic network . These solutions correspond to the corners in the region of allowed flux distributions , and in mathematical terms they are elements of the convex basis of the region of feasible solutions ( which is a convex set ) . The COBRA Toolbox [12] includes a random sampling option that uses the Hit and Run algorithm [13] to obtain points uniformly distributed in the region of allowed solutions . The difference between the two sampling methods is illustrated in Fig . 2 . In order to assess the accuracy of our sampling method to estimate the average fluxes and their standard deviations , we compared a set of internal fluxes measured with 13C labeling [14] with predictions using 500 sampling points obtained using the sampling method in the convex basis and 500 sampling points obtained using the sampling algorithm implemented in the COBRA Toolbox . The results are summarized in Table 1 where our method is labeled Convex Basis ( CB ) , because it samples elements of the convex basis of the region of allowed solutions ( see above ) , and the method from the COBRA Toolbox is labeled Hit and Run ( HR ) . The Z values in the table are the number of standard deviations that the real value is deviating from the calculated mean . The means obtained by the two sampling methods are very similar for most of the reactions; however the standard deviations found using the HR algorithm are significantly smaller . With the HR method the real values for the fluxes in many cases deviate several standard deviations from the mean , A high value of Z indicates that the real value has a very low chance of being obtained using the considered sampling method ( or in other words: the real value does not belong to the family of solutions that is generated by the sampling method ) . The number of samples with the HR algorithm was increased up to 5000 to check possible effects of the sample size on the standard deviation . Only small increases were observed for the standard deviations of the studied fluxes . Using the CB algorithm we obtain higher standard deviations and the real flux is for most reactions less than one standard deviation away from the mean flux . We can therefore conclude that the CB sampling method gives more realistic standard deviations for the fluxes . This is important if we want to compare the significance of flux changes between conditions . An underestimated standard deviation would make some flux changes appear as being significant even though they may not be in reality , and our method therefore gives a more conservative list of significantly changed reaction fluxes than the HR algorithm . To evaluate our method we used data for the yeast S . cerevisiae . Data from growth on four different carbon sources ( glucose , maltose , ethanol and acetate ) in chemostat cultures and five deletion mutants ( grr1Δ , hxk2Δ , mig1Δ , mig1Δmig2Δ and gdh1Δ ) grown in batch cultures were used . The exchange fluxes and gene expression data for the mentioned conditions have been published earlier [15]–[17] . Our method obtains probability scores for each enzyme in the metabolic network ( see methods ) and this allowed us to classify the enzymes as transcriptionally regulated ( correlation between flux and gene expression ) , post-transcriptionally regulated ( changes in gene expression don't cause changes in flux ) and metabolically regulated ( changes in flux are not caused by changes in gene-expression ) . The cut-off chosen for this classification was a probability score above 0 . 95 . Tables 2 and 3 show the 10 top scoring enzymes in each group ( or fewer when less than 10 enzymes had a score exceeding 0 . 95 ) . The method is illustrated in Fig . 3 . The method to identify the significance of flux changes relies on a set of measured external fluxes , and in some cases strains that don't show significant changes in external fluxes have changes in internal fluxes [18] . These changes cannot be identified with our method , and our estimations of the significance of flux changes can therefore be seen as conservative estimates . The lists of transcriptionally and metabolically regulated reactions are therefore more reliable than the list of post-transcriptionally regulated reactions ( in which some fluxes may be changed in reality but their change pass undetected ) . The reactions showing transcriptional regulation form a set of putative targets where enzyme over-expression or down regulation will influence the flux through these reactions . The reactions showing metabolic regulation points to parts of the metabolism where the pools of metabolites are possibly increasing or decreasing in connection with transcriptional changes and hereby counteracting possible changes in enzyme concentration as a result of transcriptional changes . This knowledge can be used to identify whether one should target changes in enzyme concentration ( vmax changes ) , e . g . through over-expression , or changes in enzyme affinity ( Km changes ) , e . g . through expression of heterologous enzymes , in order to alter the fluxes .
The steady state condition and the irreversibility of some reactions impose limitations on the flux distributions attainable by the cell [18] . The set of feasible solutions can be further constrained by fixing some fluxes to their experimental values . In general , the fluxes most accessible to experimental determination are those corresponding to uptake or secretion rates . After fixing a subset of fluxes , genome scale models still have a large number of degrees of freedom . In this study we used the genome scale model iFF708 for S . cerevisiae [27] . Random sampling has previously been performed [7] by enclosing the region of allowed solutions in a parallelepiped with the same dimensions as solution space ( the null space of the stoichiometric matrix ) and generating random points inside this parallelepiped . The points that lie inside the region of possible solutions are then selected . The COBRA Toolbox [12] uses a Hit and Run algorithm to generate random points in this way . In this work instead of sampling inside the region of allowed solutions we sampled at its corners . In order to obtain corners in the space of allowed solutions we used the simplex method with a random set of objective functions to be maximized . The maximization of each of these objective functions will give a corner in the space of solutions . The constraints imposed upon each optimization are: ( 1 ) ( 2 ) ( 3 ) The values of the measured fluxes ( vexp ) are different between conditions . This fact changes the shape of the region of feasible solutions between different conditions . S is the stoichiometric matrix of the network . In order to reduce the effects of internal loops we first identified all the reactions that can get involved in loops using the FVA ( Flux Variability Analysis ) option in the COBRA Toolbox . The reactions that can be involved in loops are unbounded and show the default maximal or minimal value set in the COBRA Toolbox ( 1000 or −1000 ) . If these bounds were kept , the means and standard deviations for these reactions would be unrealistic [6] and cannot be used for further analysis . In order to reduce the effect of loops , the default maximal and minimal fluxes for the reactions involved in loops , were set to a smaller value in order to reduce the loop effect . In order to select an appropriate value the bounds were increased from 0 in steps of 0 . 1 until the minimal value that allows obtaining flux distributions consistent with the experimental fluxes is found . These values went from 1 to 15 mmol h−1g-DW−1 depending on each condition . Also no weights ( eq . 4 ) were assigned to the reactions involved in loops in order to avoid objective functions that maximize the activity of loops . Random objective functions were generated by selecting random pairs of reactions and assigning them random weights ( the reactions involved in loops were excluded from these choices ) . The weights ( wi ) assigned to each reaction were generated by dividing a random number between 0 and 1 by the maximal flux for this reaction obtained using FVA . This normalization was made to account for the different size orders of the different reactions . The objective functions take the form: ( 4 ) One solution is obtained for each of the objective functions generated . Our objective is to obtain means and standard deviations for each flux in each of the compared conditions and use them to get a Z-score quantifying the significance of change in each flux between the considered conditions . This score is equal to the difference between the means in each of the conditions divided by the standard deviation of this difference ( note that the variance of the difference is the sum of the two variances and the standard deviation its square root ) . ( 5 ) The difference between averages in the numerator follows a normal distribution ( according to the central limit theorem ) with a standard deviation equal to the standard deviation of the flux ( the denominator in eq . ( 5 ) ) divided by the square root of the number of samples . Therefore , Z itself follows a normal distribution with a standard deviation equal to the inverse of the square root of the number of samples . The Z score measures the significance of change in terms of standard deviations . If the error in the Z score is lower than 0 . 15 , no information would be lost in terms of classifying a reaction as significantly changed or not . The order of size of a genome-scale model is about 1000 reactions . A reasonable accuracy for the Z-scores would be to expect errors higher than 0 . 15 on the Z score only for 1 reaction in the whole model . This means a p-value of 0 . 001 . If we want to keep the error on the Z score under 0 . 15 with a probability of 0 . 999 we need 500 samples , and this was therefore selected as the sampling number . The Z-scores can be transformed into probabilities of change by using the cumulative Gaussian distribution . Once we have Z-scores for the significance of flux changes and Z-scores for the significance of gene-expression changes we can obtain probabilities of having correlated expression and flux changes for each enzyme . An increase in enzyme expression can result in an increase of flux ( transcriptional regulation ) . In order to evaluate the probability for a reaction of being transcriptionally regulated we multiply the probability of its enzyme level changing by the probability of its flux changing in the same direction ( obtained using the cumulative normal distribution ) . ( 6 ) ( 7 ) If there is a decrease in expression and a decrease in flux , both Z-scores are negative and we will use the absolute values of the Zs in eq . ( 6 ) . If there is an increase in expression and a negative flux becomes more negative , we will use the absolute value of the Z-score for the flux change . If the direction of the flux changes between conditions , this change must be driven by the metabolic concentrations and no by transcriptional regulation , therefore a Ptri of zero is assigned by default . In the same way as in eq . ( 6 ) we can define probabilities for the expression level changing and for the flux not changing ( post transcriptional regulation ) . ( 8 ) ( 9 ) Now we use the error function because we want to evaluate the probability of change in any direction . The absolute value of Z is used in all the cases . The probability of a change in flux but not in transcription ( metabolic regulation ) can be obtained for each reaction as follows: ( 10 ) Each of these three probabilities can be associated to each enzyme in the metabolic network . Table 4 summarizes the criteria to assign each type of regulation . | The sequencing of full genomes and the development of high-throughput analysis technologies have made available both genome-scale metabolic networks and simultaneous transcription data for all the genes of an organism . Genome-scale metabolic models , with the assumption of steady state for the internal metabolites , allow the definition of a region of feasible metabolic flux distributions . This space of solutions can be further constrained using experimental flux measurements ( normally production or uptake rates of external compounds ) . Here a random sampling method was used to obtain average values and standard deviations for all the reaction rates in a genome-scale model . These values were used to quantify the significance of changes in metabolic fluxes between different conditions . The significance in flux changes can be compared to the changes in gene transcription of the corresponding enzymes . Our method allowed for identification of specific reactions that are transcriptionally regulated , and we further identified that these reactions can be ascribed to a few key transcription factors . This suggests that the regulation of metabolism has evolved to contain a few flux-regulating transcription factors that could be the target for genetic manipulations in order to redirect fluxes . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"computational",
"biology/metabolic",
"networks"
] | 2010 | Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes |
With the paucity of new drugs and HIV co-infection , vaccination remains an unmet research priority to combat visceral leishmaniasis ( VL ) requiring strong cellular immunity . Protein vaccination often suffers from low immunogenicity and poor generation of memory T cells for long-lasting protection . Cysteine proteases ( CPs ) are immunogenic proteins and key mediators of cellular functions in Leishmania . Here , we evaluated the vaccine efficacies of CPs against VL , using cationic liposomes with Toll like receptor agonists for stimulating host immunity against L . donovani in a hamster model . Recombinant CPs type I ( cpb ) , II ( cpa ) and III ( cpc ) of L . donovani were tested singly and in combination as a triple antigen cocktail for antileishmanial vaccination in hamsters . We found the antigens to be highly immunoreactive and persistent anti-CPA , anti-CPB and anti-CPC antibodies were detected in VL patients even after cure . The liposome-entrapped CPs with monophosphoryl lipid A-Trehalose dicorynomycolate ( MPL-TDM ) induced significantly high nitric oxide ( up to 4 fold higher than controls ) mediated antileishmanial activity in vitro , and resulted in strong in vivo protection . Among the three CPs , CPC emerged as the most potent vaccine candidate in combating the disease . Interestingly , a synergistic increase in protection was observed with liposomal CPA , CPB and CPC antigenic cocktail which reduced the organ parasite burden by 1013–1016 folds , and increased the disease-free survival of >80% animals at least up to 6 months post infection . Robust secretion of IFN-γ and IL-12 , along with concomitant downregulation of Th2 cytokines , was observed in cocktail vaccinates , even after 3 months post infection . The present study is the first report of a comparative efficacy of leishmanial CPs and their cocktail using liposomal formulation with MPL-TDM against L . donovani . The level of protection attained has not been reported for any other subcutaneous single or polyprotein vaccination against VL .
Visceral leishmaniasis ( VL ) caused by Leishmania donovani is a fatal disease with an estimated 360 , 000 new cases all over the world with almost 10% annual case fatality in the Indian subcontinent alone [1] . It is a neglected tropical disease inevitably associated with poverty and immunosuppression . High toxicity of available drugs ( amphotericin B , miltefosine and paromomycin ) , HIV co-infection , and resistant parasites pose a global threat against leishmaniases . Despite recent advances in pharmaceutics and molecular immunology , there is no licensed vaccine available against the disease till date [2] . Encapsulation of antigens within nanocarriers promises stable and customized vaccine delivery to related immune cells against various intracellular pathogens including Leishmania . Although incompletely understood , the lack of clinical efficacy for peptide-based vaccines may be related to several factors such as poor immunogenicity of the subunit antigens , inappropriate functional polarity of T cells and Leishmania induced immunosuppression [2] , [3] . Thus , there remains considerable scope for improvement of antileishmanial vaccine design to maximize the chances of clinical benefit . Outcome of prophylactic vaccination largely depends on the choice of right immunopotentiating adjuvants and/or delivery systems coupled to right antigen ( s ) . Cationic liposomes protect the labile antigens from lysosomal degradation and take the advantage of electrostatic interactions with the cells' negative charge which makes them a natural target for antigen presenting cells ( APCs ) , crucial for immune stimulation [4] , [5] . Monophosphoryl lipid A ( MPLA ) is a Toll-like receptor 4 ( TLR4 ) agonist with more than 100 , 000 human doses safely administered as a part of licensed hepatitis B and Human papillomavirus vaccines [6] . Mycobacterial glycolipid trehalose-6 , 6′-dimycolate ( TDM; cord factor ) is a potent immunostimulant known for its macrophage activation properties and induction of proinflammatory cytokines , and anti-tumor activity [7] . Recently , TDM has been shown to act via macrophage receptor with collagenous structure ( MARCO ) , TLR2 , CD14 and also macrophage-inducible C-type lectin ( Mincle ) receptors to exert its immunomodifying effects [8] , [9] . When used together , both the adjuvants i . e . MPL and TDM non-specifically activate the immune system , allowing a better response to the associated immunogen [10] . Recently , we have developed a cationic liposome and MPL-TDM ( monophosphoryl lipid-trehalose dicorynomycolate ) delivery platform that is suitable for subcutaneous delivery of leishmanial antigens in mice model [11] . Compared to an array of antigens that have been tested , very few are sufficiently promising to be carried out to Phase I clinical trials or advanced preclinical work against VL [12] . Lysosomal cysteine proteases ( CP ) of Leishmania , including cysteine proteases type I ( CPB ) , II ( CPA ) and III ( CPC ) , are conserved and functionally important proteolytic enzymes regulating cell cycle and host-parasite interactions that have been found to be immunogenic in mice . Further , CPs are established virulence factors involved in parasitic survival , autophagy and metacyclogenesis essential for onset of the disease and are being increasingly exploited as serodiagnostic markers , drug targets and vaccine candidates against Leishmania [13] , [14] . Protein and DNA vaccine attempts using CPA , CPB and CPC in different adjuvants have been reported against L . major [15] , [16] and L . infantum [17] in mice and canine models [18] . However , these research efforts have been largely restricted to cutaneous and zoonotic VL with variable success rate but have never been investigated against L . donovani , the causative agent of kala-azar . Although mouse serves as a good animal model for dissecting the protective immune responses with the available immunological reagents , murine infection is usually self-curing and differs from human VL . In contrast , hamsters closely mimic clinical symptoms of human VL characterized by severe immunosuppression ( Th2 response ) and develop progressive fatal infection when challenged with L . donovani . Higher innate susceptibility of this model compared to mice makes them a better choice for initial vaccine trial against VL to ensure its success in human [19] . Hence we selected Syrian golden hamsters to assess the protective potential of leishmanial CPs . Our aim in this study was therefore towards a comparative evaluation of prophylactic potential of CPA , CPB and CPC , individually , as well as a multi-antigen cocktail vaccine , maximizing the exposed antigenic epitopes to protect against VL . Evidence is presented here for the first time on the efficacy of highly potent liposomal formulations of recombinant CPA , CPB and CPC along with MPL-TDM adjuvant , in a hamster model against L . donovani , with an aim towards its further development for human vaccination .
The present study was approved by the Ethical Committee on Human Subjects at the Indian Institute of Chemical Biology and Ethical Committee , Calcutta School of Tropical Medicine , Kolkata . Written informed consent was obtained from each patient and healthy donors enrolled in the study and the patient/parent was informed that he/she was free to voluntarily withdraw from the study at any time . Written consent was obtained from parent or guardian in case of minors . The physicians explained the nature of the investigation and the risks involved to each patient ( or parent or guardian in case of minors ) , prior to recruitment . A copy of the patient consent form was submitted to the Ethical Committee . The animal experiments were approved by the Animal Ethical Committee ( 147/1999/CPSCEA ) of the institute , according to the National Regulatory Guidelines issued by the Committee for the Purpose of Control and Supervision on Experimental Animals ( CPCSEA ) , under the Division of Animal Welfare , Ministry of Environment and Forest , Government of India . Studies were performed with 4–6 weeks old Syrian golden hamsters ( Mesocricetus auratus ) reared in pathogen-free animal care facility of the Indian Institute of Chemical Biology . A strain of L . donovani ( MHOM/IN/83/AG83 ) originally isolated from an Indian kala-azar patient was maintained by serial passage in Syrian golden hamsters as described earlier [20] . Parasites from stationary-phase culture were sub-cultured to maintain an average density of 2×106 cells/ml . Plasmids containing full-length cpa ( GenBank accession number KF018070 ) , cpb ( GenBank accession number KC609324 ) and cpc ( GenBank accession number JX968801 . 1 ) from L . donovani ( pET28a-cpa , pET28a-cpb and pET28a-cpc ) were cloned , expressed and purified from inclusion bodies using sarkosyl ( Sigma-aldrich , St . Louis , MO ) [21] . Genomic DNA isolated from L . donovani promastigotes was subjected to polymerase chain reaction ( PCR ) with sets of gene specific primers corresponding to cpa , cpb and cpc genes based on Leishmania major and Leishmania infantum gene sequences ( Table S1 , supporting information ) . PCR conditions for rCPA and rCPB were one cycle of 5 min at 94°C , 35 cycles of 1 min at 94°C , 1 min at 59°C , and 1 min 10 s at 72°C , followed by a final cycle of 7 min at 72°C . PCR conditions for rCPC were one cycle of 5 min at 94°C , 35 cycles of 1 min at 94°C , 1 min 20 s at 58°C , and 1 min 10 s at 72°C , followed by a final cycle of 7 min at 72°C . The PCR amplified fragments were separately cloned into NdeI/BamHI site of bacterial expression vector pET28a ( Novagen , Madison , USA ) . All the restriction enzymes were from Roche Diagnostics , Mannheim , Germany . Screening for recombinant clones was performed by growing randomly selected colonies overnight at 37°C in 5 ml of Luria-Bertani ( LB ) broth ( with 35 µg/µl kanamycin ) . Plasmid DNA were extracted from bacterial cell pellets using the QIAprep Spin Miniprep Kit ( Qiagen , Valencia , USA ) , following the manufacturer's instructions . For clone confirmation , approximately 1 µg plasmid DNA from an individual miniprep was double digested with the appropriate restriction enzymes ( NdeI and HindIII ) and the digest loaded onto a 1% agarose gel , in parallel with the molecular weight marker: 1 kb DNA ladder ( Fermentas , USA ) . Positive clones were selected on the basis of the size of the insert and confirmed by DNA sequencing ( ABI Prism , Model 377; Applied Biosystems ) . Escherichia coli BL21 ( DE3 ) transformed with each of pET28a-cpa , pET28a-cpb and pET28a-cpc constructs were grown in 500 ml of culture medium at 37°C until logarithmic phase OD600 reached 0 . 6 . Protein production was induced by adding isopropyl β-d-thiogalactoside ( IPTG ) to a final concentration of 0 . 5 mM , and incubating for an additional 4 h at 30°C . The culture was then harvested by centrifugation at 6 , 000×g , for 6 min , at 4°C , and the cell pellet was resuspended in 6 ml of resuspension buffer [25 mM Tris-HCl , 500 mM NaCl , and 1 mg/ml of Lysozyme ( Roche ) , pH 8 . 0] . The cell lysate was sonicated on ice for 5 min with 1 min pulse and 1 min interval between pulses using an ultrasonicator ( Misonix , Farmingdale , NY , USA ) . The pellet containing inclusion bodies was solubilised with solublization buffer [50 mM CAPS {3- ( Cyclohexylamino ) -1-propanesulfonic acid} buffer ( pH 11 . 0 ) , 300 mM NaCl , and 0 . 5% sarkosyl] , kept at room temperature for 30 min and finally centrifuged at 12 , 000×g for 30 min at 4°C . The supernatant containing solubilised proteins were loaded separately onto Ni2+-nitrilotriacetic acid-agarose ( Ni-NTA ) column ( Qiagen ) and purified under denaturing conditions [16] . The Ni-NTA column was pre equilibriated with equilibriation buffer [50 mM CAPS buffer ( pH 11 . 0 ) , 150 mM NaCl , 0 . 5% sarkosyl and 10 mM imidazole] . The column was washed with wash buffer [50 mM CAPS buffer ( pH 11 . 0 ) , 150 mM NaCl , 0 . 5% sarkosyl and 20 mM imidazole] and eluted with elution buffer [50 mM CAPS buffer ( pH 11 . 0 ) , 150 mM NaCl , 0 . 5% sarkosyl and 300 mM imidazole] . Bacterial endotoxins were largely removed by three consecutive washing of the protein bound Ni-NTA agarose beads with wash buffer containing 0 . 5% and 0 . 1% ( v/v ) Triton X-114 ( Sigma-Aldrich ) [22] . This was followed by washing the beads without the detergent ( in 20 volumes of wash buffer without Triton X-114 ) prior to elution . The endotoxin level in each of the recombinant proteins was determined using the chromogenic Limulus Amebocyte Lysate ( LAL ) assay kit ( QCL-1000; Lonza ) according to the manufacturer's recommendations . This step was repeated as required to maintain the endotoxin content of the purified recombinant proteins as <0 . 2 endotoxin units/µg . To refold , the purified materials were diluted 2 fold in dilution buffer containing 50 mM CAPS buffer ( pH 11 . 0 ) , 150 mM NaCl and 300 mM imidazole , and then dialyzed against 25 mM Tris-HCl , 250 mM NaCl , pH 8 . 0 and finally in 0 . 02 M PBS for 6 h at 4°C . Protein concentrations were determined using Lowry's method [23] . Purity and homogeneity of the purified proteins were checked on10% sodium dodecyl sulfate ( SDS ) -PAGE , and the gel was subsequently stained with Coomassie Brilliant Blue R-250 ( Bio-Rad Laboratories ) . ClustalW DNA alignment was performed using the ClustalW program of the European Bioinformatics Institute ( http://www . genome . jp/tools-bin/clustalw ) and multiple alignment was used to view the ClustalW alignment [24] . Cysteine protease gene sequences of different Leishmania species were derived from the GenBank . Blood plasma samples were obtained from five patients confirmed with active VL , admitted between 2009 and 2012 to the School of Tropical Medicine ( Kolkata , India ) mainly from endemic regions of Bihar and West Bengal . Five recovered cases of liposomal amphotericin B -treated patients and five normal individuals negative for the recombinant K39 strip test from a non-endemic area were included as cured and healthy controls respectively . HIV-positive individuals with VL and pregnant women were excluded from study . Heparinized , longitudinal blood samples were collected from the patients ( both with active VL and cured ) as well as healthy individuals from which about 1 ml of plasma was used for the study . Plasma was obtained from the upper layer of the gradient following centrifugation and stored at −20°C until use . Purified CPs ( rCPA , rCPB and rCPC ) ( 5 µg ) were subjected to 10% SDS-polyacrylamide gel electrophoresis [25] , transferred ( Mini-Protean II , Bio-Rad ) to nitrocellulose membrane at 85 V for 1 h and incubated with human plasma ( 1∶1000 ) from patients with active VL , cured and healthy individuals . The membranes were washed and probed with horseradish peroxidase ( HRP ) -conjugated goat anti-human IgG ( 1∶4000 ) ( Southern Biotech ) as secondary antibody . The present study was approved by the Ethical Committee on Human Subjects at the Indian Institute of Chemical Biology . Distearoyl phosphatidylcholine ( DSPC ) , cholesterol ( Sigma-aldrich ) and stearylamine ( Fluka , Buchs , Switzerland ) at a molar ratio of 7∶2∶2 were dissolved in chloroform followed by evaporating the organic solvents to form a thin film as described earlier [26] . Empty and antigen entrapped liposomes were prepared by dispersion of lipid film in either 1 ml of 0 . 02 M phosphate buffered saline ( PBS ) alone or containing 500 µg/ml of recombinant CPA ( rCPA ) , CPB ( rCPB ) or CPC ( rCPC ) . The mixture was then vortexed and the suspension was sonicated for 30 s by an ultrasound probe sonicator ( Misonix , New York , USA ) thrice with 1 min gap in between at 4°C . It was then kept on ice for 2 h to stabilize the formed liposomes . Vesicles with entrapped antigen were separated from excess free antigens by three successive washings in PBS with ultracentrifugation at 105 , 000× g for 1 h at 4°C . The protein entrapped in liposome was estimated using BSA as the standard , in presence of 0 . 8% SDS and appropriate blanks [23] . Protein integrity after liposomal encapsulation was also evaluated by 10% SDS-PAGE followed by Coomassie staining . For fluorescent liposomes , the lipid film was made with DSPC , cholesterol and stearylamine at a molar ratio of 7∶2∶2 along with 0 . 1 mg/ml of rhodamine 123 ( Rh123 ) ( Sigma Life Sciences ) as a lipophilic marker . The dry lipid film was dispersed in 0 . 02 M PBS and the excess free dye was separated from labeled liposomes by three successive washings as described above . For atomic forced microscopy ( AFM ) imaging of liposomal samples , 10 µl of the samples were deposited onto freshly cleaved muscovite Ruby mica sheets ( ASTM V1 Grade Ruby Mica from MICAFAB ) for 15–20 minutes . Mica sheets are basically negatively charged so samples bind strongly on the mica surface . After 15 min , the samples were dried by using a vacuum dryer . Sometimes the samples were gently washed with 0 . 5 ml Milli-Q water to remove molecules that were not firmly attached to the mica and the samples were dried as mentioned above . Acoustic alternative current mode AFM was performed using a Pico plus 5500 ILM AFM ( Agilent Technologies , USA ) with a piezoscanner maximum range of 9 µm . Micro fabricated silicon cantilevers of 225 µm in length with a nominal spring force constant of 21–98 N/m were used from Nano sensors , USA . Cantilever oscillation frequency at 150–300 kHz was tuned into resonance frequency . The images ( 512 by 512 pixels ) were captured with a scan size between 0 . 5 and 2 µm at a scan speed rate of 0 . 5lines/S . Images were flattened using Pico view1 . 4 version software ( Agilent Technologies ) . Image processing and analyzation was done through Pico Image Advanced version software ( Agilent Technologies ) . The mechanism of cellular uptake of liposomes was quantified by fluorescence activated cell sorting ( FACS ) using various biochemical inhibitors . Macrophages collected by peritoneal lavage of healthy , adult Syrian golden hamsters were incubated overnight in a 24-well flat bottom plate at a density of 1×106 cells/well in RPMI 1640/10% fetal bovine serum ( FBS ) . Cells were incubated in serum free RPMI 1640 with various biochemical inhibitors ( all from Sigma-Aldrich ) : 500 µM amiloride , 5 µg/ml chlorpromazine , 50 µg/ml cytochalasin D for 30 min and 10 µM colchicine for 2 h to block macropinocytosis , non-clathrin , non-caveolae dependent endocytosis , actin and microtubules mediated endocytosis , respectively [27]–[29] . Cells were subsequently treated with rhodamine 123 ( Rh123 ) -labelled cationic liposomes ( final concentration 0 . 2 mg lipid/ml with respect to DSPC ) for 1 h in the presence of the inhibitors . Cells incubated with labelled liposomes without prior treatment with inhibitors served as positive controls . Cells were detached using trypsin/EDTA , washed thrice with ice-cold 0 . 02 M PBS containing 0 . 5% FBS , and centrifuged at 2000 rpm g for 10 minutes to remove the liposomes adhered to the cell surface and analysed by flow cytometry using a FACS LSR Fortessa ( Becton Dickinson ) and FACSDiva software ( BD Biosciences ) . All vaccines were formulated with cationic liposomes as described previously . Six groups ( 35 hamsters/group for controls and 25 hamsters/group for antigens ) were immunized subcutaneously between scapulae at the back , two times at an interval of 2 weeks with or without liposomal antigens and 25 µg of MPL-TDM ( Sigma ) in a total volume of 100 µl/animal/dose ( Table S2 , supporting information ) . Groups 1 , 2 and 3 received 2 . 5 µg of rCPA ( CPA/CL+MPL ) , rCPB ( CPB/CL+MPL ) and rCPC ( CPC/CL+MPL ) respectively , entrapped in cationic liposome plus MPL-TDM . Group 4 was immunized with the cocktail of liposomal rCPA , rCPB and rCPC ( 2 . 5 µg of each protein ) plus MPL-TDM ( CPA/B/C/CL+MPL ) . Control groups received PBS or empty liposomes plus MPL-TDM . Ten days after the booster , five animals/group were subjected to analysis of the cellular and humoral responses after immunization ( Table 2 , supporting information ) . The remaining hamsters were challenged intracardially with 2 . 5×107 freshly transformed stationary-phase promastigotes in 200 µl PBS [20] . Immunological assays were carried out post immunization and after 2 and 3 months of challenge infection . The total body weight and that of liver and spleen ( upon sacrifice ) were measured for all immunized groups on days 0 , 60 and 90 days post challenge . Delayed type hypersensitivity ( DTH ) was determined 1 week after boost and at 2 and 3 months post infection . DTH was evaluated by measuring the difference in the footpad swelling at 24 h following intradermal inoculation of the test footpad with 5 µg of either rCPA , rCPB , or rCPC in a total volume of 50 µl , and the swelling of the control ( PBS injected ) footpad with a constant pressure caliper ( Starrett Company , Athol , MA ) . For cocktail immunized and control groups , DTH response was evaluated by injecting the test footpad with a mixture of rCPA , rCPB , and rCPC ( total 5 µg ) . The levels of antigen-specific serum IgG and its isotypes , IgG1 and IgG2 , were determined in serum samples from experimental hamsters 10 days after the booster and at 2 and 3 months after infection , by ELISA . In brief , 96-well microtiter plates ( Maxisorp , Nunc , Naperville , IL ) were coated overnight at 4°C with rCPA/rCPB/rCPC ( 5 µg/ml ) singly or in combination ( total 5 µg/ml ) for controls and cocktail vaccinates , diluted in 0 . 02 M phosphate buffer ( pH 7 . 5 ) . For total specific IgG determination or IgG subtyping , HRP conjugated goat anti-hamster IgG ( 1∶8000 , Southern Biotech ) or biotin-conjugated monoclonal mouse anti-hamster IgG1 and IgG2 ( 1∶1000 , BD Pharmingen , San Diego , CA ) were used as secondary antibodies . The plates were blocked with 1% BSA in PBS at room temperature for 3 h to prevent nonspecific binding . The antigen-antibody reaction was detected as described [26] . The absorbance was measured using an ELISA plate reader ( Thermo , Waltham , MA ) at 450 nm . Two weeks post immunization , macrophages ( MΦ ) collected from peritoneal exudates of vaccinated hamsters were allowed to adhere to glass cover slips in 0 . 5 ml RPMI 1640 media containing 10% FCS at 37°C in 5% CO2 as detailed elsewhere [26] . MΦ were infected with promastigotes on glass cover slips ( 22 mm2; 106 MΦ/coverslip ) at a ratio of ∼10 parasites/MΦ . The unphagocytosed parasites were removed by washing with warm PBS , and the infected MΦ were further incubated in complete medium for 4 , 16 , 24 , 48 and 72 h at 37°C in 5% CO2 . The cells were then fixed in methanol followed by staining with Giemsa for determination of intracellular parasite numbers . Prior to fixation , culture supernatants were removed at the above mentioned time points and frozen at −70°C for NO analysis . The production of nitric oxide ( NO ) in MΦ culture supernatants was determined using the Griess reagent and the results were expressed in µM nitrite [26] . Briefly , 100 µl of macrophage culture supernatants were mixed with an equal volume of Griess reagent ( 1% sulfanilamide and 0 . 1% N-1-naphthylethy-lene diamine hydrochloride in 50% H3PO4 ) and incubated at room temperature for 10 min . Absorbance was then measured at 540 nm . Intracellular reactive oxygen species ( ROS ) generation was measured using the oxidant sensitive green fluorescent dye 2′ , 7′-dihydrodichlorofluorescein diacetate ( H2DCFDA ) ( Molecular Probes ) [30] . For the experiments , differently treated and untreated peritoneal macrophages were pre-stained with 10 µM H2DCFDA ( stock 10 mM solution in DMSO ) for 30 minutes in serum-free medium , followed by washing twice with fresh RPMI . Fluorescence measurements were made using a microplate reader ( Synergy H1 , BioTek; Excitation: 485 nm; Emission: 528 nm ) , in triplicate and calculated as percent of control . The spleen cells were aseptically removed from the immunized hamsters 2 weeks after the last immunization and single cell suspensions were prepared in RPMI 1640 as detailed elsewhere [11] . The splenocytes were then washed twice , resuspended in the culture medium and viable mononuclear cell number was determined by Trypan blue exclusion [31] . The splenocytes were labeled with carboxyfluorescein succinimidyl ester ( CFSE ) ( Molecular Probes ) using a slightly modified technique , originally devised by Lyons et al . [32] . Briefly , 1×107 cells/ml were incubated with 2 µM CFSE for 10 min at 37°C . The labeling was quenched by adding one volume of cold PBS and washed twice in cold RPMI 1640 ( Sigma ) . Then the cells were cultured in triplicate in 24-well flat bottomed tissue culture plates ( Nunc , Roskilde , Denmark ) at a density of 1×106cells/well in a final volume of 1 ml and stimulated with either rCPA , rCPB or rCPC ( 5 µg/ml ) singly for single antigen immunized groups or in combination ( 5 µg/ml total ) for controls and cocktail vaccinates or conA ( 2 . 5 µg/ml ) . After five days , the cells were collected and analysed on a FACSCanto flow cytometer ( Becton Dickinson ) using the FACSDiva software . Complementary DNA was synthesized using SuperScript III First-Strand Synthesis kit ( Invitrogen , Grand island , USA ) from 100 ng of total RNA isolated from splenocytes of differently vaccinated hamsters using the RNeasy mini kit ( Qiagen ) , before and after 2 and 3 months of infectious challenge . Measurement of levels of IL-2 , TNF-α , IFN-γ , IL-4 , IL-10 and TGF-β was carried out from splenocytes of vaccinated and control animals by a two-step SYBR green I real-time reverse transcription PCR using specific primers ( Table S3 , supporting information ) as reported earlier [33]–[35] . Conventional PCR using 100 ng of hamster DNA , 300 nM cytokine specific primers and High Fidelity PCR Enzyme Mix ( Fermentas ) was previously carried out in order to optimize the real time PCR for each target . Hamster HGPRT ( Hypoxanthine-guanine phosphoribosyltransferase ) was used as an endogenous control to avoid variations between the samples . The cDNA samples were subjected to an initial incubation for 10 minutes at 95°C and then 40 cycles of 95°C for 15 sec and 58°C for 30 s , and 72°C for 30 s in a 7900 HT Fast Real-Time PCR System ( Applied Biosystems ) . Each measurement was carried out in triplicate . The relative expression of mRNA in terms of fold change was calculated by the comparative Ct ( 2−ΔΔCt ) method [36] normalized to HGPRT expression . A no-template control without genetic material was included to eliminate nonspecific reactions . A part of liver from the sacrificed hamsters ( 2 months post challenge ) was used for tissue histology . After standard procedures like paraffin embedding , histological sectioning and mounting on glass slides , sections were stained with hematoxylin and eosin . Sections from each liver ( n = 3 hamsters per group ) were examined by counting 25 consecutive 40× microscopic fields per section . After 2 and 3 months of challenge infection , the animals were sacrificed to determine the parasite load in liver and spleen . The course of organ parasite load was monitored by the microscopic examination of Giemsa-stained impression smears of liver and spleen , expressed as Leishman Donovan Units ( LDU ) [37] as well as by limiting dilution assay ( LDA ) [38] as described previously . For LDA , a weighted piece of liver and spleen isolated from different vaccinated groups were homogenized and five-fold serial dilutions of homogenized tissue suspension were cultured at 22°C for 21 days in 96-well tissue culture plates ( Nunc ) . The culture plates were examined every 7 days for the presence of motile promastigotes for 21days . The reciprocal of the highest dilution that was positive for viable parasites was considered to be the concentration of parasites per mg of tissue . The total organ parasite burden was calculated using the weight of the respective organs . One-way ANOVA statistical test was performed to assess the differences among various groups . Multiple comparisons Tukey-Kramer test was used to compare the means of different treatment groups using the GraphPad Prism 5 . 0 software for windows ( http://www . graphpad . com ) . The Kaplan-Meier method was used to estimate survival rates , and the Log-rank test was applied to compare the survivalities between different groups of hamsters . A value of p<0 . 05 was considered to be significant for all analyses . Cysteine protease A ( KF018070 ) Cysteine protease B ( KC609324 ) Cysteine protease C ( JX968801 . 1 )
Full length cpa , cpb and cpc were successfully cloned in the right orientation in the bacterial expression vector pET28a and the overexpressed proteins from E . coli BL21 ( DE3 ) cells were purified under denaturing conditions ( Fig . 1 & S2 ) . The molecular weights of rCPA , rCPB and rCPC were approximately 38 . 8 , 38 . 5 and 37 . 4 kilodalton ( kDa ) respectively . The yield of purified proteins was approximately 3–5 mg per liter of culture . ClustalW analysis shows 98–99% sequence homology of these three CP with the different strains of L . donovani and L . infantum . Also , L . donovani cpa , cpb and cpc were highly similar also to those of L . major cpa: AJ130942 . 1 ( 64% identity ) , cpb: U43706 . 1 ( 88% identity ) and cpc: XM_003722109 . 1 ( 95% ) respectively . Phylograms depicting the phylogenetic relationships of cpa , cpb and cpc among different Leishmania species are shown in Fig . S1 ( supporting information ) . After successful entrapment in cationic liposomes , the level of incorporation ranged between 70–80% . Protein integrity after liposomal encapsulation was evaluated by 10% SDS-PAGE followed by Coomassie staining . Positive reactions of all three recombinant proteins CPA , CPB and CPC with anti-His monoclonal antibody in Western blot analysis identified these proteins from bacterial lysates ( Fig . 1C ) . To determine the presence of antibodies against L . donovani CPs in human VL , rCPA , rCPB and rCPC were subsequently blot checked against a panel of plasma samples from kala-azar patients . Blood plasma isolated from patients infected with L . donovani recognized all the three recombinant proteins ( rCPA , rCPB and rCPC ) separately at 1∶1000 dilution ( Fig . 1 , E–G ) . This finding verified the reactivity of the selected proteins in both active and cured VL patients , but not in healthy controls . Recognition of L . donovani CPs also by Brazilian VL ( L . chagasi ) patient plasma ( data not shown ) is predictive of cross-protective potential of these vaccine candidates and also for diagnosis of kala-azar . AFM in the acoustic alternative current mode allows the observation of the liposomal surface morphology and structure , overcoming sample manipulation ( Fig . 2 ) . Flattening of vesicles on the mica support few minutes after deposition indicates a moderate stability of the liposomes . AFM images clearly depict the spherical , well-defined shape of the liposomes with visible multilamellar structures and heterogeneous size distribution , which is also maintained after protein loading ( rCPC taken as reference ) ( Fig . S4 ) . Size of the vesicles ranged between 137–172 ( ±14 . 3 ) nm . Among all APCs , MΦs play a central role in processing of liposomal antigens in lysosomes for successful Ag presentation to T cells [39]–[44] . To expand on that , we investigated intracellular trafficking patterns of our cationic liposomes as well as their mechanism of internalization in hamster peritoneal MΦ in vitro ( Fig . 2D & S3 ) . LysoTracker Red was used as a fluorescent acidotrophic probe for tracking acidic organelles in MΦ . Confocal laser scanning microscopy ( CLSM ) of MΦ cells incubated for 2 hrs with both Rh123-labeled liposomes ( green ) , LysoTracker Red ( red ) and DAPI ( blue ) simultaneously showed successful co-localization ( yellow ) of labeled liposomes in acidic compartments such as lysosomes ( Fig . S3 , B ) which confirms antigen presentation by MΦ . For a quantitative insight into the various potential mechanisms of endocytosis , a series of liposome uptake assays were performed in the presence of different inhibitors ( amiloride , chlorpromazine , cytochalasin D and colchicines ) to block each individual pathway . The use of amiloride ( macropinocytosis inhibitor ) and chlorpromazine ( clathrin-mediated endocytosis inhibitor ) strongly inhibited the uptake of cationic liposomes by ∼31% ( ±2 . 674 ) and ∼29% ( ±1 . 07 ) respectively ( Fig . 2D ) . We deduce partial overlap or synergistic involvement of these pathways for liposomal uptake in hamster peritoneal MΦ . By contrast , cytochalasin D and colchicines had only minimum effect on the cellular entry of liposomes indicating minor contributions of actins and microtubules in the uptake process . Nevertheless , other unidentified pathway ( s ) of endocytosis may also be involved in liposome trafficking through MΦ . The FACS estimation of the inhibitory effects on internalization and post-endocytic trafficking of labeled liposomes further confirmed the CLSM data ( Fig . S3 , A ) . To evaluate innate capacity of our adjuvant formulation to stimulate MΦs , naïve peritoneal MΦs were isolated and stimulated with bacterial lipopolysaccharides ( LPS ) ( 1 mg/ml ) , cationic liposomes ( 50 mM ) , MPL-TDM ( 100 ng/ml ) and combination of cationic liposomes ( 50 mM ) and MPL-TDM ( 100 ng/ml ) ( Fig . 2 , E & F ) . MPL-TDM alone activates MΦs from naïve hamsters resulting in increased NO and ROS production , which are regarded as the potent microbicidal molecules responsible for parasite clearance from infected MΦs [45] . Evidence for such endotoxin and lipid A mediated NO production via inducible iNOS is increasing [46] , [47] which results in innate host cell activation and subsequent adjuvant properties . The significant production of NO ( Fig . 2E ) and ROS ( Fig . 2F ) in response to cationic liposomes along with MPL-TDM are in agreement with our previous study which showed that stimulation of murine DCs with liposomes plus MPL-TDM could enhance IL-12 ( p40 ) and NO generation in vitro [11] . These data indicated that combining MPL-TDM with cationic liposomes result in increased the adjuvant activity of the formulation on the APCs in vitro , serving our rationale for its utilization . DTH was measured as an index of in vivo elicitation of cell-mediated immunity . All the CP immunized hamsters displayed steady increase in DTH response , both before and 2 and 3 months after infection . Among the three antigens , liposomal rCPC vaccination induced maximum DTH followed by rCPB and rCPA at 3 months post challenge . Highest DTH was obtained for cocktail vaccinates ( Fig . 3A; p<0 . 0001compared to free adjuvants ) , which was even significantly higher than rCPC ( p<0 . 0001 ) vaccinated hamsters post vaccination as well as at 2 and 3 months post infection . Serum samples were analyzed for total IgG , IgG1 and IgG2 both before and after infection in vaccinated animals . Although distinct IFN-γ and IL-4 mediated IgG isotype switching is not reported in hamsters , IgG1 and IgG2 are still considered as surrogates of Th2 and Th1 response similar to murine IgG1 and IgG2a/b respectively [48] . Shown in Fig . 3 are the antibody levels in immunized ( Fig . 3B ) and 3 months post challenged ( Fig . 3C ) hamsters ( sera dilution 1∶1000 ) . In comparison to controls , significant enhancement of antigen-specific total IgG , IgG1 and IgG2 with a dominance of IgG2 was observed in all antigen immunized hamsters . Contrastingly , after infection , the levels of IgG2 were quite low in hamsters receiving PBS or free adjuvants as controls indicating a Th2 response favouring disease progression . All CP immunization induced higher specific IgG2 titers than IgG1 , with significant difference observed between the IgG2 subclasses in single antigens and cocktail vaccination schedule ( Fig . 3B ) . Remarkable IgG2 dominance was observed in hamsters receiving rCPC and triple antigen cocktail which also differed significantly between rCPC and cocktail immunized groups ( p<0 . 01 ) ( Fig . 3B & C ) . The results were consistent with lymphoproliferation and Th1 biased cytokine response , which showed strong CMI response in animals receiving rCPC and cocktail antigens . Impaired T-cell mediated immunity as assessed by in vitro lymphocyte proliferation has been the hallmark of progressive VL [49] , [50] . Proliferative capacity is a wanted feature of vaccine , reflecting stimulation of T cell responses . Therefore , to gain insight into cellular immunity developed in hamsters after vaccination , antigen specific splenocyte proliferation was evaluated using CFSE . As shown in Fig . 4 , ConA ( mitogen ) , taken as a positive control , highly enhanced cell proliferation . Splenocytes from all the CP vaccinated hamsters proliferated in response to corresponding antigen . Among the three CPs , rCPC showed higher antigen-specific proliferation compared to rCPA and rCPB . Significant enhancement of antigen-specific splenocyte proliferation was observed for liposomal rCPC ( p<0 . 001 ) and liposomal cocktail ( p<0 . 0001 ) vaccinated animals compared to adjuvant controls . Highest percent lymphoproliferation was observed in hamsters vaccinated with triple antigen cocktail which was significantly higher than rCPA ( p<0 . 0001 ) , rCPB ( p<0 . 0001 ) and rCPC ( p<0 . 0001 ) as single antigen . Taken together , these finding suggest that all three CPs present in the cocktail vaccine act synergistically to counteract the impaired T cell response after challenge for improved protection . MΦ have long been known to play an important role in leishmanicidal activity through IFN-γ and IL-12 mediated production of NO [51] . However , like human , NO generation in infected hamsters is severely impaired due to lack of IFN-γ mediated upregulation of nitric oxide synthase ( NOS ) 2 mRNA [52] . To evaluate the vaccine-induced activation of MΦ to arrest parasite multiplication , the number of intracellular amastigotes and NO production were determined in resident peritoneal MΦ from different immunized groups at designated time points ( 4 hr , 16 hr , 24 hr , 48 hr and 72 hr ) after in vitro infection with L . donovani ( Fig . 5 ) . No significant difference was observed between groups in the numbers of initial uptake and percent infected MΦ till 24 h of incubation ( Fig . 5A ) . However , after 48 and 72 h , significant leishmanicidal activity , lowering of percent infected MΦ ( Fig . 5A ) and mean number of amastigotes/MΦ ( Fig . 5B ) was observed in all CPs immunized animals compared to controls . Of interest , maximum inhibition of parasite multiplication in vitro was observed in hamsters receiving rCPC and cocktail antigens ( p<0 . 0001 ) which was even significantly higher than liposomal rCPA ( p<0 . 0001 ) and rCPB ( p<0 . 0001 ) vaccinates , sustained upto 72 h of incubation ( Fig . 5B ) . However , no significant difference in amastigote killing activity was noted between rCPC and cocktail vaccinates after 72 h . This may be due to the reason that control of in vitro infection by MΦ is not solely due NO production but also by the milieu of cytokines and chemokines , specially IL-12 and TNF-α secreted by the parasitized APCs [53] . Almost comparable levels of these may also account for the lack of significant difference in parasite clearance between rCPC and cocktail vaccinates in vitro . NO level in single antigen vaccinates were almost comparable without significant difference between the groups upto 48 h , but was significantly upregulated in rCPB and rCPC vaccinated animals at 72 h post infection compared to controls ( Fig . 5C ) . Interestingly , MΦ from liposomal cocktail antigen immunized animals released the highest amount ( 7 µM ) of NO which was ∼4 . 3 fold more than that of infected controls and ∼2-fold more than the single antigen ( rCPA , rCPB , rCPC ) immunized groups after 72 h post culture . Collectively , immunization with liposomal cocktail CPs mounted highest antileishmanial activities towards L . donovani infected MΦ , followed by liposomal rCPC>rCPB>rCPA , immunized animals . The degree of cellular response in terms of granuloma formation in liver of immunized and infected control hamsters indicates the histological features of either parasite clearance or multiplication in this organ [54] . Considerable variation among animals between different groups was observed in hepatic histology 2 months after L . donovani challenge ( Fig . S6 ) . In the infected controls the cellular response in liver was restricted to few immature granulomas , comprising of fused infected Kupffer cells with a large number of infiltrating lymphocytes and histiocytes . The histological features in controls were indicative of suboptimal cellular response inappropriate for parasite clearance . Occasional portal tract inflammation , fibrosis and hepatocyte degeneration resulting in loss of normal tissue architecture were also visible in liver of infected controls . Mature granulomas with compact collection of cellular infiltrates , indicative of optimal cellular response leading to parasite clearance , were mostly seen in groups immunized with cocktail and liposomal rCPC vaccinated animals ( Fig . S6 , A ) . Figure S6 , B shows a magnified view of a representative granuloma assembly in a cocktail immunized hamster , showing cellular infiltration of lymphocytes and monocytes . Weight loss and hepatosplenomegaly in infected controls , as a normal course of progressive VL was evident from 2 months of infection ( Fig . S5 ) . Fig . 6A–D shows the outcome of vaccination on parasite burden in liver and spleen at 2 and 3 months post infection . In all the CP vaccinated groups , parasite load in spleen sharply decreased after 2 and 3 months of infection compared to controls . At 3 months post infection , significantly high yet comparable level of protection was induced by rCPA and rCPB immunized groups , suggesting a specific partial protection induced by these antigens compared to controls ( p<0 . 0001 ) . rCPC induced greater protection than both rCPA and rCPB , when injected alone . Of interest , the mean hepatic and splenic parasite burden ( LDU ) were lowered by ∼93% and ∼98% respectively , in animals immunized with cocktail antigens as compared to the adjuvant controls at 3 months post infection . Significantly higher protection was obtained using the cocktail vaccine compared to the single antigens: rCPA ( 73% ) , rCPB ( 76% ) but not rCPC ( 91% ) in spleen . Our result obtained in LDU was reconfirmed through limiting dilution assay , LDA ( Figure 6 , C & D ) , the more sensitive method for monitoring viable parasites . Free adjuvant ( CL+MPL ) itself generated some protection resulting in 2 . 46 log10± . 61 in the liver and 1 log10±1 . 66 in the spleen at 3 months post challenge . This was , however , not significant compared to PBS controls . After 3 months post infection , hamster immunized with liposomal rCPA and/rCPB showed reduced parasite burden ∼6 . 5–9 log-folds in liver and ∼8 . 8–10 log-folds in spleen , respectively , compared to animals immunized with adjuvant alone ( Figure 6 , C & D ) . Immunization with rCPB thus resulted in better protection than rCPA in clearing the parasites from visceral organs . Interestingly , hamsters vaccinated with liposomal rCPC showed a parasitic burden of 5 . 963 log10±0 . 269 in the liver and 6 . 77 log10±0 . 463 in the spleen , with an impressive 4 ( log10 ) -fold decrease in the parasite burden compared to hamsters receiving liposomal rCPA ( 9 . 55 log10±1 . 037 in the liver and 11 . 5 log10±0 . 467 in the spleen , p<0 . 0001 ) and 2 ( log10 ) -fold reduction compared to liposomal rCPB ( 7 . 413 log10±0 . 016 in the liver and 10 . 02 log10±0 . 451 in the spleen , p<0 . 0001 ) . Highest level of protection against L . donovani was however achieved in hamsters immunized with the liposomal triple antigen cocktail which showed 13 log-fold and 16 log-fold reductions in the parasite burden in liver and spleen , respectively , compared to adjuvant controls ( p<0 . 0001 ) after 3 months of infectious challenge . Moreover , they exhibited minimal weight loss and the surviving animals appeared apparently healthy till the termination of the experiment . Consequently , almost 80% survival was observed in hamsters injected with the cocktail vaccine for at least 180 days post infection ( Fig . 6E ) . In contrast , almost 50–60% of the controls died after 3 months due to steady increase in parasite load ( Fig . 6E ) . In experimental VL , the efficacy of antileishmanial prophylaxis chiefly depends on CMI and balanced production of endogenous Th1 and Th2 cytokines [55] . The cytokine expression profiles of vaccinated and control infected hamsters were thus dissected to find out their role in immunoprotection . All immunized groups receiving either of liposomal rCPA , rCPB and rCPC singly or as cocktail produced significant upregulation of IL-2 and IFN-γ mRNAs in comparison to controls , which progressively increased at 2 and 3 months post infection ( Fig . 7A ) . rCPA and rCPB immunizations although showed heightened IFN-γ and IL-12 expression , also enhanced expression of disease promoting cytokines like IL-4 and IL-10 mRNAs after infection , which perhaps limited their vaccine efficacy . The splenocytes of vaccinated hamsters receiving liposomal rCPC and cocktail of three liposomal CPs showed higher IFN-γ , IL-2 , TNF-α and IL-12 mRNA expression than all other groups but appreciable downregulation of macrophage deactivating cytokines like IL-4 , IL-10 mRNAs , before and after infection ( Fig . 7 ) . Even after 3 months post infection , an appreciable ∼4 . 5 fold and ∼3 . 5 fold upregulation of IFN-γ ( p<0 . 001 ) and IL-12 ( p<0 . 001 ) mRNAs respectively were observed in cocktail immunized animals compared to PBS controls . There were 9 to10-fold reductions in IL-10 , for rCPC and cocktail immunized groups ( p<0 . 0001 ) after 3 months post infection ( Fig . 7 D ) compared to adjuvant controls which was directly related to disease progression . Further analysis of the induced cytokine expression by the means of IFN-γ/IL-10 mRNA ratio ( not shown ) revealed that liposome formulated cocktail CP vaccines clearly induced a strongest Th1 response in comparison to all other groups , required for sustained protection . Further , augmented level of disease-resolving cytokine , TNF-α mRNA ( Fig . 7 , C & F ) , notably high in rCPB , rCPC and cocktail vaccinated groups corroborate intracellular parasite killing . In particular , a dramatic shift towards Th1 phenotype and most probably an effective CMI response was maintained by high IL-12 and IL-2 mRNA levels even at 3 months post infection in cocktail vaccinates . Although we could not dissect the T cell phenotype responsible for protective immunity , results from antigen-specific T-cell proliferation , NO production , increased mRNA expression of IL-2 , IL-12 , IFN-γ , robust DTH responses , and IgG2 antibody titer are indicative of durable T-cell response which is sustained at least up to 3 months post infection . Further , our results emphasize a major role of IL-10 and IL-4 upregulation for disease progression and death in infected controls , probably mediating their effect through IFN-γ blocking , macrophage deactivation and lymphocyte apoptosis as reported earlier [56] , [57] .
Till date , ‘leishmanization’ remains the gold standard for vaccination against leishmaniasis for providing long term protection . However , safety concerns and anti-vector immunity have complicated the development of live vaccines against Leishmania [58] . In contrast , a non-living subunit vaccine containing selected antigens combines safety and rational designing to target the intracellular pathogens , avoiding anti-vector immunity . However , subunit protein vaccines without adjuvant often suffer from limited or non-existent T cell response due to poor antigen presentation via APCs [59] , [60] . Interestingly , current prophylactic vaccine strategies against intracellular pathogens like Leishmania focus on strengthening host innate immunity that target the pathogen , in addition to vaccine induced adaptive response [61] . Vaccination against Leishmania requires both innate and adaptive arms of host defence mediated through MΦ , dendritic cells and both CD4+ and CD8+ T cells for protection [62] , [63] . The liposomal cargos loaded with protein antigens in combination with defined immunostimulatory molecules mimicking pathogens in reductionist mode are attractive formulations to elicit protective T cell immunity . Use of TLR agonists with cationic liposomes allows concurrent antigen presentation along with targeting pattern recognition receptor ( PRR ) pathways , for effective expansion of effector T cells [64] , [65] . In this study we therefore selected highly immunostimulating adjuvant formulation ( cationic liposomes and MPL-TDM ) to investigate the ability of leishmanial CPs to generate both humoral and cell mediated immune response via subcutaneous route . Lysosomal cysteine proteases ( CP ) are reported to be involved in distinct cellular functions and make safe vaccine antigens against Leishmania [15]–[17] . Nevertheless , evaluation of protective efficacies of these proteins against L . donovani is lacking . The present study compares the efficacies of three recombinant cysteine proteases ( rCPA , rCPB , rCPC ) and their cocktail using cationic liposomes with MPL-TDM adjuvant platform for the first time against L . donovani . Hamsters are well-established animal models for L . donovani infections , with high innate susceptibility and clinicopathological resemblance to human VL [66] , thus suitable for evaluation of vaccines for human use . Macrophages , the major intracellular niche for Leishmania , are also involved in antigen presentation and vaccine-induced leishmanicidal activity , usually predictive of disease outcome in vivo . Phagocytosis by macrophages plays an important role in lymph node retention of liposomes after subcutaneous immunization [67] , [68] . Cellular internalization and successful endosomal/lysosomal loading for MHC class II antigen presentation via APCs has been studied for the first time for DSPC bearing cationic liposomes showing involvement of distinct endocytic pathways ( macropinocytosis and clathrin-mediated ) ( Fig . 2 ) for cellular entry . This is advantageous in lowering the amount of vaccine Ag by liposomal encapsulation enabling administration of higher effective doses if required [26] . Impaired cellular immunity along with a shift towards Th2- type immunity has been the hallmark of VL [50] . Although the role of humoral response in resolution of VL is controversial , elevated level of IgG after infection usually correlates with VL progression in hamsters . The rapid increase in anti-leishmanial IgG observed after antigen immunization appeared to result from associated MPL-TDM which is known to augment humoral in addition to CMI response [69] . However , the sustained dominance of IgG2 over IgG1 in CP vaccinated animals indirectly reflects the Th1-biased protective immune responses at 2 and 3 months post challenge . This is in accordance with enhanced proliferation of hamster splenocytes isolated from cocktail vaccinated group over nonvaccinated and single antigen immunized animals . In VL , a balance must be established between Th1 and Th2 cytokines for parasite clearance . High IL-10 and IL-4 can lead to disease exacerbation and vaccine failure even in presence of high IFN-γ [70] . TNF-α , another well-defined inflammatory cytokine with antileishmanial properties , is known to act either alone or with IFN- γ to induce the production of NO and ROS and might have a strong additive effect in clearing parasites from vaccinated animals . All three CPs immunizations resulted in increased level of IL-12 driven IFN-γ , but low amounts of IL-4 , IL-10 and TGF-β . Thus , enhanced IL-2 , IL-12 , TNF-α and IFN-γ mRNA expression but downregulation of IL-10 , most remarkable in cocktail vaccinated hamsters is chiefly responsible for strong Th1 biased immunity after infection [71] . Interestingly , IL-10 is also generated at low levels after infection as a part of effector response to prevent autoimmunity and maintenance of T cell proliferation [72] . Significantly low but consistent mRNA expression for both IL-4 and IL-10 in all CP vaccinates , more noticeably in rCPC and cocktail immunized groups , probably circumvents uncontrolled Th1 response and tissue damage due to high post-challenge IFN-γ and IL-12 expression levels in these groups . Of interest , hepatic histology , in vitro lymphoproliferation and NO mediated clearance of parasites from MΦ nicely correlates with in vivo protection after virulent challenge . Maximum number of matured hepatic granuloma was observed in rCPC and cocktail immunized hamsters after 2 months which led to almost complete clearance of parasites from visceral organs . The combination of CPA , CPB and CPC resulted in enhanced protection in liver ( >93% ) and spleen ( >98% ) which was even higher than all other CPs based vaccines tested against Leishmania so far [15]–[17] . Previous vaccine attempts with different liposomal adjuvants have reported remarkable efficacy of about 80–90% protection against Leishmania [73] and other pathogens [74] , [75] . Liposome entrapped native LD51 ( β-tubulin ) and LD31 ( ATP synthase α-chain ) from L . donovani induced 75–77% protection in mice , through intraperitoneal route [73] . Similarly , 7–10-log-fold reduction in parasite burden upon was obtained upon vaccination with liposomal recombinant gp63 with MPL-TDM in mice [11] . One plausible explanation for the synergistic enhancement of protection with cocktail CP vaccination is the cumulative increase in T-cell epitopes in a non-antagonistic manner . In this context , it is a known that the parasite CPA , CPB and CPC all belong to the same group of papain-like CPs , and probably behave like a multi-subunit vaccine when given in combination . Enhanced Th1 dominance in the current study chiefly arises from the associated MPL-TDM within the liposomal formulation , increasing the efficacy and adaptability of the delivery system as reported earlier in mice [11] . MPL is known for its direct interaction with TLR4 on dendritic cells ( DC ) influencing IL-12/IFN-γ axis , to skew the T cell response towards a Th1-phenotype [76] , [77] . Taken together , vaccination with liposomal CPs combined with MPL-TDM confers a Th1 biased mixed Th1/Th2 response to reduce the parasite burden in hamsters . Although , direct comparison of our work with previous reports is difficult , it is noteworthy that rCPC individually induced better protection than promising vaccine candidates like gp63 , ORFF , LelF-2 tested in murine VL [78] against L . donovani . Hence , we propose that combining rCPC or its antigenic motif with other immunodominant antigens as cocktail or fusion hybrid can induce durable and complete protection against VL . Here we report almost complete elimination of parasites from both liver ( 16-log fold ) and spleen ( 13-log fold ) after vaccination with CP triple antigen cocktail and L . donovani virulent challenge . Though sterile protection was not achieved , the efficacy of this antigenic cocktail is quite high compared to other subunit protein vaccinations tried against experimental VL [79] , [80] and needs further optimization . Finally , one clinical consequence of this work is that the protective antigen ( s ) might not be the only determinant of protection but also require highly immunopotentiating adjuvant to realize its full potential . The development of a long-term protective immunity in terms of CD8+ T cells response is extremely important for successful vaccination against intracellular pathogens like Leishmania . In the recent past , many protein subunit vaccines that reported various levels of protection against Leishmania , often failed to generate sufficient long term memory against the disease [81] . Although , rHASPB1 , rORFF , Leish-111f , and Leish-110f have reported sustained long term immunity against VL , they suffered either from using human incompatible adjuvant [82] or from low level of protection [83] . Importantly , Leish-111f and Leish-110f formulated with MPL-SE were human compatible , but did not show data for protection in both liver and spleen were not reported [82] , [83] . Moreover , all these studies carried out in mice , challenged the vaccinated animals 3–4 weeks after the last boost . Our previous reports with crude leishmanial membrane antigen ( LAg ) [84] and native gp63 [85] entrapped in positively charged liposomes showed significant long term protection when challenged with virulent parasites 10–12 weeks after final immunization . However , one of the major drawbacks of these studies was the use of intraperitoneal route of immunization , not suitable for human use . This problem was overcome with the use of MPL-TDM adjuvant which could prime both CD8+ and CD4+ T cells when used in subcutaneous route with liposomal antigens: soluble leishmanial antigen ( SLA ) [86] and recombinant gp63 [11] , leading to both short-term and long-term protection . The above results obtained so far and our preliminary survival analysis data in the present study do indicate an appreciable long-term protective response generated by the cocktail CPs formulated with MPL-TDM which was sustained at least up to 180 days post infection in hamsters . Unfortunately , mechanistic details of CD4+ , CD8+ and regulatory T cells involved in vaccine mediated protection cannot be fully elucidated due to unavailability of hamster-specific reagents . This can be overcome with mice model in future which is the focus of our continuing research efforts . | Conventional chemotherapy of visceral leishmaniasis ( VL ) typically relies on pentavalent antimonials that suffer from extensive drug resistance in India . Development of preventive vaccination is undoubtedly a better alternative to completely eradicate the disease . With this in mind , we chose to target parasite cysteine proteases ( CPs ) , with immense biological importance , as potential vaccine candidates against Leishmania donovani . Here , we describe the superior efficacy of an antigenic cocktail of type I , II and III CPs entrapped in cationic liposomes with Toll like receptor ( TLR ) agonists: monophosphoryl lipid A- Trehalose dicorynomycolate ( MPL-TDM ) , against L . donovani in a hamster model . The three CPs acted synergistically in the cocktail to induce almost complete protection against Leishmania . The protection is chiefly mediated through upregulation of protective cytokines like interferon-gamma ( IFN-γ ) , interleukin-12 ( IL-12 ) , IL-2 , and tumour necrosis factor ( TNF-α ) , with concomitant down-regulation of disease promoting cytokines , like transforming growth factor–beta ( TGF-β ) , IL-10 and IL-4 . The antigens were also compared singly for their protective potential . Interestingly , type III ( CPC ) CP emerged as the most potent antigenic component of the cocktail inducing better protection than type I and II . Hence , the cysteine proteases of Leishmania form an attractive group of vaccine candidates for future studies in human VL . | [
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] | 2014 | Combining Cationic Liposomal Delivery with MPL-TDM for Cysteine Protease Cocktail Vaccination against Leishmania donovani : Evidence for Antigen Synergy and Protection |
The polyamines putrescine , spermidine , and spermine are organic cations that are required for cell growth and differentiation . Ornithine decarboxylase ( ODC ) , the first and rate-limiting enzyme in the polyamine biosynthetic pathway , is a highly regulated enzyme . To use this enzyme as a potential drug target , the gene encoding putative ornithine decarboxylase ( ODC ) -like sequence was cloned from Entamoeba histolytica , a protozoan parasite causing amoebiasis . DNA sequence analysis revealed an open reading frame ( ORF ) of ∼1 , 242 bp encoding a putative protein of 413 amino acids with a calculated molecular mass of 46 kDa and a predicted isoelectric point of 5 . 61 . The E . histolytica putative ODC-like sequence has 33% sequence identity with human ODC and 36% identity with the Datura stramonium ODC . The ORF is a single-copy gene located on a 1 . 9-Mb chromosome . The recombinant putative ODC protein ( 48 kDa ) from E . histolytica was heterologously expressed in Escherichia coli . Antiserum against recombinant putative ODC protein detected a band of anticipated size ∼46 kDa in E . histolytica whole-cell lysate . Difluoromethylornithine ( DFMO ) , an enzyme-activated irreversible inhibitor of ODC , had no effect on the recombinant putative ODC from E . histolytica . Comparative modeling of the three-dimensional structure of E . histolytica putative ODC shows that the putative binding site for DFMO is disrupted by the substitution of three amino acids—aspartate-332 , aspartate-361 , and tyrosine-323—by histidine-296 , phenylalanine-305 , and asparagine-334 , through which this inhibitor interacts with the protein . Amino acid changes in the pocket of the E . histolytica enzyme resulted in low substrate specificity for ornithine . It is possible that the enzyme has evolved a novel substrate specificity . To our knowledge this is the first report on the molecular characterization of putative ODC-like sequence from E . histolytica . Computer modeling revealed that three of the critical residues required for binding of DFMO to the ODC enzyme are substituted in E . histolytica , resulting in the likely loss of interactions between the enzyme and DFMO .
Entamoeba histolytica is a unicellular protozoan parasite that infects about 50 million people each year and may cause potentially life-threatening diseases such as hemorrhagic colitis and/or extraintestinal abscesses [1] . The infections are primarily treated by antiamoebic therapy . Drugs of choice for invasive amoebiasis are tissue-active agents such as metronidazole , tinidazole , and chloroquine [2] . Although drug resistance to E . histolytica does not appear to be a serious problem , there are occasional reports of failure with metronidazole suggesting the possibility of development of clinical drug resistance [3] . Polyamine biosynthetic pathway is the critical regulator of cell growth , differentiation , and cell death [4]–[6] . Polyamines are involved in nucleic acid packaging , DNA replication , apoptosis , transcription , and translation [7] . The polyamine biosynthetic pathway is a potential target for therapeutic agents against various hyperproliferative disorders , particularly cancer [8]–[10] . Given the importance of the polyamine biosynthetic pathway as a validated therapeutic target in protozoan parasites [11]–[14] , we decided to further investigate this pathway in E . histolytica in the hope of extending our attempts at drug discovery to include this medically important parasite . Ornithine decarboxylase ( ODC; EC 4 . 1 . 1 . 17 ) is the first rate-limiting enzyme in polyamine biosynthesis , catalyzing the decarboxylation of L-ornithine to putrescine . This enzyme is found in a variety of systems ranging from bacteria [15] and protozoa [16] to plants [17] and mammals [18] . The rapid activation of the enzyme by various stimuli such as hormones , growth factors , or stress makes this enzyme a vital mediator in the regulation of polyamine pathway . ODC , like most amino acid decarboxylases , requires pyridoxal-5′-phosphate ( PLP ) as a cofactor [19] . E . histolytica ODC protein has been biochemically purified from trophozoites of the parasite [20] . Analytical electrophoresis revealed the presence of a major polypeptide of 45 kDa and scarcely noticeable amounts of two other proteins of 70 and 120 kDa . The major polypeptide exhibited amino-terminal sequence homology in the range of 40%–73% with ODCs of other organisms [20] . Biosynthesis of polyamines in parasites has been exploited as a target to control disease caused by several parasites with specific inhibitors of ODC such as α-difluoromethylornithine ( DFMO ) , which is a structural analog of ornithine . DFMO has been found to be an efficient therapeutic agent against ODC up-regulation [13] , [14] , [21]–[23] . It is a specific and irreversible inhibitor of ODC , and previous studies have shown that DFMO inhibits growth of Giardia lamblia [24] , Acanthamoeba castellani [25] , Plasmodium falciparum , and some Trypanosoma species [13] , [14] , [21] , [26] but has no inhibitory effect on E . histolytica ODC [20] . In this paper we report , to our knowledge for the first time , molecular cloning , expression , and characterization of a putative ODC-like sequence from E . histolytica , the parasitic protozoan responsible for amoebiasis . ODC is a PLP-dependent enzyme , and in the present work the ability of E . histolytica putative ODC to form complexes with PLP and DFMO was investigated using modeling of the three-dimensional structure .
Restriction enzymes Pfu and Taq DNA polymerases were obtained from MBI Fermantas . All other chemicals were of analytical grade and were available commercially . All experiments were carried out with E . histolytica strain HM-1:IMSS clone 6 , which was obtained from William A . Petri ( University of Virginia ) . The cells were maintained and grown in TYI-33 medium supplemented with 15% adult bovine serum , 2% Diamond's vitamin mix , and antibiotic ( 0 . 3 units/ml penicillin and 0 . 25 mg/ml streptomycin ) . Cell viability was determined by microscopy using a trypan blue dye exclusion test . Experiments were conducted with cells that showed >90% viability . A ∼1 , 242 base pair fragment was amplified from the genomic DNA of E . histolytica using a sense primer with flanking BamH I site ( underlined ) , 5′-CGCGGATCC ATGAAACAAACATCTCTAGAAG-3′ , which codes for amino acid sequence MKQTSLE at position 1–21 and one extra base , G , and the antisense primer with a flanking Xho I site ( underlined ) , 5′- CCGCTCGAGAGCATAGTGTGGAATACCAT-3′ , which codes for amino acids GIPHYA at position 1 , 220–1 , 239 with two extra bases , A and T . Polymerase chain reaction was performed in a 50 µl reaction volume containing 150 ng of genomic DNA , 25 pmol each of gene-specific forward and reverse primers , 200 µM of each dNTPs , 2 . 5 mM MgCl2 , and 2 . 5 units of Taq DNA Polymerase ( MBI Fermentas ) . PCR cycling conditions were as follows; 94°C for 10 min , followed by 35 cycles of 94°C for 1 min , 47°C for 45 sec , 72°C for 1:30 min . A final extension was carried out for 10 min at 72°C . A single 1 , 242 bp PCR product was obtained and subcloned into pTZ57R T/A vector ( Promega , Madison , USA ) and subjected to automated sequencing . Sequence analysis was performed by DNAstar , whereas comparisons with other sequences of the database were performed using the search algorithm BLAST [27] . Multiple alignments of amino acid sequences were performed using CLUSTAL W ( http://www . ebi . ac . uk/clustalw/ ) . The phylogenetic tree was constructed using PHYLIP style treefile produced by CLUSTAL W . The ∼1242-bp DNA fragment , amplified by Pfu polymerase ( MBI Fermentas ) , was also cloned into the BamH I-Xho I site of pET 30a vector ( Novagen ) . The recombinant construct was transformed into BL21 ( DE3 ) strain of E . coli . Expression from the construct pET30a-ODC-like sequence was induced at O . D . of 0 . 3 with 1 mM IPTG ( isopropyl β-D-thiogalactoside ) ( Sigma ) at 37°C for different time periods . Bacteria were then harvested by centrifugation and the cell pellet was resuspended in binding buffer ( 50 mM sodium phosphate buffer , pH 7 . 5; 10 mM imidazole , pH 7 . 0; 300 mM sodium chloride; 2 mM phenylmethylsulphonyl fluoride ( PMSF ) ; and 30 µl protease inhibitor cocktail ) . Lysozyme ( 100 µg/ml ) was added to the cell suspension and kept on a rocking platform for 30 min at 4°C . The resulting suspension was sonicated six times for 20 s with 1 min intervals . The lysate was centrifuged at 20 , 000g for 30 min at 4°C . The resulting supernatant , which contained protein , was loaded onto a pre-equilibrated Ni-NTA agarose beads ( Ni2+-nitrilotriacetate ) -agarose beads ( Qiagen ) . The mixture was kept on a rocking platform for 2 h at 4°C . It was centrifuged at 400 g for 30 min at 4°C . The supernatant was discarded and pellet was washed thrice with wash buffer ( 50 mM sodium phosphate buffer , pH 7 . 5; 50 mM imidazole , pH 7 . 0; 300 mM sodium chloride; 2 mM phenylmethylsulphonyl fluoride [PMSF]; and 30 µl protease inhibitor cocktail ) . The protein was eluted with increasing concentrations of imidazole , pH 7 . 0 . The imidazole was removed by dialysis in 20 mM sodium phosphate buffer , pH 7 . 5 . The purified protein was aliquoted and stored at −80°C . Genomic DNA was digested with the enzymes XhoI and HindIII and subjected to electrophoresis in 0 . 8 % agarose gels . The fragments were transferred to nylon membranes ( Amersham Pharmacia Biotech ) and subjected to Southern blot analysis . For Northern blot analysis , 15 µg of total RNA was fractionated by denaturing agarose gel electrophoresis and transferred onto nylon membrane following standard procedures . Pulsed-field gradient gel electrophoresis ( PFGE ) was carried out essentially as described earlier [28] . The agarose blocks containing the cells were subjected to PFGE in 1 . 2% agarose gels using the Gene Navigator system ( Pharmacia ) . The pulse conditions used were 70 s for 15 h , 120 s for 14 h , and 200 s for 7 h at 5 . 5 V cm−1 . Saccharomyces cerevisiae chromosomes were used as size markers . Following the transfer of DNA , RNA , and chromosomes onto nylon membranes , the nucleic acids were UV cross-linked to the membrane in a Stratagene UV cross-linker . Prehybridization was done at 65°C for 4 h in a buffer containing 0 . 5 M sodium phosphate; 7% SDS; 1mM EDTA , pH 8 . 0; and 100 µg/ml sheared denatured salmon sperm DNA . The blots were hybridized with denatured α-[PPPP32P]-dCTP-labeled DNA probe ( PCR probe described for the E . histolytica putative ODC-coding region ) at 10PP6P cpm/ml , which was labeled by random priming ( NEB BlotPPKit , New England Biolabs ) . Membranes were washed , air-dried , and exposed to an imaging plate . The image was developed by PhosphorImager ( Fuji film FLA-5000 , Japan ) using Image Quant software ( Amersham Biosciences ) . E . histolytica ( 1×106 cells ) were harvested by centrifugation at 16 , 000 g at 4°C for 10 min , washed with phosphate-buffered saline , pH 7 . 4 . The cell pellet was resuspended in lysis buffer ( 100 mM Tris-Cl , pH 7 . 5; 150 mM sodium chloride; 2 mM PMSF; 2 mM iodoacetamide; 2 mM EDTA; 2 . 5 mM parahydroxymercuricbenzoic acid; 2 mM ethylene glycol-bis ( amino ether ) ; and 10 µg/ml proteinase cocktail ) and incubated on ice for 10 min . The cells were lysed by freeze-thaw in liquid nitrogen and subjected to sonication for 10 sec with 1 min interval at 4°C , thrice . The lysate was centrifuged at 15 , 000g for 30 min at 4°C and the supernatant was used for ODC assay , polyamine estimation , and Western blot analysis as mentioned below . ODC activity was assayed by following the release of 14CO2 from L- [-14C] ornithine [29] . The standard assay mixture containing the supernatant , 200 µM PLP; 12 . 5 mM DTT; 250 mM Tris , pH 7 . 5; 2 mM ornithine; and 3 µCi of the radiolabeled ornithine were incubated at 37°C for 1 h . The reaction was terminated by injecting 5 N H2SO4 . Activity is expressed in enzyme units in which one unit is nmol of CO2 /mg protein/h . The assay was repeated thrice . Protein concentrations were determined by the method of Bradford [30] using bovine serum albumin as standard . Quantitative determination of polyamines in crude lysates of E . histolytica was performed by C18 reversed-phase high performance liquid chromatography after precolumn derivatization with dansyl chloride [31] . The results were based on three separate determinations . The purified recombinant putative ODC-like protein ( 20 µg ) was subcutaneously injected in mice using Freund's complete adjuvant , followed by two booster doses of recombinant putative ODC-like protein ( 15 µg ) with incomplete adjuvant at 2 wk intervals to produce polyclonal antibody against the recombinant putative ODC-like protein . The mice were bled after 2 wk after the second booster , and sera were collected and used for Western blot analysis . Recombinant putative ODC-like protein and cell lysate ( 100 µg of protein ) from E . histolytica were fractionated by SDS/PAGE blotted on to nitrocellulose membrane using electrophoretic transfer cell ( Bio-Rad ) . Western blot analysis was carried out using the ECL ( enhanced chemiluminescence ) kit ( Amersham Biosciences ) according to the manufacturer's protocol . Anti-polyhistidine ( mouse IgG2a isotype , Sigma ) and polyclonal antibody ( 1∶500 dilution ) against purified recombinant E . histolytica putative ODC generated in mice were used for Western blot analysis . The structure of the Trypanosoma brucei ODC mutant in complex with DFMO [32] was used as a template to model E . histolytica ODC . In this mutant structure , lysine 69 has been mutated by alanine ( K69A ) . STAMP ( structural alignment of multiple proteins ) [33] , was used for structural alignment of three ODCs from T . brucei ( 2TOD ) ( ExPASy [http://expasy . org/] accession number: P07805 ) , H . sapiens ( 1D7K ) ( ExPASy accession number: P11926 ) and M . musculus ( 7ODC ) ( ExPASy accession number: P00860 ) . Later , the program JOY ( version 5 ) [34] was used to align and merge three structurally aligned ODCs with the sequence of E . histolytica putative ODC such that properties of both the structure-based alignment for the homologues of known three-dimensional structures and the sequence-based alignment involving E . histolytica putative ODC are reflected in the final alignment used for modeling ( Figure 1A ) . JOY represents structural information and annotates each amino acid residue according to its structural environment . JOY uses local structural features calculated from the atomic coordinates in a PDB file . The three-dimensional model of E . histolytica putative ODC in complex with PLP and DFMO has been built based on the crystal structure of T . brucei ODC by using the program MODELLER [35] . MODELLER generates a three-dimensional structure of a given protein sequence ( target ) based primarily on its alignment to one or more proteins of known structure ( template/templates ) . The modeling process consists of fold assignment , target-template alignment , structure building , and evaluation . MODELLER implements comparative protein structure modeling by satisfying spatial restraints [36] , [37] and performs tasks such as de novo modeling of loops , comparison of protein structures , optimization of various models of protein structures , etc . Interactive graphics like SYBYL ( Tripos , St . Louis , Missouri , United States ) was used for energy minimization of the modeled structure to relieve the short contacts , if any . Modeled E . histolytica putative ODC was subjected to energy minimization using the AMBER force field [38] encoded in the SYBYL software . Energy minimization was done in order to rectify all stereochemical inconsistencies and short contacts that may be present in the initial model .
In order to clone the gene encoding putative ODC-like gene , PCR was performed using specific oligonucleotides ( as described in the Methods section ) , whose sequence was based on Genome Sequencing Project of E . histolytica ( http://www . tigr . org ) . Examination of the E . histolytica database predicts a single ODC gene ( http://pathema . tigr . org ) . A single open reading frame consisting of ∼1 , 242 bp was obtained , cloned , and sequenced . ( E . histolytica ODC gene , GenBank [http://www . ncbi . nlm . nih . gov/Genbank/] accession number AY929249 ) . The open reading frame coded for a putative polypeptide of 413 amino acids , with a predicted molecular mass of ∼46 kDa . The predicted isoelectric point ( pI ) of E . histolytica putative ODC-like protein ( GenBank accession number AAX35675 ) was determined to be 5 . 61 , comparable to those of proteins from L . donovani ( GenBank accession number P27116 ) ( pI 5 . 29 ) , and T . brucei ( GenBank accession number AAA30219 ) ( pI 5 . 46 ) ( Figure 1 ) . There was only 33% sequence identity with human ODC ( GenBank accession number AAA59967 ) , 32% identity with T . brucei ( GenBank accession number AAA30219 ) , and 36% identity with Datura stramonium ODC ( GenBank accession number CAA61121 ) sequences ( Figure 1B ) . The sequences of the mammalian ODC has some highly conserved amino acids and regions that are reported to be essential for catalytic activity and dimerization [39]–[41] , and these were also found in the putative ODC-like sequence of E . histolytica . The putative ODC-like sequence was 413 amino acids smaller than ODCs from T . brucei , Homo sapiens , and D . stramonium ( Figure 1B ) . The residue that is essential for dimerization of ODC monomers , mediated by glycine-387 in mammals [42] , was found to have equivalents in the sequence of the E . histolytica putative ODC-like protein at position glycine-361 . The sequence motif PFYAVKCN at position 64–71 of mammalian ODC , which contains the lysine-69 residue to which the cofactor pyridoxal-5′-phosphate binds , is present at position 53–60 of the E . histolytica putative ODC-like protein , although with changes of phenylalanine to cysteine and tyrosine to phenylalanine . The region GPSCNGSD at position 331–338 in the E . histolytica putative ODC-like protein is probably equivalent to consensus sequence GPTCDGLD of the ODC sequences of various eukaryotes . This sequence contains cysteine-360 in mammalian ODC , which is the major binding site of α-difluoromethylornithine ( DFMO ) [40] . The corresponding cysteine-334 is conserved in E . histolytica putative ODC-like protein . The overall amino acid homology of E . histolytica putative ODC with the mammalian ODC is low , but highly conserved signature motifs responsible for dimerization and catalytic activity were present . Another signature sequence , as predicted by PROSITE , D ( I/V ) GGGF , is present across varied sequences without exception . Other highly conserved amino acid stretches , i . e . , FDCAS , EPGR , FNGF , and GAYT , are also consistently conserved , though the functional significance of these stretches is not known . A phylogenetic tree was constructed using the E . histolytica putative ODC-like sequence and other representative ODC sequences ( Figure 2 ) . The nearest homologue to the amoebic protein , as revealed by the tree , is the plant D . stramonium . The human ODC sequence seems to be farthest from the amoebic one . Among kinetoplastids , L . donovani appears to be the closest homologue , while Trypanosoma not clustering with L . donovani , is quite distantly related . To determine the E . histolytica putative ODC-like gene copy number , Southern blot studies were performed as described in Materials and Methods , using the 1 , 242-bp PCR product as a probe ( Figure 3 ) . The enzymes used for Southern analysis were Xho I and Hind III , which have no recognition sites in the E . histolytica putative ODC-like gene sequence . A single band was obtained in each case ( Figure 3A ) , demonstrating that it is a single-copy gene . A PFGE blot probed with the 32P labeled 1 , 242 bp ODC PCR fragment , hybridized to a 1 . 9 Mb size chromosome . ( Figure 3B ) . Northern blotting of E . histolytica total RNA and PCR-generated ∼1 , 242-bp gene probe , revealed two transcripts of ∼4 . 8 and ∼3 . 5 kb ( Figure 3C ) . In order to characterize the recombinant protein , the gene sequence encoding the E . histolytica putative ODC-like protein was cloned in-frame in a pET-30a expression vector with its own start ATG codon . The resultant pET-30a E . histolytica putative ODC-like construct was transformed into E . coli , and protein expression was induced as described in Materials and Methods . A protein with molecular weight that matched the estimated ∼48 kDa predicted by the amino acid composition of E . histolytica putative ODC-like protein with His-tag and S-tag present at its N-terminal end was induced ( Figure 4 ) . The recombinant protein was purified on a Ni2+-NTA affinity chromatography column ( Figure 4A and 4B ) . To further confirm the size of the protein , a Western blot was done with anti-His antibody that revealed the band of purified product ( ∼ 48 kDa ) ( Figure 4C ) . Recombinant E . histolytica putative ODC-like protein was used to raise polyclonal antibody in BALB/c mice as described in Materials and Methods . The antiserum recognized a ∼48 kDa fusion protein on a Western blot of purified recombinant E . histolytica putative ODC-like fusion protein ( Figure 4D ) . The same antiserum detected a band of anticipated E . histolytica putative ODC at size ∼46 kDa in Western blots of parasite cell extracts , in agreement with the value calculated from the predicted sequence ( Figure 4E ) . Purification of His-tagged E . histolytica putative ODC-like protein by metal affinity chromatography yielded ∼3–4 mg of pure protein from a 1-liter bacterial culture . ODC activity was measured in the crude E . histolytica lysates and in the recombinant putative ODC-like protein . Detailed study was limited by its remarkable instability . Addition of dithiothreitol ( 2 mM DTT ) , a known stabilizer of mammalian ODC [43] , to the purified enzyme samples did not improve the stability of the enzyme or its activity . However , we were able to measure the activity by adding 0 . 002% BRIJ-35 to the reaction mix . The activity obtained in the crude lysate was 4 . 8±0 . 8 nmol h−1 mg−1 protein , and the recombinant protein gave an activity of 1 , 311±7 . 0 nmol h−1 mg−1 protein ( Table 1 ) . Addition of DFMO ( 10 mM ) to the recombinant ODC protein did not have any affect on the ODC activity ( 1 , 085±15 nmol h−1 mg−1 protein ) . The values obtained were not significantly different from that of the control with no DFMO . The Km value for the substrate ornithine was 1 . 5 mM . The activity obtained here for the recombinant protein was much lower than that reported earlier for the purified protein from E . histolytica [20] . Ammonium sulfate purification of the His-tagged recombinant ODC protein from E . histolytica did not improve the activity of this recombinant protein . Since we were not able to obtain higher Km values using ornithine as the substrate , we checked substrate preference . Decarboxylation of L-arginine and L-lysine was also measured in order to check the substrate preference of the recombinant protein . In the ODC assay we found no activity using arginine and lysine as the substrate . Analysis of polyamine content of E . histolytica revealed substantial levels of putrescine ( 137±6 . 4 nmol/mg protein ) compared to spermidine , which is present in very low amounts ( 6 . 9±0 . 2 nmol/mg protein ) . Spermine was not detected in the lysate ( Table 1 ) . E . histolytica putative ODC-like protein is 413 amino acids long , with 32% sequence identity with the ODCs of T . brucei and H . sapiens . We looked for two important motifs in the sequence of ODC that are essential for binding of DFMO and PLP . The homologues of known three-dimensional structure complexed with DFMO show that the amino acid motif GPSCNGSD corresponds to the binding site for DFMO . DFMO is known to bind to cysteine in this motif . This cysteine is well conserved in the ODC from all three organisms ( T . brucei , H . sapiens , and E . histolytica ) . Despite the presence of the cysteine , E . histolytica putative ODC is not inhibited by DFMO . Concentration of DFMO as high as 10 mM did not inhibit the enzyme activity in vitro . The lysine in the PCFAVKCN motif is an important residue for the interaction of PLP with ODC , which is well conserved in ODC from all three organisms discussed above . Structural analysis of the E . histolytica putative ODC is shown in Figure 5 . Known three-dimensional structural analysis suggests that DFMO , which can inhibit T . brucei ODC , makes a hydrogen bond ( H-bond ) with three residues in the two chains of ODC . The nitrogen ( ε ) of DFMO forms an H-bond with aspartate-332 , and water mediates H-bonds with aspartate-361 and tyrosine-323 , both from the same chain ( Figure 5A ) . All the three residues mentioned above with which DFMO is interacting are replaced in E . histolytica putative ODC . Tyrosine-323 , aspartate-332 , and aspartate-361 of the T . brucei ODC are substituted by histidine-296 , phenylalanine-305 , and asparagine-334 respectively in the E . histolytica putative ODC . The distance between the nitrogen atom ( ε ) of DFMO and asparagine-334 is more than 3 . 4Å in E . histolytica and hence unable to form an H-bond . These three residues have been labeled in the modeled structure ( Figure 5B ) of E . histolytica . It should be noted that a deliberately unrealistic model of the E . histolytica putative ODC in complex with DFMO was generated in order to understand why DFMO does not bind to E . histolytica putative ODC . The overall conformation of modeled E . histolytica putative ODC in complex with DFMO and PLP is not very different from that of the T . brucei ODC . The site of PLP binding is fully conserved , thus E . histolytica putative ODC should be able to accommodate it . It is possible that the substitution of important interacting amino acids in E . histolytica putative ODC , makes DFMO unable to bind and hence unable to inhibit the action of E . histolytica putative ODC . However , this mechanism can be experimentally proved only by mutating these residues , namely histidine-296 , phenylalanine-305 , and ssparagine-334 , respectively , and determining if the inhibition is restored .
The polyamines putrescine , spermidine , and spermine are polycationic organic compounds present in all eukaryotic cells , including parasitic protozoans . It was reported earlier that the polyamines are essential for the proliferation of normal cells and for differentiation [6] . Ornithine decarboxylase is the rate-limiting enzyme in the de novo synthesis of polyamines and it catalyses the decarboxylation of ornithine to putrescine and is a highly regulated enzyme . Interest in ODC has arisen mainly from the observation that the development of certain tumors closely correlates with increase in enzyme activity and that specific inhibitors of ODC reduce or stop these malignancies [13] . In addition , because of the critical role of ODC in growth and differentiation of the cells , it has been exploited as a target to control certain parasitic infections with specific inhibitors of ODC such as DFMO , a structural analog of ornithine that has been proved as an efficient therapeutic drug [21] . In this paper , we describe the molecular cloning and characterization of putative ODC-like gene of E . histolytica , a protozoan parasite known for causing amoebiasis . We cloned the putative ODC-like gene of E . histolytica ( GenBank accession number AY929249 ) , and there is only 33% identity to H . sapiens ODC . Comparison of the putative ODC-like protein sequence from E . histolytica with other eukaryotic species revealed conserved regions . The sequence PFYAVKCN , which resembles the consensus sequences of PXXAVKC ( N ) , contains the lysine ( K ) to which the pyridoxal 5′ phosphate cofactor binds . Other highly conserved amino acid stretches , for example , FDCAS , EPGR , and FNGF , are also conserved , although their functional significance of these stretches is not known . Phylogenetic tree analysis showed a close evolutionary relationship of ODC of E . histolytica and the plant D . stramonium . However , comparison of E . histolytica putative ODC-like sequences with L . donovani and H . sapiens showed closer evolutionary relationship with L . donovani . The recombinant putative ODC from E . histolytica was very unstable . Addition of 2 mM DTT to the enzyme samples did not improve the activity or stability of this enzyme . Earlier reports also show that purified ODC from trophozoites of E . histolytica lost most of the activity after 24 h in unfractionated samples and was reported to be very unstable [20] . In our hands even ammonium sulfate purification of the His-tagged recombinant putative ODC-like protein from E . histolytica did not improve the activity of this recombinant protein . The irreversible inhibitor DFMO ( α-difluoromethylornithine ) did not inhibit activity of E . histolytica recombinant ODC ( data not shown ) . Similar observations made previously rules out the possibility of its being used as a suitable target for this enzyme [20] . It has been reported that purified preparations of E . histolytica ODC contain a major polypeptide band of 45 kDa and barely detectable amounts of two other proteins of 70 and 120 kDa . Both the 45 and the 70 kDa bands were recognized by a mouse anti-ODC monoclonal antibody [20] . However , in the present study , Western blot analysis of the whole cell lysates of E . histolytica using the polyclonal antibody against E . histolytica putative ODC-like enzyme showed a single band of approximately 46 kDa , and the same antibody recognized the recombinant protein of about 48 kDa , the expected size of the putative ODC-His tag fusion protein . Analysis of polyamine content of E . histolytica revealed significant levels of putrescine compared to spermidine , which is present in very low amounts . Spermine was not detected in the lysates . Computer modeling revealed that three of the critical residues required for binding of DFMO to the ODC enzyme are substituted in E . histolytica resulting in the likely loss of interactions between the enzyme and DFMO . These residues correspond to Tyrosine-323 , Aspartate-332 and Aspartate-361 in T . brucei ODC homologue and these are substituted by histidine-296 , phenylalanine-305 , and asparagine-334 respectively in E . histolytica homologue . It is known that Asp-332 and Asp-361 are essential catalytic residues that interact with the substrate [44] . Several members of the ODC family are known to be found in the GenBank database with amino acid substitutions at the position of Asp-332 ( D332E ) [45]; however , our present study shows that amino acid substitution at Asp-361 ( an active site ) is unique to E . histolytica putative ODC . Asp-332 is highly conserved in the ODC family and is known to play an important role in substrate binding and catalysis . Shah et al . [45] reported that in Paramecium bursaria chlorella virus-1 ornithine decarboxylase ( PBCV-1 DC ) the equivalent position is residue 296 , which is glutamate; according to the authors this substitution was a key determinant in the change in the substrate specificity from ornithine to arginine . This substitution ( D332E ) has also been observed in sequences of antizyme inhibitor , which is an inactive ODC homolog that regulates ODC activity ( GenBank accession numbers: human , NP_680479; mouse , NP_06125; and rat , NP_072107 ) [46] . Furthermore , they investigated the impact of the active-site difference at position 332 on substrate specificity and mutated Asp332 ( E296D ) . They reported that this substitution alone was insufficient to produce the observed substrate specificity change in PBCV-1 DC . In the present study we found a unique E . histolytica substitution in the putative ODC-like gene sequence both at Asp-332 and Asp-361; given that the amino acid changes affected the pocket of the E . histolytica enzyme , we wanted to know whether it was likely to cause a significant change in substrate specificity . We checked the activity of the recombinant protein using arginine and lysine as the substrate and found no enzyme activity . Kinetic analysis using ornithine as the substrate showed lower kinetic parameters compared to those reported for well-characterized enzymes from other organisms . It is possible that this enzyme has evolved a novel substrate specificity . In view of this situation we feel that this E . histolytica protein might have other functions , so far unidentified , including a regulatory role . In conclusion , characterization of the E . histolytica putative ODC-like enzyme and expression of the protein will facilitate studies of structural and functional aspects of the enzyme and could prove to be an important anti-amoebic target . | Entamoeba histolytica is a unicellular protozoan parasite that infects about 50 million people each year and can cause potentially life-threatening diseases such as hemorrhagic colitis and extraintestinal abscesses . The infections are primarily treated by antiamoebic therapy . Drugs of choice for invasive amoebiasis are tissue-active agents , such as metronidazole , tinidazole , and chloroquine . Although drug resistance to E . histolytica does not appear to be a serious problem , there are occasional reports of failure with metronidazole , suggesting that clinical drug resistance may be developing . When identifying a drug target , it is important that the putative target be absent in the host , or , if it is present in the host , that the homologue in the parasite be substantially different from the host homologue so that it can be exploited as a drug target . Such is the case with the enzymes involved in polyamine biosynthesis , a pathway that has been exploited as a target to control disease caused by several parasites . We report , to our knowledge for the first time , molecular cloning , expression , and characterization of the ornithine decarboxylase from E . histolytica , a rate limiting enzyme in the polyamine biosynthesis pathway . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"molecular",
"biology"
] | 2008 | Characterization of the Entamoeba histolytica Ornithine Decarboxylase-Like Enzyme |
The porcine reproductive and respiratory syndrome virus ( PRRSV ) is a major threat to swine health worldwide and is considered the most significant viral disease in the swine industry today . In past years , studies on the entry of the virus into its host cell have led to the identification of a number of essential virus receptors and entry mediators . However , viral counterparts for these molecules have remained elusive and this has made rational development of new generation vaccines impossible . The main objective of this study was to identify the viral counterparts for sialoadhesin , a crucial PRRSV receptor on macrophages . For this purpose , a soluble form of sialoadhesin was constructed and validated . The soluble sialoadhesin could bind PRRSV in a sialic acid-dependent manner and could neutralize PRRSV infection of macrophages , thereby confirming the role of sialoadhesin as an essential PRRSV receptor on macrophages . Although sialic acids are present on the GP3 , GP4 and GP5 envelope glycoproteins , only the M/GP5 glycoprotein complex of PRRSV was identified as a ligand for sialoadhesin . The interaction was found to be dependent on the sialic acid binding capacity of sialoadhesin and on the presence of sialic acids on GP5 . These findings not only contribute to a better understanding of PRRSV biology , but the knowledge and tools generated in this study also hold the key to the development of a new generation of PRRSV vaccines .
At the end of the 1980s , a new syndrome was described affecting pig herds in North America and Canada [1] , [2] . This ‘mystery swine disease’ manifested itself in respiratory problems and reproductive disorders and was eventually designated Porcine Reproductive and Respiratory Syndrome ( PRRS ) , reflecting the associated disease symptoms . The causative agent is a positive sense RNA virus that groups within the order Nidovirales , family Arteriviridae , and is referred to as the PRRS virus ( PRRSV ) [3] . At present , the disease is endemic in swine-producing countries worldwide , causing enormous production losses in the swine industry . A study by Neumann et al . assessing the economic impact of PRRS on swine production in the US reported an annual loss of approximately 560 . 32 million US dollars due to this syndrome [4] . Also , recent studies report on the emergence of highly pathogenic variants of the virus in Asia causing atypical PRRS or ‘High Fever’ disease [5]–[7] . Consequently , PRRS is considered to be the most significant viral disease problem in the swine industry today . Availability of safe and effective vaccines is essential for PRRSV control . Currently , there are two types of PRRSV vaccines on the market: attenuated and inactivated vaccines . However , these have specific drawbacks concerning safety [8]–[10] and efficacy [11]–[15] and there is a strong demand for a new generation of vaccines . Up until now , PRRSV vaccine development often has followed the trial and error approach . As there was a clear gap in the knowledge of PRRSV ligands that bind to specific receptors , it was difficult to aim for specific blocking of crucial steps in PRRSV infection of the porcine macrophage . A fundamental understanding of how PRRSV enters its host cell is crucial to reverse the tide . The PRRSV virion consists of a nucleocapsid that is surrounded by a lipid envelope . The capsid structure consists of nucleocapsid proteins ( N ) and contains the viral genome: a single , positive RNA strand of approximately 15 kb [16]–[21] . In the viral envelope , six structural proteins are embedded: the small envelope protein E , the membrane protein M and the glycoproteins GP2 , GP3 , GP4 and GP5 [21] . However , some North American PRRSV isolates do not seem to incorporate GP3 as a structural membrane protein , in contrast to European and other North American isolates [21]–[24] . The major structural proteins M and GP5 have been shown to form disulfide-linked heterodimers [21] , [25] , [26] . The minor structural proteins GP2 , GP3 and GP4 form non-covalent heterotrimers in the virion and there are indications that also the E protein may be associated with the minor glycoprotein trimer [21] , [23] , [27]–[29] . As has been shown for other arteriviruses , PRRSV shows a marked in vivo tropism for cells of the monocyte/macrophage lineage: the virus infects specific subsets of porcine macrophages [30] , [31] . Primary cultures of alveolar macrophages ( PAM ) are the only cells that allow efficient ex vivo virus propagation . In addition , a limited number of cell lines support in vitro virus replication upon adaptation of the virus . One such cell line , the African green monkey kidney cell line MARC-145 [32] , has become the most widely used cell type for PRRS virus production . Over the past years , various studies have focussed on the entry of PRRSV into its host cell . These efforts have resulted in the identification of a number of macrophage molecules involved in PRRSV entry . As for many other viruses , initial binding of the virus to its host cell occurs via interactions with heparan sulphate glycosaminoglycans present on the cell surface [33]–[35] . The virus receptor that determines subsequent virus entry and that likely accounts for the macrophage tropism of PRRSV has been identified as porcine sialoadhesin ( pSn ) . This macrophage-specific molecule is a sialic acid-binding immunoglobulin-like lectin ( siglec ) that mediates virus attachment and subsequent internalization via clathrin-mediated endocytosis [36] , [37] . Virus attachment to this receptor is dependent on the sialic acid-binding activity of the N-terminal immunoglobulin-like domain of pSn [38] and on the presence of sialic acids on the virion surface [39] . A study by Delputte et al . pointed out that α2-3 linked sialic acids and to a lesser extent α2-6 linked sialic acids , most likely present on complex N-linked glycans attached to viral envelope glycoproteins , are involved in this interaction [39] . Clearly , a glycosylated PRRSV protein is responsible for PRRSV binding to pSn , but the exact viral ligand has not yet been identified . After pSn-dependent internalization into the endosomal compartment of the macrophage , the viral genome is released into the cytoplasm , thereby initiating the transcriptional and translational events necessary for the production of new virions . The scavenger receptor CD163 has been shown to be essential for virus uncoating [40] , [41] . A pH drop within the endosome is required [37] , [42] and also the aspartic protease cathepsin E and a yet unidentified trypsin-like serine protease [43] have been implicated in this process . Although pSn has been shown to be a critical entry receptor for PRRSV on macrophages , viral envelope glycoproteins that act as ligands for this receptor have remained elusive . In the light of vaccine development , knowledge on this is particularly interesting , since it allows targeting of the immune response to a specific , critical step in virus infection . Vaccination with a functional viral pSn-binding epitope can elicit a protective immune response that specifically blocks the crucial , pSn-dependent internalization of the virus into its host cell . By construction and use of soluble recombinant porcine sialoadhesins , we identified the viral M/GP5 glycoprotein complex as a ligand for pSn and showed the sialic acid-dependency of the pSn-M/GP5 interaction .
The experimental procedure for the collection of porcine alveolar macrophages was authorized and supervised by the Ethical and Animal Welfare Committee of the Faculty of Veterinary Medicine of Ghent University . The use of human red blood cells was approved by the Medical Ethical Committee of the Ghent University Hospital and informed written consent was obtained from the donors of red blood cells . Detection of pSn was performed using the mouse monoclonal antibody ( mAb ) 41D3 [36] , [44] that recognizes a conformational epitope within the N-terminal sialic acid-binding domain of pSn . Detection of structural PRRSV proteins was performed using the following mAbs: mAb VII2D/5-1D ( IgG1 ) , mAb XVI11C/5-10F ( IgG2a ) and mAb VII2H/2-4D ( IgG1 ) [45] were used for the detection of GP3 , GP4 and GP5 , respectively . Detection of the M protein was performed using mAb 126 . 3 ( IgG2a ) [28] and mAb P3/27 ( IgG1 ) [46] was used for detection of the N protein . MAb 13D12 [47] and mAb 16G12 [48] were used as isotype-matched , irrelevant control mAbs for the mAbs with IgG1 and IgG2a isotype , respectively . Porcine alveolar macrophages ( PAM ) were obtained from 4- to 6-week-old conventional Belgian Landrace pigs from a PRRSV-negative herd as described by Wensvoort et al . [2] and cultivated as described by Van Gorp et al . [40] . MARC-145 cells were cultivated as described before [40] . HEK-293T cells were grown in DMEM ( Gibco ) containing 10% heat-inactivated FBS ( Gibco ) , 2 mM L-glutamine , 1 mM sodium pyruvate and a mixture of antibiotics . Cell cultures were kept in a humidified 5% CO2 atmosphere at 37°C . Human red blood cells ( RBCs ) were obtained from healthy donors and stored at 4°C in Alsever's solution for up to 7 days . The European prototype PRRSV strain Lelystad Virus [2] ( LV; kindly provided by G . Wensvoort ) was passaged 14 times on macrophages ( macrophage-grown LV stock ) or 13 times on macrophages and subsequently 5 times on MARC-145 cells ( MARC-145-grown LV stock ) . Virus was semipurified from the supernatants via ultracentrifugation as described before [39] . Virus titrations on MARC-145 cells and macrophages and calculation of the virus titers were performed as described by Van Gorp et al . [40] . The pSn cDNA had been cloned previously into the pcDNA3 . 1/D vector ( Invitrogen ) [36] . The soluble Fc-tagged pSn , pSn4D-Fc , was generated by polymerase chain reaction ( PCR ) amplification as done before for human sialoadhesin [49] and cloning into a modified version of the pEE14 vector , designated pEE14-3C-Fc [50] . The cDNA fragment corresponding to the first 4 N-terminal immunoglobulin-like domains of pSn was amplified using forward primer 5′-CCTTCACCATGGACTTCCTG-3′ and reverse primer 5′-ACTAGATCTACTTACCTGTGCTGACCACCACGCTGACAG-3′ . The pEE14-3C-Fc vector was cut with HindIII , treated with Klenow DNA polymerase to obtain blunt ends and subsequently cut with BamHI . The PCR product was digested with BglII and cloned into the cut pEE14-3C-Fc vector , yielding pEE14-pSn4D-3C-Fc . To obtain a non-sialic acid-binding pSn4D-Fc protein , a point-mutation ( R116E ) was introduced in the sialic acid-binding domain in pEE14-pSn4D-3C-Fc using the Quickchange site directed mutagenesis kit ( Stratagene ) with forward primer 5′-TCGGGCTCCTATAACTTCgaaTTTGAGATCAGCGAGGGC-3′ and reverse primer 5′-GCCCTCGCTGATCTCAAAttcGAAGTTATAGGAGCCCGA-3′ , resulting in pEE14-pSn4DRE-3C-Fc . For production of the pSn-Fc chimeras , HEK-293T cells were transiently transfected using calcium phosphate . Transfected cells were cultured for 3 days in DMEM supplemented with 3% IgG-depleted low IgG FBS ( Gibco ) , 2 mM L-glutamine , 1 mM sodium pyruvate , 1% nonessential amino acids ( 100× stock; Gibco ) and a mixture of antibiotics in a humidified 5% CO2 atmosphere at 37°C , after which the culture supernatant was collected . The pSn-Fc fusion proteins were purified from the supernatant using standard protein A sepharose chromatography following the manufacturer's instructions ( GE Healthcare ) . Fractions of the eluate containing the purified protein were pooled and the buffer was exchanged to phosphate-buffered saline ( PBS ) by dialysis . Purified protein was stored at −70°C until use . Each well of an Immulon 4HBX 96-well flat-bottomed microtiter plate ( Dynax Technologies Inc . ) was coated with 50 µl of 8 µg/ml goat anti-human IgG ( Fc- specific; Sigma-Aldrich Corp . ) in 0 . 05 M carbonate-bicarbonate buffer ( pH 9 . 6; Sigma-Aldrich Corp . ) . Plates were washed with PBS containing 0 . 25% bovine serum albumin ( PBA ) , after which 2-fold dilution series of the purified siglec-Fc chimera were added to the wells and incubated for 2 h at ambient temperature . Unbound siglec-Fc protein was removed by washing with PBA . RBCs were washed twice in PBA and resuspended in PBA at 0 . 25% ( v/v ) immediately before use . 100 µl of the RBC suspension was added to each well and the plate was incubated for 30 min at 37°C . The wells were gently washed with PBA to remove unbound RBCs and RBC binding was evaluated using an Olympus CK40-F200 inverted light microscope ( Opelco ) . To quantify RBC binding , the plate was dried , fixed with methanol and dried again , after which a peroxidase substrate assay was performed ( Substrate Reagent Pack; R&D Systems; processed according to manufacturer's instructions ) . The result was evaluated measuring the optical density at 450 nm ( OD450 ) using a Multiskan RC ( Thermo Labsystems ) . As a positive control , an identical assay was performed in parallel for mSiglecE-Fc [51] , a protein with established sialic acid-binding activity . As a control for sialic acid-dependent binding , sialidase-treated RBCs were used . To remove sialic acids from the cell surface , RBCs were diluted ( 0 . 25% v/v ) in RPMI-1640 containing 50 mU/ml Vibrio cholerae sialidase ( Roche Applied Science; specific for α2-3 , 6 , 8 linked sialic acids ) and incubated for 1 h at 37°C . The RBCs were subsequently washed with PBA to remove the enzyme , resuspended in PBA at 0 . 25% ( v/v ) and used in the solid phase binding assay as described . Samples of purified pSn-Fc protein were mixed with ( non- ) reducing Laemmli buffer , boiled for 5 min and subjected to SDS-PAGE ( 8% gel ) using a BioRad Mini Protean 3 system . For Coomassie Blue staining , the SDS-PAGE gel was incubated successively in Coomassie Blue staining solution ( 0 . 125% Coomassie Blue , 50% methanol , 10% acetic acid ) , destaining solution I ( 50% methanol , 10% acetic acid ) and destaining solution II ( 5% methanol , 7% acetic acid ) . Alternatively , for Western Blot analysis , proteins were transferred from the SDS-PAGE gel to a PVDF membrane ( Membrane Hybond-P , GE Healthcare ) via Western Blotting ( BioRad Mini Trans Blot ) . The membrane was blocked overnight in PBS +0 . 1% Tween 20+5% skimmed milk . Detection of pSn-Fc protein was performed by subsequent incubation of the blot with the pSn-specific mAb 41D3 and peroxidase-labeled polyclonal goat anti-mouse antibodies ( Dako ) , followed by visualization using enhanced chemiluminiscence ( ECL; GE Healthcare ) . Alternatively , pSn-Fc protein was detected using peroxidase-labeled goat anti-human IgG antibodies ( Fc-specific; Sigma-Aldrich Corp . ) and subsequent ECL . Samples of purified pSn4D-Fc and pSn4DRE-Fc protein were either left untreated or were treated with endoglycosidase H or PNGase F ( New England Biolabs Inc . ; used according to manufacturer's instructions ) . Also , samples of both proteins were incubated with 100 mU/ml Vibrio cholerae sialidase ( Roche Applied Science; used according to manufacturer's instructions ) for 3 h at 37°C . After addition of reducing Laemmli buffer , samples were boiled for 5 min and analyzed via SDS-PAGE and Western Blot using peroxidase-labeled goat anti-human IgG antibodies as described above . 100 µl of Dynabeads protein A ( Invitrogen ) were coated with 25 µg of pSn4D-Fc protein and incubated with semipurified , MARC-145-grown PRRSV LV at 37°C . After 90 min of incubation , the unbound virus fraction was collected , beads were washed 4 times with PBS and bound material was eluted with 0 . 1 M citrate buffer ( pH 3 . 1 ) . Samples of the semipurified virus , the bound and the unbound fraction were mixed with non-reducing Laemmli buffer , boiled for 5 min and resolved on a 12% SDS-PAGE gel . Subsequently , resolved proteins were transferred to a PVDF membrane via Western Blotting . The membrane was blocked overnight in PBS +0 . 1% Tween 20+5% skimmed milk and probed with a nucleocapsid-specific , a GP5-specific or an isotype-matched irrelevant control mAb in combination with peroxidase-labeled polyclonal goat anti-mouse antibodies . The detected protein bands were visualized using ECL . To confirm that the beads were efficiently coated with pSn-Fc protein , a sample of the bound fraction was subjected to SDS-PAGE and Western Blot analysis using the pSn-specific mAb 41D3 . As a control , identical experiments were performed using uncoated beads and beads coated with the non-sialic acid-binding mutant pSn4DRE-Fc . PAM were seeded at a concentration of 105 cells/well in 96-well plates and kept in culture for 48 h . 50 µl of a 3-fold dilution series of pSn4D-Fc or pSn4DRE-Fc ( starting concentration 50 µg/ml ) was mixed with a constant amount of virus ( 5 µl of a 2×105 TCID50/ml virus suspension ) and incubated for 1 h at 37°C to allow binding . The mixtures were transferred to the PAM and cells were incubated for 1 h at 37°C . Soluble receptor-virus mixtures were then removed , fresh medium was added and cells were further incubated for 9 h at 37°C , after which they were fixed . Infected cells were then visualized via a nucleocapsid-specific immunoperoxidase staining [2] and counted . Viral proteins were solubilized from semipurified MARC-145-grown PRRSV LV by a 1 h incubation in TNE buffer ( 50 mM Tris-HCl ( pH 7 . 4 ) , 200 mM NaCl , 1 mM EDTA ) containing 1% NP-40 ( Roche Applied Science ) and insoluble material was pelleted by centrifugation at 10 , 000×g for 45 min . Subsequently , the virus lysate was incubated with pSn4D-Fc-coated beads . The precipitation experiment , sample preparation and subsequent SDS-PAGE and Western Blotting were performed as described above . Membranes were blocked overnight in PBS +0 . 1% Tween 20+5% skimmed milk and probed with mAbs directed against the structural proteins of PRRSV LV and isotype-matched irrelevant control mAbs in combination with peroxidase-labeled polyclonal goat anti-mouse antibodies . Detected protein bands were visualized using ECL . Coating of the beads with pSn-Fc protein was checked as described above . To assess the sialic acid-dependency of the interaction , an identical experiment was performed using the non-sialic acid-binding mutant pSn4DRE-Fc . Alternatively , sialidase-treated virus was used in the precipitations . Semi-purified virus was incubated with 100 mU/ml sialidase from Vibrio cholerae in RPMI-1640 for 3 h at 37°C . In parallel , virus was incubated with the buffer in which the sialidase was supplied . Titration on alveolar macrophages revealed a 90% decrease in infectivity of sialidase-treated virus when compared to control-treated virus , indicating that sialidase treatment removed sialic acids implicated in viral entry . To evaluate the effect of the treatment on different envelope glycoproteins , samples of sialidase- and control-treated virus were subjected to SDS-PAGE and Western Blot analysis using specific mAbs . The remaining virus was lysed and subjected to immunoprecipitation using pSn4D-Fc as described above . Similar precipitation reactions were performed using macrophage-grown virus . Lysates of semipurified macrophage-grown virus were applied to pSn4D-Fc- and pSn4DRE-Fc-coated beads . The bound and unbound fractions as well as the original virus lysates were analyzed via SDS-PAGE and Western Blotting as described above . GenBank accession numbers ( http://www . ncbi . nlm . nih . gov/Genbank ) :
To identify the PRRSV envelope glycoproteins interacting with pSn , we constructed a soluble form of the pSn receptor ( pSn4D-Fc ) . This recombinant protein consists of the 4 N-terminal domains of pSn , coupled with the Fc- and hinge region of human IgG1 and allows study of PRRSV-pSn interactions in a cell-free context . The sialic acid-binding activity of pSn has been shown before to be essential for PRRSV binding [38] . Therefore , a non-sialic acid-binding mutant of the soluble pSn ( pSn4DRE-Fc ) was generated as a control . This protein was obtained by changing the amino acid R116 , which is essential for sialic acid binding , to an E residue . PSn- and Fc-specific immunofluorescence cell stainings ( Protocol S1 ) and ELISAs of the cell supernatants ( Protocol S2 ) revealed that the pSn-Fc chimeras were expressed in transfected HEK-293T cells and that they were secreted into the culture medium ( data not shown ) . After optimization of production and purification , all further tests and experiments were performed using purified pSn-Fc proteins . To check the purity of the proteins , samples were resolved on SDS-PAGE under reducing and non-reducing conditions and Coomassie Blue staining was performed ( Fig . 1A ) . Under non-reducing conditions , a single band of more than 250 kDa was seen for both the pSn4D-Fc and pSn4DRE-Fc protein . Presence of a reducing agent resulted in single bands at about 80 kDa . These results indicate that the purified pSn-Fc proteins are present as disulfide-linked dimers under non-reducing conditions . The fact that single bands were obtained under each condition shows that the bulk of the protein in the purified fractions was pSn-Fc protein . Purified proteins were also subjected to SDS-PAGE and Western Blot analysis ( Fig . 1B ) . Under non-reducing conditions , both pSn4D-Fc and pSn4DRE-Fc were recognized by pSn-specific mAb 41D3 . When SDS-PAGE was performed in the presence of a reducing agent , binding of mAb 41D3 to the blotted proteins was lost , which is in line with earlier observations for wild type pSn . Both the pSn4D-Fc and the pSn4DRE-Fc proteins could be detected on the blot membrane using polyclonal antibodies specific for the Fc-part of human IgG . Using the Fc-specific polyclonal antibodies for detection , additional bands could be observed with varying intensity in between batches . These bands most likely represent pSn-Fc protein that is partially degraded . Nevertheless , Coomassie Blue analysis of the same samples clearly indicated that the bulk of the protein was present in one specific band , which correlates with conformationally correct pSn-Fc protein . Wild type pSn undergoes processing in the endoplasmic reticulum ( ER ) and Golgi before being displayed at the cell membrane . During its passage through these compartments , the protein does not only obtain intramolecular disulfide bridges , but also N-linked glycans , which are often essential for proper protein folding . To check intracellular processing of the recombinant proteins , purified pSn4D-Fc and pSn4DRE-Fc proteins were treated with different glycosidases and analyzed via reducing SDS-PAGE and Western Blotting ( Fig . 1C ) . Treatment of the proteins with N-glycosidase F , which removes all types of N-linked glycans , resulted in a clear shift in the protein size . Treatment with endoglycosidase H , which removes high mannose and some hybrid types of N-linked carbohydrates from glycoproteins , or Vibrio cholerae sialidase , removing sialic acids in a α2-3 , α2-6 or α2-8 configuration , did not increase the electrophoretic mobility of the proteins . These results indicate that the soluble pSn proteins carry mainly complex type N-glycans capped with little or no sialic acids . The presence of these sugar moieties shows that the soluble sialoadhesins pass through the ER and Golgi for processing , as has been shown for wild type pSn [36] . Since the sialic acid-binding activity of pSn is essential for PRRSV binding , we first evaluated the sialic acid-binding capacity of the purified pSn4D-Fc protein . This was done via a solid phase red blood cell binding assay , a test routinely used to analyze the sialic acid-binding capacity of sialoadhesin-Fc and other siglec-Fc chimeras [52] ( Fig . 2A & 2B ) . The pSn4D-Fc protein showed clear , sialic acid-dependent RBC binding , since removal of sialic acids from the RBC surface impeded the interaction . The pSn4DRE-Fc protein did not show RBC-binding activity . These results indicate that pSn4D-Fc has the capacity to bind sialic acids . As for full length pSn , the sialic acid-binding activity is critically dependent on the R116 residue within the N-terminal domain of pSn . To assess the PRRSV-binding capacity , pSn4D-Fc was coated on protein A beads and incubated with purified virus at 37°C to allow binding . The unbound fraction was then collected , beads were washed and the bound material was eluted from the beads . Samples of the bound and unbound fraction and samples of purified virus were then subjected to non-reducing SDS-PAGE and Western Blotting and analyzed for the presence of virus using mAbs recognizing the GP5 envelope glycoprotein and the nucleocapsid protein N ( Fig . 2C ) . GP5 and N could clearly be detected in both the purified virus stock and the bound fraction , while little or no protein was found in the unbound fraction . These results show that pSn4D-Fc is able to efficiently bind the virus . When beads were coated with pSn4DRE-Fc or when uncoated beads were used , viral proteins were detected in the unbound fraction but not in the bound fraction . These findings show that the interaction of pSn4D-Fc with PRRSV is similar to the interaction between wild type pSn and the virus . To further substantiate this , we tested the soluble receptors for their infection-inhibition capacity ( Fig . 2D ) . We found that PRRSV infection of alveolar macrophages could be partially blocked by pre-incubating the virus with pSn4D-Fc . This effect was dose-dependent and no such effect was obtained using the pSn4DRE-Fc protein , indicating that also here the sialic acid-binding functionality of pSn is crucial for the interaction . These data evidence that the soluble sialoadhesin pSn4D-Fc can compete with the wild type pSn on the macrophage surface for specific ligands present on the PRRSV virion surface and confirm the role of pSn as an important PRRSV receptor on macrophages . To identify viral ligands for the PRRSV receptor pSn , pSn4D-Fc was used in an immunoprecipitation reaction . PSn4D-Fc was coated on protein A beads and incubated with a lysate of purified virus at 37°C to allow binding . Subsequently , the unbound lysate fraction was collected , beads were washed and the bound material was eluted from the beads . Samples of the bound and unbound fraction and samples of the original virus lysate were subjected to non-reducing SDS-PAGE and Western Blotting and analyzed for the presence of specific viral envelope proteins using protein-specific mAbs ( Fig . 3A ) . GP3 , GP4 and the M/GP5 complex were all detected in the original virus lysate and in the unbound fraction . In addition , the M/GP5 complex was detected in the bound fraction . GP3 and GP4 were however not found in the bound fraction . These results indicate that the viral M/GP5 complex is able to bind to pSn4D-Fc . Control experiments with pSn4DRE-Fc pointed out that this protein does not interact with any of the viral proteins analyzed . This shows that the sialic acid-binding capacity of pSn is essential for M/GP5 binding and suggests that sialic acids on GP5 are involved in the interaction with pSn . Sialidase treatment of PRRSV , which removes sialic acids from the GP5 protein as indicated by an increased electrophoretic mobility of the protein on SDS-PAGE ( Fig . 3B ) , indeed also resulted in loss of M/GP5 binding to pSn4D-Fc ( Fig . 3C ) . The experiments described above clearly showed that sialic acids on the envelope glycoproteins are essential for the pSn-M/GP5 interaction . However , as the glycosylation machinery of MARC-145 cells and porcine macrophages differs , their glycome and the glycan array present on virus grown in these cells may differ substantially . As all experiments were performed with MARC-145-grown virus , immunoprecipitations were also performed using lysates of purified macrophage-grown PRRSV ( Fig . 4 ) . The results showed that also the M/GP5 glycoprotein complex of macrophage-grown PRRSV binds to the pSn4D-Fc protein . Binding was also critically dependent on the sialic acid-binding capacity of pSn , as no interaction was observed with the non-sialic acid-binding mutant pSn4DRE-Fc . No binding was observed for the GP3 and GP4 proteins . These results suggest that macrophage-grown and MARC-145-grown PRRSV interact with pSn in the same way .
PRRSV has a very restricted tropism for subpopulations of differentiated cells of the monocyte/macrophage lineage . The virus infects macrophages in lungs and several lymphoid organs , while other cells such as circulating blood monocytes and peritoneal macrophages are refractory [53] , [54] . This restricted cell tropism has been attributed to the restricted expression pattern of the PRRSV receptor pSn on these subsets of differentiated macrophages [36] , [44] , [55] . Since pSn is an essential PRRSV-binding and -internalization receptor on porcine macrophages , identification of the PRRSV glycoprotein ligands that mediate virus binding to this receptor is clearly of great importance , not only for basic understanding of PRRSV replication biology , but also for development of strategies to tackle PRRSV infection . For many viruses ( e . g . classical swine fever virus , porcine circovirus 2 ) , subunit vaccines comprising components involved in viral entry have proven very useful tools in the protection against viral infection [56]–[59] . A vaccine capable of inducing antibodies against the sialoadhesin-binding epitope of PRRSV should be capable of providing a good protection against PRRSV infection by neutralizing virus entry . Previously , the interaction of PRRSV with pSn was shown to be dependent on a functional sialic acid-binding domain on pSn [38] and on the presence of sialic acids on the virion [38] , [39] , but the sialic acid-carrying viral ligand was never identified . This study aimed to identify PRRSV glycoprotein ligands for the pSn receptor . For this purpose , a soluble pSn was constructed and characterized . This soluble receptor showed the same binding functionality as wild type pSn , as it showed sialic acid-binding activity and could bind PRRSV in a sialic acid-dependent manner . Via pull-down assays in which the soluble receptor was mixed with lysates of PRRSV , it was shown that the M/GP5 complex of the PRRS virus interacts with the pSn receptor . This interaction was clearly dependent on the sialic acid-binding capacity of pSn , as a non-sialic acid-binding mutant of the soluble pSn differing in only one amino acid was not able to bind M/GP5 . Removal of sialic acids from the virus prior to the pull-down assay also blocked the interaction of the M/GP5 complex with pSn , evidencing the importance of sialic acids on the GP5 protein for interaction with the receptor . Clearly , the characteristics of the interaction of M/GP5 with pSn are identical to these of the interaction of PRRSV particles with pSn , suggesting that the M/GP5 complex indeed mediates PRRSV-particle binding to pSn . In this study , the interaction of PRRSV with the pSn receptor was initially studied using MARC-145-grown virus , as it is easy to obtain relatively high virus titers in these cells and it avoids the necessity of animal sacrifice . Moreover , MARC-145-grown virus can infect porcine alveolar macrophages and has been shown to interact efficiently with the pSn receptor [38] , [40] , indicating that MARC-145 cells provide correct glycosylation for the virus to interact with pSn . However , the glycome of the primary porcine macrophage , and hence of the virus grown therein , is most likely very different from the MARC-145 cell glycome . In the light of lectin-glycoprotein interaction , it was therefore imperative to evaluate the interaction of the viral glycoprotein ( complexe ) s of macrophage-grown PRRSV with the pSn receptor . Via pull-down experiments , in which lysates of macrophage-grown PRRSV were applied to immobilized recombinant sialoadhesins , it was found that also macrophage-grown PRRSV interacts with pSn via the M/GP5 glycoprotein complex . The interaction was also critically dependent on the sialic acid-binding capacity of pSn , indicating that the interaction of pSn with sialic acids is central to the pSn-M/GP5 interaction . While it should be kept in mind that primary macrophages cultured in vitro may also show different glycosylation than they do in vivo , it is clear that the interaction of pSn with macrophage-grown virus is more relevant to the in vivo situation than the interaction with MARC-145-grown virus . Previous studies on PRRSV have shown that virus glycosylation is crucial for its infectivity towards macrophages . Treatment of the virus with N-glycosidase F to remove N-glycans has a negative impact on viral infectivity and removal of sialic acids from the virion surface using sialidase even results in a 10- to 20-fold reduction of the infectivity [39] . Considering the findings of the present study , these results suggest a central role of sialic acid-carrying N-glycans on the GP5 glycoprotein in the interaction with sialoadhesin . The GP5 protein of most PRRSV isolates contains two conserved N-glycosylation sites . One or more additional putative N-glycosylation sites can be present , depending on the virus isolate [25] , [60] , [61] . One of the conserved glycosylation sites , N46 in European isolates , N44 in American isolates , is particularly interesting in the light of the present study as it appears to be critical for the formation of infectious PRRSV virions [62] , [63] . Wissink and coworkers showed that N46 is important for virus infectivity towards macrophages: the specific infectivity of recombinant GP5-N46Q mutant virus , in which the N46 glycosylation site was deleted , was reduced 10- to 20-fold when compared to the specific infectivity of wild type virus [62] . This correlates with the reduction in infectivity seen when virus is treated with sialidase to remove sialic acids from the surface [39] . Together , these data suggest that the critical sialic acid , necessary for M/GP5 binding to the pSn receptor , is present on the N-glycan appended to the N46/N44 amino acid . Another interesting observation is that the N46/N44 residue is part of the primary neutralization epitope of PRRSV [64]–[66] . While some other segments of the GP5 protein appear to be hypervariable , this epitope is located in an area of the GP5 ectodomain that is highly conserved among PRRSV isolates [65]–[70] . This suggests that this domain has a critical function in virus replication . It has been suggested to be crucial for the disulfide linkage between GP5 and M , as the cysteine residue involved in this linkage lies within the conserved area and since the linkage between GP5 and M is critical for virion formation [23] , [26] . It is however also tempting to speculate that this critical function lies in the interaction of the GP5 glycoprotein with the sialoadhesin receptor , this via essential sialic acids present on the glycan appended to the N46/N44 residue of GP5 . This could also explain why antibodies directed against this epitope appear to have a neutralizing effect on infection of macrophages . Previously , a study by Delputte et al . showed that neutralizing antibodies can indeed block attachment and internalization of the virus into macrophages [71] . Sialic acids are acidic monosaccharides often present at the termini of glycan chains on animal glycoconjugates [72] . Siglecs like sialoadhesin display a marked preference for specific types of sialic acids . However , also the linkage of the sialic acid to the subterminal sugar residue seems to influence the affinity of a siglec for a specific ligand . In addition , formation of high affinity interactions can depend on additional structural features of the glycoconjugate that carries the sialic acid [73] . These factors contribute to the specificity of a siglec for specific sialylated ligands and may explain why the GP3 and GP4 envelope proteins , although they carry sialic acids , do not seem to interact with sialoadhesin , while the M/GP5 glycoprotein complex shows a strong binding to this receptor . Furthermore , it is well known that physiologically relevant , high affinity lectin-glycan interactions strongly depend on the lectin and glycan valency [74] . As the pSn receptor is abundantly expressed at the macrophage cell surface [36] and as a single PRRSV virion carries a whole array of M/GP5 complexes on its surface [75] , it can be expected that also the interaction between macrophage and virus depends on avidity rather than on simple affinity . In conclusion , we identified the M/GP5 complex of PRRSV as a ligand for the pSn receptor . Furthermore , we showed that the M/GP5-pSn interaction is critically dependent on the sialic acid-binding capacity of pSn as well as on sialic acids on the viral GP5 glycoprotein . The information and tools generated in this study can provide a theoretical and practical basis for the development and evaluation of a new generation of inactivated and subunit PRRSV vaccines . | The porcine reproductive and respiratory syndrome virus ( PRRSV ) is a major threat to swine health worldwide . The virus specifically targets subpopulations of macrophages , central players in the immune system , and can persist in animals for extended periods of time due to a hampered immunity . At present , no vaccines are available that are both safe and effective and it is clear that a more rational vaccine design is needed to solve this problem . Therefore , advancing our fundamental understanding of PRRSV biology is crucial . The macrophage-specific lectin sialoadhesin is a crucial viral receptor on macrophages and although its role in PRRSV infection is well documented , its viral counterparts have remained unknown . Using a soluble form of sialoadhesin , we identified the M/GP5 glycoprotein complex of PRRSV as the ligand for sialoadhesin and found this ligand-receptor interaction to be critically dependent on the lectin activity of sialoadhesin and on sialic acids on the GP5 glycoprotein . These data represent a major breakthrough in the understanding of the role of PRRSV proteins in viral entry and pave the way for the development of a new generation of PRRSV vaccines capable of inducing an immunity that specifically blocks the interaction between viral M/GP5 and sialoadhesin . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/viral",
"infections",
"microbiology/cellular",
"microbiology",
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"pathogenesis",
"virology/host",
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] | 2010 | The M/GP5 Glycoprotein Complex of Porcine Reproductive and Respiratory Syndrome Virus Binds the Sialoadhesin Receptor in a Sialic Acid-Dependent Manner |
In recent experimental work it has been shown that neuronal interactions are modulated by neuronal synchronization and that this modulation depends on phase shifts in neuronal oscillations . This result suggests that connections in a network can be shaped through synchronization . Here , we test and expand this hypothesis using a model network . We use transfer entropy , an information theoretical measure , to quantify the exchanged information . We show that transferred information depends on the phase relation of the signal , that the amount of exchanged information increases as a function of oscillations in the signal and that the speed of the information transfer increases as a function of synchronization . This implies that synchronization makes information transport more efficient . In summary , our results reinforce the hypothesis that synchronization modulates neuronal interactions and provide further evidence that gamma band synchronization has behavioral relevance .
Gamma band synchronization has been found in many cortical areas and in a variety of tasks . It has been studied most extensively in the visual cortex of cats and monkeys [1]–[8] . Several authors have proposed that these synchronizations influence the interactions among neuronal groups [9] , [10] , a hypothesis referred to as communication through coherence ( CTC , [11] ) . In computational studies , it has been shown that entrainment enhances transmitted information between input and output spikes [12] , that synchronization in the gamma frequency range increases the effective synaptic gain for the responses to an attended stimulus [13] and that the transmission time of responses of coupled oscillators depends on the phase difference in the stable synchronized state [14] . Also , several experimental studies have presented results supporting the CTC hypothesis [15]–[19] . In this study , we concentrate on the results shown by Womelsdorf et al . [17] . They explore the mutual influence of two groups of neurons as a function of their phase shift . These authors quantify the mutual influence of the multi unit activity ( MUA ) in the two groups as the Spearman rank correlation coefficient of the two MUA's 60 Hz power . They show evidence that the correlation between the two groups of neurons varies as a function of the phase shift of the oscillations at 60 Hz . There is a specific phase shift at which the correlation between the two groups is highest . [17] conclude that the effective connectivity in a network can thus be maximized or minimized through synchronization of a specific phase relation , resulting in an effective interaction pattern . While the results presented by [17] clearly support the CTC hypothesis , they leave some open questions . Is it only the 60 Hz power that depends on the 60 Hz phase ? Do the MUAs only correlate or is there mutual interaction between the two ? Is this effect restricted to the gamma band or can it be generalized to other frequency bands ? What is the influence of the total gamma power in the signal ? Here , to address these questions , we use a detailed biophysical model network with realistic spiking properties . A first advantage of using a model is that we can generate more data than in an experiment . This makes it possible to use an information theoretical measure for the mutual interaction instead of rank correlation . Many different interdependence measures such as mutual information , transfer information , nonlinear regression , phase synchronization and generalized synchronization have recently been proposed ( see [20] and [21] for comparisons of the different methods ) . It has become evident that the appropriateness of each measure is determined by the data it is applied to . Thus , given our current data set , we opted to use transfer entropy ( TE ) , introduced by [22] . The TE is an information theoretical measure that quantifies the statistical coherence between systems . It has the advantage that it does not only measure the coherence between two signals , but is able to distinguish between driving and responding elements and therefore between shared and transported information . This is called the directionality of the information flow . We measure the TE between the MUA of the two neuronal clusters , which allows us to study the interdependence of the spiking activity in each of them and not just the correlation of the spectral power in a specific frequency band , as was done in the experimental work . A further crucial advantage of the model is that we can change network parameters systematically and explore the dynamical range of the network . The model we use in this study consists of integrate-and-fire neurons . One of two pools of excitatory neurons receives input ( Poisson spike train ) which it passes to a neighboring pool , connected by feedforward and feedback connections . Each pool of excitatory neurons is connected to a pool of inhibitory neurons , which generates oscillations in the gamma frequency band through a pyramidal-interneuron feedback loop [23] . Beta oscillations are obtained from the same network by parameter modification . Several methods have been proposed to generate beta oscillations [24] , [25] . Here , for the sake of simplicity , we modify the decay constants of the synapses . We show that the correlation as measured by the Spearman rank correlation coefficient depends on the phase relation in the gamma band . This result confirms the experimental finding of [17] . Secondly , after applying TE to measure the information exchange between two pools , we find that TE very similarly depends on the phase shift , i . e . , that there is an optimal phase relation where the TE is maximal . Thirdly , we reveal such dependence also in the beta band . Fourthly , we demonstrate that the TE increases as a function of the power in the gamma band . Lastly , we show that the information exchange gets faster if the gamma band synchronization increases . In sum , we provide support for the CTC hypothesis and make the prediction that CTC is a general mechanism , not restricted to the gamma band .
Womelsdorf et al . [17] analyzed four different data sets . The first data set consisted of measures from awake cats in area 17 [26] , the second from awake cats in areas 18 and 21a [4] , the third from awake monkeys in area V1 and the fourth from awake monkeys in area V4 [3] , [7] . In all four data sets they recorded multi unit activity simultaneously from 4 to 8 electrodes . For each pair of neuronal groups , they quantified the synchronization by MUA-MUA phase coherence spectrum , which showed a peak in the gamma frequency band . These authors then calculated the Spearman rank correlation coefficient between the two MUAs' 60 Hz power . They found that the fluctuations of the 60 Hz power were most strongly correlated when the 60 Hz phase relation was close to its mean , as illustrated in Fig . 1 . From this they concluded that effective connectivity can be maximized or minimized through synchronization at a favorable or unfavorable phase relation . We use a model with leaky integrate-and-fire ( IF ) dynamics , following [27] . Each IF unit charges up to its stationary value as long as its membrane potential stays below a threshold . The membrane potential is given by: ( 1 ) is a membrane capacitance , a membrane leak conductance , a resting potential and is the total synaptic current flowing into the cell . When the membrane potential reaches the threshold potential , it sends out a spike to all connected neurons and resets its membrane potential to the reset potential . The circuit remains shunted for a refractory period . Synaptic currents are mediated by excitatory ( AMPA and NMDA ) and inhibitory ( GABA ) receptors . The total synaptic current is given by ( 2 ) The currents are defined as follows: ( 3 ) ( 4 ) ( 5 ) ( 6 ) denotes the receptor specific synaptic conductances , the fractions of open channels and the the synaptic weights . and are the reversal potentials of the excitatory and inhibitory neurons , respectively , is the number of neurons encoding the spontaneous activity in the cortex , and and are the numbers of excitatory and inhibitory neurons in the network . The sum in each expression runs over all neurons , summing their open channels , weighted by the synaptic weights that implement the connection strengths between neurons . The NMDA synaptic current is dependent on the membrane potential and controlled by the extracellular concentration of . The fractions of open channels are given by: ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) , and are the decay times and is the rise time for the corresponding synapses . AMPA has a very short decay time ( 2 ms ) while NMDA has a long one ( 100 ms ) and the GABA decay time lies in-between ( 10 ms ) . The rise times of AMPA and GABA currents are neglected , as they are typically very short ( <1 ms ) . The sums over represent a sum over spikes formulated as -peaks ( ) emitted by presynaptic neuron at time . All input is generated via a Poisson process . The equations are integrated using a fourth order Runge-Kutta method with a time step of 0 . 02 ms . The network is organized in pools . Neurons within a specific pool have stronger recurrent connections than neurons between the pools . The intention of this work is to study cortical neural interactions not limited to a specific brain area . However , as our simulations needed to be directly comparable to [17] , and have specific parameter sets , our network models two clusters of cortical neurons in visual cortex V4 . The network model consists of two parts ( Fig . 2 ) . In each part there are pools of excitatory and inhibitory neurons , with a total of 800 excitatory and 200 inhibitory neurons . The excitatory neurons are subdivided into a selective pool and a non-selective pool . The neurons in the selective pools ( S , S′ ) are the ones that receive input either from outside or from the connected selective pool . The non-selective neurons ( NS , NS′ ) simulate the surrounding brain areas . Each population of excitatory neurons is connected to a pool of inhibitory neurons ( I , I′ ) . This allows for generating oscillations in each population separately . The two parts of the network are connected via feedforward ( ) and feedback ( ) connections that project onto the selective pools . The external input ( ) is a Poisson spike train that projects to the selective pool ( S ) of the first part of the network . In addition to the recurrent connections , the network is exposed to an external current ( ) , modeled as a Poisson spike train of 800 neurons , firing at 3 Hz . This models the spontaneous activity observed in the cerebral cortex . The network is fully connected . Gamma oscillations in a network with excitatory and inhibitory neurons are generated through a pyramidal-interneuron feedback loop [23] , [28] . Pyramidal neurons excite interneurons and interneurons in turn send inhibition back on pyramidal cells . The population frequency is determined by the sum of excitatory and inhibitory lags . The recurrent excitatory connections tend to decrease the oscillation frequency ( as compared to only excitatory-inhibitory and inhibitory-excitatory connections ) as they tend to prolong the positive phase in each cycle . In our network we can therefore generate and control the oscillations in the gamma frequency band by adjusting the AMPA and NMDA conductances . For example , increasing the and decreasing shifts the balance in the network towards fast excitation ( AMPA ) and slow inhibition ( GABA ) and thus increases the gamma frequency band oscillations . The conductances in our network are varied according to the following rule: and . Throughout the paper , we will refer to the parameter as the modification ratio . The factor 10 stems from the fact that near the firing threshold , the ratio of NMDA∶AMPA components becomes 10 in terms of charge entry , as stated in [27] . Therefore , in order not to change the spontaneous state , a decrease in is compensated by a tenfold increase in . All recurrent conductances ( both inhibitory and excitatory ) are changed according to these rules . By adjusting the synaptic decay constants , the oscillation frequency can be shifted into the beta band . The crucial parameter is . An increase of slows down the rhythm of the pyramidal-interneuronal loop and will therefore yield an oscillation at a lower frequency . To generate oscillations in the beta range ( around 20 Hz ) , we use ms and ms . To generate phase shifts in the gamma oscillations between the two parts of the network , we introduce a delay . The delay is set bidirectionally in the feedforward and feedback connections of the selective pools . Each spike emitted in arrives at after and vice versa . This lag in spike transmission generates a phase lag in the oscillations . A delay of , e . g . , 4 ms yields a phase shift of about 90 in a 60 Hz oscillation . The actual value of the mean phase shift is not crucial to the obtained results . All trials are initiated with a period of 400 ms in which no stimulus is presented , followed by a period of 5500 ms composed of the presentation of the stimulus , followed by 100 ms in which no stimulus is present . Each simulation consists of 100 trials . All parameter values are listed in Table 1 .
Raster plots for 20 neurons from each neuronal pool are shown in ( Fig . 3 ) . The power spectrum of the MUA in our network shows a clear peak in the gamma band ( Fig . 4 ) , in accordance with the experimental results . Therefore , the introduced delay sets the phase shift for oscillations in the gamma band . The delay , however , sets only the mean phase shift , but the phase shifts fluctuate over time . Thus , even for a fixed delay they show a broad distribution around this mean phase shift . This distribution is shown in Fig . 5a . The mean phase in this specific simulation is 91 . 4° . This , however , is just an example , as the mean phase shift in the simulations can be set to any value by adjusting the delays accordingly . The phases are similarly widely distributed as in the experimental results by [17] , shown in Fig . 5b . The phase shifts at 60 Hz between the two pools show a broad range of phases . We determine the phase shift in each time window of 500 ms . Then we calculate the correlation between the two pools for this time window by calculating the Spearman rank coefficient for the 60 Hz power in the two pools . The obtained correlation can now be sorted into different bins for the different phase shifts . We find that the correlation of the gamma band power between the two pools depends on the mean phase shift in the gamma band . Fig . 6 shows the rank correlation plotted against the phase shifts . The correlation is highest for the bin containing the mean phase shift and drops as it moves away from the mean . This confirms the experimental results of [17] . We apply TE to the same data as in the previous section . However , we measure the TE between the MUA in the two pools and not only the spectral power at 60 Hz , as was done in the experiment . We find that the TE depends strongly on the phase relation in the gamma band between the spiking activities of the two groups of neurons . It is highest for the mean phase between the two signals and drops as it moves away from the mean . This is consistent with our results for correlation . The phase dependence is illustrated in Fig . 7 . TE is plotted as a function of the mean phase shift . The solid line represents TE from the first to the second pool ( forward ) and the dashed line TE from the second to the first one ( backward ) . Forward TE is stronger than backward TE , implying that TE correctly detects the causal dependence of the second neuronal pool on the first one . Forward TE is stronger than backward TE even if the feedforward and feedback connections are symmetrical ( not shown ) . The stronger the feedforward and the weaker the feedback connections , the bigger the difference in the TE for the two directions , as shown in Fig . 8 . We plot the relative difference in the TE , calculated as . The feedback/feedforward ratio is defined as . We use a feedback/feedforward ratio of in the baseline simulations . To make sure the phase dependence is not only a by-product of changes in spectral power , we sorted the trials according to their spectral power in the gamma frequency band and calculated the phase dependence both for trials with power below and above the median . In both cases , the phase dependence is very similar ( not shown ) . Another result we obtain is that the phase dependence of information transport is not restricted to the gamma band . We find that even in simulations with a network oscillating strongly in the beta band ( around 20 Hz ) , the TE is again highest for the mean phase shift . In Fig . 9 , we compare the results for networks oscillating in the beta and gamma frequency band . Fig . 9a shows the TE for a network oscillating in the gamma band . The trials are sorted according to their phase relation in the gamma band . Fig . 9b shows the same network but with the trials now sorted according to their phase relation in the beta band . The phase dependence curve becomes a lot flatter and the optimal phase for maximal TE is much less pronounced . Fig . 9c shows the TE for a network oscillating in the beta band with trials sorted according to the phase relation in the gamma band . And Fig . 9d shows the TE for a network oscillating in the beta band with trials sorted according to the phase relation in the beta band . It becomes clear that it is the phase of the dominating frequency band that is responsible for high of low TE . We therefore conclude that it is not only the gamma band that has the ability to shape effective network connections via the phase , but that it is a general mechanism , observable in different frequency bands . We further find that TE depends on the spectral power in the gamma band ( 30–85 Hz ) . For a fixed parameter set , we first sort all the trials according to their power in the gamma band into bins . In each of these bins , we measure the TE for the mean phase relation . The TE as a function of the power in the gamma band is plotted in Fig . 10 . We find that the TE increases as a function of power . Note , however , that instead of sorting the trials according to their gamma band power for a fixed parameter set , we can also vary the parameters in the network . This allows us to vary the power over a wider range and the effect becomes clearer ( see the next section ) . In the previous section we have shown how TE depends on power in the gamma band for a fixed parameter set . Now we explicitly vary the amount of gamma power and study the TE dependence . Gamma band oscillations in a network of excitatory and inhibitory integrate-and-fire neurons appear when excitation is faster then inhibition [28] . Thus , we made the network oscillate by increasing AMPA conductance and decrease NMDA conductance . This change was applied to both excitatory and inhibitory neurons . In our simulations , we vary the / modification ratio from 0 to 0 . 12 , which results in a gamma band that contains from 10 to 65% of power . If we sort the data according to its shift as in the previous section , we find that , for the different / modification ratios , the TE shows a similar dependence on the phases . However , if the / modification ratio is too low , the phase measurement is not reliable any more and the curve gets flat , consistent with the case of random phase distribution . Fig . 11 shows the TE as a function of phase shift for several different / modification ratios . To summarize this result , we take the TE at the mean phase shift and plot it against the / modification ratio . As the spectral power in the gamma band increases from 10 to 65% , the TE increases from 0 . 38 to 0 . 65 and thus shows strong positive correlation with the level of gamma band power ( Fig . 12 ) . In other words , the higher the gamma band synchronization between the two pools , the higher the information throughput . This result suggests that gamma band oscillations improve the signal processing in a network of IF neurons , as they increase the amount of transmitted information . This in turn confirms the idea that gamma band synchronization can shape effective networks , especially as it can influence the information transmission in a given direction , as shown above . Finally , we are interested in whether the gamma band oscillations also have an influence on the speed of information exchange , on top of the increased amount of information exchange . To do this , we measure the time required until the stimulus presentation to the first pool leads to an increase in TE towards the second pool . We find that the onset of TE increase is significantly earlier when there is a lot of power in the gamma band . While for a / modification ratio of 0 . 02 it requires 28 ms to reach 50% of the average TE , for a / modification ratio of 0 . 12 it takes only 17 ms . The onset of information flow is clearly faster for higher levels of gamma band power ( Fig . 13 ) . This increase in speed is a further demonstration of how gamma oscillations increase network performance and shows how a network can be made more competitive .
It has been hypothesized that interactions among neuronal groups depend on neuronal synchronization . Recent results show that gamma band oscillations and especially the phase relation in the gamma band can modify the strength of correlations in a network and therefore influence the effectiveness of connections in it [17] . These effects could be used as a mechanism to connect and disconnect areas in a network without altering the physical connections . Here , using a model network of IF neurons , we intend to test this hypothesis . We demonstrate that also in a model network , the correlation between two areas depends on the phase shift in the gamma band between these two areas . Our modeling approach enables us to use an information theoretical measure , as it allows us to generate as much data as needed for such a measure . Thus , we use transfer entropy , which has the advantage of being able to distinguish driving and responding elements in a network . We show that also for TE there is an optimal phase shift between two neuronal groups , where TE is highest . We study this phase dependence in different bands ( beta and gamma ) . Our results demonstrate that , in a network with strong beta oscillations , TE depends on the phase shift in the beta band similarly to the way TE depends on the phase shift in the gamma band in a network with strong gamma oscillations . The ability to shape network connections seems therefore not to depend on the frequency range and seems to be a general mechanism . This confirms recent experimental results that have pointed out the importance of beta synchrony in functional integration [32] , [33] . We also study how TE depends on power in a specific frequency band . We do this here for the gamma band . For a fix set of parameters , we sort the trials in a simulation according to their power in the gamma band . We find that within a simulation , the trials with high gamma power have a high TE . Then we modify the parameters and vary the gamma power over a wider range . Again , we find that TE increases as a function of gamma power . Finally , we reveal that it is not only the amount of exchanged information that increases but also the speed: The higher the power in the gamma band , the earlier the onset of the information flow . Our results support the CTC hypothesis . If the effective connections in a network are to be influenced by the phase lock in a specific frequency band between two areas , it is important that it not only affects the coherence between them , but also the throughput of information in a specific direction . By measuring TE instead of the Spearman rank coefficient , we extend the work of [17] . Our result is also more general , as we use the rates to measure TE and not only the 60 Hz power . Our study of different frequency bands is a further extension . We provide evidence that the CTC mechanism is not restricted to the gamma band , but also functions in different frequency bands . In addition , our modeling approach also enables us to study how the information transport depends on the total power in a specific frequency band . Our finding that TE increases as a function of power suggests that both the phase and the power in a specific frequency band are important to shape effective connections in a network . The phase dependence of information transmission is not only a byproduct of the power dependence , as we find the same phase dependence both in trials with high gamma power as well as in trials with low gamma power . Furthermore , we demonstrate that the onset of information exchange depends on the power , which contributes to effectively shaping the connections in a network . We have already shown in the context of attention that gamma power increases the network effect of an attentional bias and that it makes the network more efficient [34] . Here , we can confirm this finding and put it in a more general context , independent of attention . As we are modeling results from visual cortical areas , we can assume that the neuronal clusters in the model transmit largely visual information . Several recent studies have contributed to the understanding of visual information transmission . These studies suggest that LFP power gradually increases as a function of stimulus contrast and gamma band LFP power increases differentially , that is , to a higher extent with respect to the baseline than relative to either higher or lower bands [35] . For the highest stimulus contrast , these authors report a clear peak in the gamma frequency band . In other words , the contrast dependence of the LFP is different in different frequency bands and the LFP power spectrum changes shape depending on contrast , with a peak in the gamma band emerging at high contrast . [36] studied the encoding of naturalistic sensory stimuli in LFPs and spikes . They found that the most informative LFP frequency ranges were 1–8 Hz and 60–100 Hz . They showed that the LFP in the 60–100 Hz high gamma band showed little noise correlation during visual stimulation but showed the highest observed signal correlation across all LFP frequencies . The high gamma band also had the highest proportional power increase during visual stimulation . These experimental results are supported by the modeling work of [37] . These authors showed that their modeling network encoded static input spike rates into gamma-range oscillations generated by inhibitory-excitatory neural interactions . In sum , these reports indicate that the gamma frequency range is the one used most frequently to encode visual information in the visual cortex and that visual information is encoded by the power of gamma range oscillations . These observations , together with our result that gamma oscillations increase both the overall amount and the speed of information indicates that information about preferred stimuli is treated preferentially and , in consequence , that cortical modules mostly exchange information about their preferred stimuli . In sum , we provide results to support the CTC hypothesis , we show evidence that CTC is a general mechanism independent of a specific frequency band and show that not only the phase but also the power is important to effectively shape the flow of information in a network . | Different brain areas are involved in any cognitive task . This implies that information has to be transmitted between different brain areas . Recent experimental results suggest that synchronization plays a crucial role in information exchange between cortical areas . They show that synchronization is capable of rendering network connections effective or ineffective . We study this hypothesis using a neurodynamical model and present results suggesting that both phase and strength of neuronal oscillations in the gamma frequency band influence amount and speed of information transport . We conclude that neuronal synchronization is crucial for information transmission and therefore might even have behavioral relevance . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"neuroscience/neuronal",
"signaling",
"mechanisms",
"neuroscience/theoretical",
"neuroscience"
] | 2010 | Optimal Information Transfer in the Cortex through Synchronization |
Epstein–Barr virus ( EBV ) , a ubiquitous human herpesvirus , can latently infect the human population . EBV is associated with several types of malignancies originating from lymphoid and epithelial cell types . EBV latent antigen 3C ( EBNA3C ) is essential for EBV-induced immortalization of B-cells . The Moloney murine leukemia provirus integration site ( PIM-1 ) , which encodes an oncogenic serine/threonine kinase , is linked to several cellular functions involving cell survival , proliferation , differentiation , and apoptosis . Notably , enhanced expression of Pim-1 kinase is associated with numerous hematological and non-hematological malignancies . A higher expression level of Pim-1 kinase is associated with EBV infection , suggesting a crucial role for Pim-1 in EBV-induced tumorigenesis . We now demonstrate a molecular mechanism which reveals a direct role for EBNA3C in enhancing Pim-1 expression in EBV-infected primary B-cells . We also showed that EBNA3C is physically associated with Pim-1 through its amino-terminal domain , and also forms a molecular complex in B-cells . EBNA3C can stabilize Pim-1 through abrogation of the proteasome/Ubiquitin pathway . Our results demonstrate that EBNA3C enhances Pim-1 mediated phosphorylation of p21 at the Thr145 residue . EBNA3C also facilitated the nuclear localization of Pim-1 , and promoted EBV transformed cell proliferation by altering Pim-1 mediated regulation of the activity of the cell-cycle inhibitor p21/WAF1 . Our study demonstrated that EBNA3C significantly induces Pim-1 mediated proteosomal degradation of p21 . A significant reduction in cell proliferation of EBV-transformed LCLs was observed upon stable knockdown of Pim-1 . This study describes a critical role for the oncoprotein Pim-1 in EBV-mediated oncogenesis , as well as provides novel insights into oncogenic kinase-targeted therapeutic intervention of EBV-associated cancers .
Epstein-Barr virus ( EBV ) , a ubiquitous lymphotropic herpesvirus , latently infects human populations worldwide [1] . EBV infection is typically asymptomatic and is an important etiological factor which contributes to different human malignancies [2] . EBV is consistently associated with nasopharyngeal carcinoma ( NPC ) [3] , African Burkitt's lymphoma ( BL ) [4] , post-transplantation lymphoproliferative disease ( PTLD ) [5] , Hodgkin's disease ( HD ) [6] , and AIDS-related non-Hodgkin's lymphomas ( AIDS-NHL ) [7] . Additionally , EBV is also found in a fraction of gastric carcinomas particularly in Asian and African countries [8] . EBV has the potential to transform human B-lymphocytes in vitro by maintaining a continuous proliferative state , known as “immortalization” which generates permanent lymphoblastoid cell lines ( LCLs ) [9] . The LCLs which are produced in culture carry the viral genome as extra-chromosomal episomes and express nine latent EBV proteins including , the six nuclear antigens ( EBNA 1 , 2 , 3A , 3B , 3C & LP ) , an additional three membrane associated proteins ( LMP1 , LMP2A & 2B ) , and the two EBV-encoded small RNAs ( EBERs ) [10] . These viral factors help to activate the quiescent B-cells from G0 into the cell cycle , and to sustain proliferation and maintenance of the viral genome [11] . Among the potential EBV latent antigens , EBNA3A , EBNA3B , and EBNA3C are sequentially encoded in the EBV genome and generate protein products of approximately 1 , 000 aa . Moreover , the EBNA3A , EBNA3B , and EBNA3C amino- terminal homologous domains are associated with RBP-Jk which mediates the association of EBNA2 and Notch with DNA [12] . EBNA3C and EBNA3A are also essential for EBV to drive primary human B-lymphocytes into continuously proliferating LCLs and for maintaining LCL growth [13] . Notably , Epstein-Barr virus nuclear antigen 3C ( EBNA3C ) plays an intricate regulatory role in the transcription of several viral and cellular genes [14] . EBNA3C targeted RBP-Jκ antagonizes EBNA2-mediated transactivation [15] , and cooperates with EBNA2 in activating the major viral LMP1 promoter [16] . EBNA3C was found to regulate chromatin remodeling by recruiting histone acetylase and deacetylase activities [17] . Moreover , EBNA3C modulates the transcriptional level of cellular genes which are involved in cell migration and invasion by targeting the metastasis suppressor Nm23-H1 [18] . In addition , EBNA3C can modulate diverse cellular functions , presumably mediated by direct protein–protein interactions [19] . EBNA3C also stabilizes c-Myc and interacts with Mdm2 to modulate p53 mediated transcription and apoptotic activities [20] , [21] . Interestingly , EBNA3C was found to be crucial for regulating the activity of cellular kinases . Recently , we have shown that EBNA3C enhances the kinase activity of cell-cycle regulatory protein Cyclin D1 which allows for subsequent ubiquitination and degradation of the tumor suppressor pRb [22] . Provirus integration site for Moloney murine leukemia virus ( Pim-1 ) , a proto-oncogene encoding a serine/threonine kinase , is linked to several cellular functions involving cell survival , proliferation , differentiation , and apoptosis [23] . It was reported that overexpression of Pim-1 is associated with the development and progression of multiple hematopoietic malignancies such as B-cell lymphomas , erythroleukemias , and acute myelogenous leukemia , T-cell lymphomas , and non-hematological malignancies including , oral squamous cell carcinoma , and prostate cancer [24] . During the process of embryo development , Pim-1 is highly expressed in liver , spleen and bone marrow in typical hematopoietic progenitors [25] , [26] , neonatal heart [27] , central nervous system [28] , and mammary gland [29] . Surprisingly , at the adult stage , Pim-1 is only slightly expressed in circulating granulocytes [26] . Previous reports also indicated that heterologous expression of Pim-1 in transgenic mice leads to increased lymphoproliferation and inhibition of apoptosis [30] . Augmented expression of Pim-1 in lymphoid cells by transgenesis highlighted its potential for oncogenesis [31] . Being a potent serine/threonine kinase , Pim-1 is able to phosphorylate itself [32] , [33] , through an autophosphorylation site that diverges from its consensus phosphorylation motif [34] . Several Pim-1 substrates have been identified , including p21Cip1/WAF1 [35] , [36] , Cdc25A [37] , PTPU2 [38] , NuMA [39] , C-TAK1 [40] , and Cdc25C [41] , indicating a crucial role for Pim-1 in cell proliferation through both the G1/S and G2/M phase transition . Pim-1 also possesses anti-apoptotic activity [42] , and recent reports have demonstrated a role for Pim kinases in regulation of herpesviral oncogenesis . KSHV encoded LANA was found to be crucial for transcriptional activation of Pim-1 in KSHV-positive cells and it also acts as a Pim-1 substrate [43] . In the context of EBV infection , studies have shown that Pim-1 may be required for LMP1-induced cell survival [44] . Furthermore , the expression levels of Pim-1 and Pim-2 are up-regulated upon EBV infection and they in turn enhance the activity of the viral nuclear antigen EBNA2 , suggesting a role in driving EBV-induced immortalization [45] . However , the molecular mechanism by which Pim-1 is activated through expression of viral antigens which creates a micro-environment for B-cell transformation is not fully elucidated . In our current study , we demonstrated that EBNA3C is responsible for inducing Pim-1 expression in EBV transformed B-cells as well as in EBV-infected PBMCs . Further , we showed that EBNA3C interacts with Pim-1 through a small N-terminal domain ( amino acids 130–159 ) and forms a complex in B-cells . Our results demonstrated that EBNA3C stabilized the Pim-1 protein by inhibiting its degradation by the ubiquitin/proteasome pathway . Interestingly , EBNA3C also facilitated the nuclear localization of Pim-1 , and promotes EBV-induced cell proliferation by regulating Pim-1 mediated degradation of p21/WAF1 . We observed that deregulation of p21 ultimately resulted in higher cellular proliferation . Lentivirus mediated stable knockdown of Pim-1 resulted in a significant reduction of EBV transformed cells and induction of apoptosis . Cumulatively , these findings demonstrate a vital role for Pim-1 in EBV-mediated oncogenesis and also support the conclusions that Pim-1 kinase is a potential target for therapeutic intervention strategies against EBV associated malignancies .
Pim-1 expression was found upregulated in different hematological and non-hematological malignancies [23] . To determine whether EBV latent antigen 3C modulates Pim-1 expression , 10 million human peripheral blood mononuclear cells ( PBMC ) were infected by wild type and mutant ΔEBNA3C BAC-GFP-EBV for 4 hrs at 37°C described previously [46] . The mRNA and protein levels of Pim-1 were detected after 0 , 2 , 4 , 7 days of infection . Our results showed upregulation of both the transcript and protein levels of Pim-1 with wild type EBV infection ( Fig . 1A ) . Interestingly , infection with ΔEBNA3C BAC-GFP-EBV resulted in low Pim-1 expression at 2 days post-infection and returned to the levels seen for infected cells at 0 day post-infection ( Fig . 1B ) . The results indicated that Pim-1 expression was induced by wild-type EBV infection . Therefore , we wanted to determine the expression pattern of Pim-1 in EBV transformed Lymphoblastoid cells LCL1 , LCL2 , and EBNA3C stably expressing BJAB7 and BJAB10 cells when compared to EBV negative BJAB . Our results showed that Pim-1 expression was highly upregulated in LCL1 , LCL2 , BJAB7 and BJAB10 cells ( Fig . 1C ) . To investigate the role of EBNA3C on Pim-1 , we monitored the Pim-1 protein expression levels with a dose dependent increase of EBNA3C in EBV negative DG75 as well as in HEK-293 cells . The results showed a steady increase in Pim-1 expression levels in both cell lines ( Fig . 1D and 1E ) . Moreover , Real-time PCR analysis showed upregulation of Pim-1 mRNA expression in BJAB7 and LCL1 cells when compared to EBV negative BJAB cells ( Fig . 1F , left panel ) . To further investigate the role of EBNA3C in inducing Pim-1 expression , we performed Real-time PCR as well as Western blot analysis on EBNA3C stable knock-down LCL1 cells . The results demonstrated a substantial reduction of Pim-1 expression in both mRNA and protein levels as compared with sh-control LCL1 cells ( Fig . 1F , right panel and Fig . 1G ) . Moreover , to check the role of other EBV antigens including EBNA2 , EBNA3A , and EBNA3B on Pim-1 expression , we performed si-RNA mediated knockdown of EBNA2 , EBNA3A , EBNA3B and EBNA3C in LCL1 cells . Our Real-time PCR analysis demonstrated that Pim-1 mRNA level is significantly reduced upon EBNA3C knockdown but no significant change was observed in Pim-1 mRNA expression level with EBNA2 , EBNA3A , EBNA3B knockdown further suggesting a major role for EBNA3C in upregulating Pim-1 expression ( Fig . S1A and S1B ) . Additionally , we performed Western blot analysis to determine whether knock down of EBNA3C may have an effect on other EBNAs expression levels . The results demonstrated that expression levels of other EBNAs were not affected with EBNA3C knockdown ( Fig . S2 ) . To determine whether EBNA3C interacted with Pim-1 , we performed co-immunoprecipitation experiments in HEK-293 cells by expressing Myc-tagged Pim-1 , Flag-EBNA3C , or Myc-EBNA3C . Immunoprecipitation was performed using A10 ( Fig . 2A ) or 9E10 antibody ( Fig . 2B ) . The results clearly demonstrated that EBNA3C strongly associated with Pim-1 ( Fig . 2A and B ) . We further supported our results by GST-pull down assays using EBV negative BJAB , EBNA3C expressing BJAB10 and EBV transformed LCL1 cell lysates incubated with bacterially purified GST-Pim-1 protein . EBNA3C was detected by A10 antibody [47] which showed a substantial level of association between Pim-1 and EBNA3C in the EBNA3C stable cell lines as well as in an LCL ( Fig . 2C ) . Coomassie staining of bacterially purified GST and GST-Pim-1 proteins are shown in Fig . 2D . We also observed the association between EBNA3C and Pim-1 in BJAB7 , BJAB10 , LCL1 , LCL2 cells compared with BJAB in separate co-immunoprecipitation experiments by using Pim-1 specific antibody ( Fig . 2E and 2F ) . To determine the specific domain of EBNA3C associated with Pim-1 , we performed co-immunoprecipitation experiments expressing GFP-tagged Pim-1 with Myc-tagged full length ( residues 1–992 ) and different truncated mutants ( residues 1–365 , 366–620 and 621–992 ) of EBNA3C in HEK-293 cells . Immunoprecipitation ( IP ) was performed by using either 9E10 or GFP-specific antibodies . The results indicated that Pim-1 strongly associated with full length as well as the N-terminal domain ( residues 1–365 ) of EBNA3C ( Fig . 3A and 3B ) . We extended the binding experiments by performing in vitro GST-pulldown assay with in vitro translated full length and truncated mutants of EBNA3C including fragments within the N-terminal domain ( residues 1–992 , 1–365 , 366–620 , 621–992 , 1–100 , 100–200 , 200–300 , 366–992 , 1–129 , 1–159 , 1–250 , 130–300 ) . Our results demonstrated that EBNA3C residues 100–200 , 1–159 , 1–250 , 130–300 associated strongly with full length Pim-1 ( Fig . 3C ) . To further map the specific binding residues , we performed additional in vitro GST-pulldown assays by using in vitro translated full length Pim-1 incubated with bacterially expressed N-terminal truncated mutants of GST-EBNA3C fused to residues 90–129 , 130–159 , 130–190 , 160–190 . The results indicated that Pim-1 strongly bound to residues 130–159 of EBNA3C ( Fig . 3D , 3E and 3F ) . In the context of cancer progression , the significance of different subcellular localization patterns of Pim-1 has not been fully elucidated . Previous studies suggested that irradiation can promote nuclear translocation of Pim-1 in radio-resistant squamocellular malignancies of head and neck [48] . Importantly , nuclear localization of Pim-1 may correlate with the proliferating cells and may also contribute to a survival response upon pathologic injury [49] . In our study , we transfected Myc-tagged Pim-1 with or without GFP-tagged EBNA3C expression vectors in HEK-293 cells . Cellular localization of Pim-1 was examined by immunofluorescence analysis using specific antibodies against the Myc-epitope . Interestingly , our results showed that the localization of Pim-1 was predominantly in the cytoplasm without EBNA3C and was translocated to the nucleus in the presence of EBNA3C . Also , strong co-localization with Pim-1 and EBNA3C was observed ( Fig . 4A and 4C ) . To further validate these results , we performed nuclear and cytosolic fractionation assays using transiently transfected HEK-293 cells with Myc-tagged Pim-1 with or without Flag-tagged EBNA3C expression vectors . Our Western blot analysis with nuclear and cytosolic fractions showed that in the presence of EBNA3C , the level of Pim-1 substantially increased in the nuclear fraction ( Fig . 4B ) . Moreover , we corroborated the above observations in EBV negative BJAB , EBNA3C stably expressing BJAB10 and EBV transformed LCL1 cells using specific antibodies against Pim-1 and EBNA3C . The results showed that Pim-1 was mostly localized in the nucleus in both EBNA3C expressing BJAB10 and EBV transformed LCL1 cells , but was almost entirely cytoplasmic in EBV negative BJAB cells ( Fig . 4D ) . Recent reports suggested that expression of EBNA3C is responsible for the stabilization of different oncoproteins , transcription factors and cellular kinases [19] , [22] , [50] , [51] , and also plays an important role in modulating the ubiquitin ( Ub ) -proteasome machinery [52] . Our results so far showed that EBNA3C is important for enhanced protein expression of Pim-1 . To determine , if this induced expression is related to EBNA3C-mediated stabilization of Pim-1 by the inhibition of Ub-proteosome machinery , we co-transfected Myc-tagged Pim-1 with or without Flag-tagged EBNA3C expression plasmids in HEK-293 cells which were treated with or without the proteasome inhibitor MG132 . The results showed a substantial accumulation of Pim-1 protein levels in MG132 treated cells in the presence of EBNA3C compared with mock treatment and control vector ( Fig . 5A ) . Next , we performed the stability assay of Pim-1 by transfecting Myc-tagged Pim-1 with or without Flag-tagged EBNA3C in HEK-293 cells . After 36 hours of post-transfection , cells were treated with the protein synthesis inhibitor cyclohexamide and harvested at 0 , 3 , and 6 hours intervals . The Western blot results clearly demonstrated that Pim-1 levels were stabilized with co-expression of EBNA3C whereas , the Pim-1 expression levels were markedly reduced with cyclohexamide treatment by 3 to 6 hours in the absence of EBNA3C ( Fig . 5B ) . To further corroborate our results , we extended the stability assays with EBV negative BJAB , EBNA3C stably expressing BJAB10 and EBV transformed LCL1 , control vector transfected and EBNA3C stably knockdown LCL1 cells . As anticipated , our results showed that Pim-1 protein levels were stabilized in BJAB10 , LCL1 and sh-Ctrl LCL1 cells as well but significantly reduced in BJAB , sh-E3C LCL1 cells over time with the treatment of cyclohexamide ( Fig . 5C and 5D ) . The enhanced stability of Pim-1 in the presence of EBNA3C encouraged us to investigate the role of EBNA3C for regulating Pim-1 poly-ubiquitination . Therefore , we performed in vivo poly-ubiquitination assays in cells by co-transfecting with control vector , Myc-tagged Pim-1 , HA-Ubiquitin , with or without Flag-tagged EBNA3C in HEK-293 cells . The results demonstrated a significant reduction of Pim-1 poly-ubiquitination levels in the presence of EBNA3C ( Fig . 6A ) . To further validate the role of EBNA3C , we performed poly-ubiquitination assays by using the wild type Myc-tagged EBNA3C and its specific mutant EBNA3C ( Myc-EBNA3C-C143N ) expression vectors . We observed higher poly-ubiquitination levels of Pim-1 in the presence of the EBNA3C-C143N mutant compared with wild type ( Fig . 6B ) . We also performed the ubiquitination assays in a B-cell background by using EBV-negative BJAB , EBNA3C stably expressing BJAB10 and EBV transformed lymphoblastoid LCL1 , as well as the sh-Ctrl and sh-EBNA3C LCL1 cell lines . Our result showed that the status of Pim-1 ubiquitination was much lower in BJAB10 and LCL1 cells compared with BJAB ( Fig . 6C ) and somewhat enhanced upon EBNA3C knockdown ( Fig . 6D ) . The serine/threonine-protein kinase Pim-1 is upregulated in a number of hematological malignancies such as leukemia [26] , mantle-cell lymphoma [53] , and diffuse large B-cell lymphoma ( DLBCL ) [54] . A wide range of Pim-1 substrates were identified including , BAD [55] , NuMa [39] , Socs [56] , Cdc25A [37] , C-TAK1 [40] , NFATc [57] , HP-1 [58] , PAP-1 [59] , and cyclin-dependent kinase inhibitor p21 or p21Cip1/WAF1 [35] , which suggested that Pim-1 can function at different cellular events , such as cell proliferation , differentiation , and cell survival [60] . Earlier reports showed that p21 suppresses tumors by promoting cell cycle arrest in response to various stimuli . Furthermore , considerable evidence from biochemical and genetic studies have demonstrated that p21 can act as a master effector molecule of multiple tumor suppressor pathways for promoting anti-proliferative activities which are independent of classical p53 tumor suppressor pathway [61] . Studies have also shown that enhanced levels of Pim-1 kinase phosphorylates Thr145 residue , and regulates the activity of p21Cip1/WAF1 [35] . Therefore , we checked the kinase activity of Pim-1 towards its substrate p21 with or without EBNA3C to investigate whether EBNA3C can modulate the phosphorylation status of p21 . HEK-293 cells were transiently transfected with control vector , with and without Myc-tagged Pim-1 and increasing doses of Flag-tagged EBNA3C expression vectors . Immunoprecipitation was performed using anti-Myc 9E10 antibody and immunoprecipitated complexes were further examined for in vitro kinase activity as determined by GST-p21 phosphorylation . Interestingly , the results demonstrated that the ability of Pim-1 kinase to phosphorylate p21 was substantially and proportionally augmented by a dose-dependent increase in EBNA3C expression ( Fig . 7A ) . We further extended the kinase assay using a kinase-dead ( KD ) mutant of Pim-1 ( Fig . 7B ) . As anticipated , there was no kinase activity observed with the kinase-dead ( KD ) mutant of Pim-1 when compared with wild type . Next , we performed in vitro kinase assay for Pim-1 in the presence or absence of EBNA3C by using wild type and mutant ( T145A ) GST-p21 as substrate . The results showed no phosphorylation with mutant ( T145A ) p21 in comparison with wild-type , even in the presence of EBNA3C ( Fig . 7C ) . This suggested that the Thr145 residue is important for EBNA3C mediated enhancement of p21 phosphorylation by Pim-1 kinase . Earlier reports suggested the potential of a complex containing Pim-1 and p21 in cells [36] . We have now confirmed a strong association between Pim-1 and EBNA3C above . We then performed competitive binding assays in HEK-293 cells by co-transfecting increasing doses of EBNA3C-expression construct and a constant amount of Myc-tagged Pim-1 and Flag-tagged p21 . Immunoprecipitation ( IP ) was performed with anti-Myc antibody for immunoprecipitation of complex with Pim-1 . Our results demonstrated that increasing doses of EBNA3C can result in reduced association between Pim-1 and p21 ( Fig . 8A ) . Previous studies showed that p21 is a prime target for ubiquitination in gliomas [62] , and was dependent on the ubiquitin ligase APC/CCdc20 for its proteolytic degradation by the proteasome [63] . To explore the modulation of p21 protein levels by EBNA3C through regulation of the Ub-proteasome machinery , HEK-293 cells were co-transfected with Myc-Pim-1 , Flag-p21 , and increasing amounts of untagged-EBNA3C then treated with the proteasome inhibitor , MG132 . The results indicated that the level of p21 was significantly reduced in the mock treated cells . However , with MG132 drug treatment , the level of p21 was further enhanced in the presence of EBNA3C ( Fig . 8B ) . Previous reports suggested that p21 regulates fundamental cellular processes , including cell cycle progression , apoptosis , and transcription on DNA damage response [64] , [65] . Interestingly , involvement of p21 in all these major signaling pathways may occur not only after DNA damage response , but also depends on physiological conditions [66] , [67] . To determine whether EBNA3C alone or an EBNA3C/Pim-1 complex had a role in p21 stabilization in DNA damage response , we performed stability assays using cyclohexamide treated HEK-293 cells co-transfected with different combinations of untagged-EBNA3C , Myc-Pim-1 , Myc-Pim-1 KD ( kinase dead ) mutant , and Flag-p21 expression constructs . The experiments were performed with or without DNA damage response signal ( reduction of serum with etoposide drug treatment ) . Interestingly , the results demonstrated that p21 expression levels were substantially reduced with co-expression of wild type Pim-1 and EBNA3C . However , p21 expression levels remained unchanged with EBNA3C , wild type Pim-1 or kinase dead Pim-1 alone with or without DNA damage ( Fig . 9A , upper and lower panels ) . Therefore , EBNA3C contributes to the process of p21 degradation in co-operation with wild type Pim-1 . We also extended our stability assays in EBV negative BJAB , EBNA3C stably expressing BJAB10 , EBV transformed lymphoblastoid LCL1 , sh-Ctrl and sh-EBNA3C LCL1 cells with cyclohexamide treatment in the presence or absence of etoposide induced DNA damage . P21 protein expression was found significantly reduced in LCL1 , BJAB10 compared with BJAB even both with or without DNA damage response ( Fig . 9B , upper and lower panels ) . Importantly , the expression levels were found augmented with or without DNA damage in EBNA3C stable knockdown LCL1 cells ( Fig . 9C , upper and lower panels ) suggesting a role for EBNA3C in deregulating p21 stability independent of etoposide induced DNA damage response . To examine , whether EBNA3C has a vital role in p21 degradation alone or in collaboration with Pim-1 , we performed poly-ubiquitination assays by expressing Flag-p21 and Myc-tagged EBNA3C in HEK-293 cells . The results demonstrated there was no significant change in the level of poly-ubiquitination ( Fig . 10A ) . Our study also revealed a strong association with p21 and EBNA3C by co-immunoprecipitation experiments ( Fig . S3A ) . Next , we attempted to examine the potential changes in p21 protein levels by expressing Flag-p21 , Myc-Pim-1 , along with increasing amounts of EBNA3C in HEK-293 cells . Interestingly , we observed reduced levels of p21 with a dose dependent increase of EBNA3C in the presence of Pim-1 ( Fig . S3B ) . Our poly-ubiquitination assay results for p21 , with wild-type Pim-1 and kinase-dead Pim-1 mutant clearly showed that the level of poly-ubiquitination was much higher with wild-type Pim-1 compared with kinase-dead mutant in the presence of EBNA3C ( Fig . 10B ) . This supported an important role for EBNA3C in enhancing Pim-1 kinase activity and is likely to be required for p21 degradation . Moreover , we extended the poly-ubiquitination assays using the P21T145A mutant to determine whether the p21 Thr145 phosphorylation was related to its degradation . The results showed that levels of poly-ubiquitination remained unchanged both with wild-type and the kinase-dead mutant of Pim-1 in the presence of EBNA3C ( Fig . 10C ) . Additionally , we performed poly-ubiquitination assay using EBV negative BJAB , EBNA3C stably expressing BJAB10 , EBV transformed lymphoblastoid LCL1 , as well as sh-Ctrl and sh-EBNA3C LCL1 cells to monitor the poly-ubiquitination status of p21 . The results clearly indicated higher poly-ubiquitinated levels of p21 in EBNA3C expressing BJAB10 , and LCL1 cells compared with EBNA3C negative BJAB ( Fig . 10D ) . The levels were also reduced in EBNA3C stable knockdown LCL1 cells ( Fig . 10E ) . To determine if the degradation of p21 is Pim-1 dependent , we performed poly-ubiquitination assays with Pim-1 stable knockdown LCL1 cells . The results indicated that upon Pim-1 knockdown , the level of p21 degradation was reduced ( Fig . 10F ) . Earlier reports demonstrated that Pim-1 kinase activity is linked to enhanced cellular proliferation in neoplastic cell types [68] . To determine the effect of EBNA3C on Pim-1 mediated cell proliferation , HEK-293 cells were transfected with control vector , Flag-tagged EBNA3C , Myc-Pim-1 expression vector , and Myc-Pim-1 with Flag-EBNA3C . Colony formation assays were performed after G418 selection for 2 weeks . The results demonstrated a significant increase in the colony numbers in EBNA3C and the Pim-1 co-transfected set compared with control vector or only Pim-1 transfected sets ( Fig . S4A and S4B ) . Additionally , cell proliferation assays were performed by cell counting using Trypan blue dye exclusion technique up to 6 days ( Fig . S4C ) . Previous studies suggested that Pim-1 expression accelerated the process of lymphoproliferation and inhibits apoptosis [30] . Also , depletion of Pim-1 by RNA interference in mouse and human prostate cancer cells reduced cellular proliferation and survival [69] . To validate these studies , we used Lentivirus mediated delivery of sh-RNA vectors to knock down Pim-1 in LCL1 cells . Wild type LCL1 , puromycin selected stable Ctrl-vector and Pim-1 knocked down cells with GFP fluorescence were monitored ( Fig . 11A ) . Also , the expression levels of Pim-1 in different clones were examined by performing Western blot analysis ( Fig . 11B ) . In order to determine whether Pim-1 knockdown in an LCL background has some implications in apoptotic cell death , we performed apoptosis assays using stable sh-Ctrl LCL1 , sh-Pim-1 LCL1 cells with or without serum starvation . Cells were stained with Propidium iodide for FACS analysis . The results showed substantial increase in apoptotic cell death in stable Pim-1 knockdown LCL1 with serum starvation ( Fig . 11C , D ) . Programmed cell death or apoptosis is considered as a major regulator of cellular growth control and tissue homeostasis [70] . Previous reports suggested that caspases activation can be triggered through the induction of the extrinsic apoptotic pathway or at the mitochondria by stimulating the intrinsic apoptotic pathway in response with anticancer chemotherapy [71] . In order to determine whether the inhibition of Pim-1 had some effect on apoptotic event in LCLs , we performed Western blot analysis to monitor the levels of PARP-1 cleavage . Our result showed that Pim-1 knock-down EBV transformed cells showed higher signals for the PARP-1 cleavage ( Fig . 11E ) . Moreover , we detected higher expression levels of Caspase-3 , Caspase-9 , and Apaf-1 in Pim-1 stable knockdown LCL1 in comparison with sh-Ctrl LCL1 cells which indicates that Pim-1 knockdown induced the intrinsic apoptotic pathway in EBV transformed cells ( Fig . 11F ) . We performed cell proliferation assays in the context of Pim-1 knock-down . Interestingly , the result showed that the rate of proliferation of Pim-1 stable knock-down LCL1 cells was lower compared with LCL1 and sh-Ctrl LCL1 cells ( Fig . 11G ) . As an inhibitor of cyclin-dependent kinases , p21Waf1/Cip1 is required for proper cell-cycle progression [72] . Earlier reports suggested that p21 suppresses tumors by promoting cell cycle arrest in response to various stimuli [61] . In addition , substantial evidence from biochemical and genetic studies shows that p21 acts as a potential effector of multiple tumor suppressor pathways to promote its anti-proliferative activities independent of p53 [61] . Interestingly , several studies demonstrated that ubiquitin-mediated degradation of p21 can also promote cancer cell proliferation [73] . Our results above indicated that p21 is targeted by EBNA3C through Pim-1 dependent degradation . We next attempted to examine whether EBNA3C has a role in modulating p21-mediated inhibition of cell proliferation involving Pim-1 kinase with DNA damage response . HEK-293 and MEF cells were transfected with different combinations of Flag-tagged p21 ( wild type and the T145A mutant ) , Myc-Pim-1 ( wild type and the kinase dead mutant ) , EBNA3C expression vectors . Cell proliferation assays were performed without serum and with etoposide treatment after 2 weeks of G418 antibiotic selection . The results demonstrated that EBNA3C together with wild type Pim-1 effectively reduced the growth suppressive effect of p21 . The cell proliferation rate in p21 expressing cells either with EBNA3C or wild type Pim-1 was shown to be enhanced compared to control vector alone . Interestingly , the lower rate of cell proliferation was observed with kinase dead Pim-1 mutant or P21T145A mutant even in the presence of EBNA3C ( Fig . 12A , B ) . To check the expression levels of these proteins , we performed Western blot analysis with these G418 selected cells ( Fig . S5A and S5B ) . Moreover , our immunofluorescence studies for BrdU incorporation with DNA damage showed an increased number of BrdU foci with wild type Pim-1 and p21 , co-expressed cells with EBNA3C ( Fig . 12C and 12D ) . To determine the possible contribution of different molecules which are involved with intrinsic apoptotic signaling in the context of Pim-1 mediated p21 downregulation in the presence of EBNA3C , we checked the protein expression profiles of Caspase-3 , Caspase-9 , Apaf-1 , and Bcl2 in sh-Ctrl-vector transfected and Pim-1 stable knockdown EBV negative Ramos cells . These cells were transfected with p21 and an increasing dose of EBNA3C . Our results demonstrated that the expression levels of Caspase-3 , Caspase-9 , Apaf-1 were unchanged with P21 transfection in Pim-1 knockdown Ramos cells , in the presence of EBNA3C compared with sh-Ctrl-Ramos cells where the expression of these proteins were reduced ( Fig . 12E , compare left and right panels ) . Interestingly , Bcl2 levels were found to be upregulated in sh-Ctrl cells ( Fig . 12E ) . These results support our hypothesis that EBNA3C can potentiate Pim-1 kinase activities for inhibiting cell growth suppressive property of p21 which occurs through the intrinsic apoptotic pathway .
Pim-1 was identified in murine leukaemia virus ( MuLV ) -induced lymphomas that frequently contains proviral insertions which were associated with the transcriptional activation of the Pim-1 gene frequently associated with enhanced tumorigenesis [74] . Overexpression of Pim kinases have been found in various lymphomas and leukemias [75] . Different reports have suggested a role for Pim-1 kinase in progression of Burkitt's lymphoma [76] , primary cutaneous large B-cell lymphoma [77] and prostate cancer [78] . Pim-1 kinase also performs multiple cellular functions related to cell survival , proliferation , differentiation , apoptosis , and progression of tumors [79] . Previous studies showed upregulation of Pim kinases during Epstein-Barr virus infection [45] . Epstein–Barr virus ( EBV ) was found potentially involved in the pathogenesis of different B-cell lymphoproliferative disease and all three EBV nuclear antigen 3 proteins can manipulate the expression of a wide range of cellular genes and they often act co-operatively to induce epigenetic chromatin modifications [80] . Another report demonstrated that the involvement of EBNA3A and EBNA3C expression with polycomb complexes for the covalent K27me3 modification of histone H3 at the p16INK4A promoter to repress the transcription [81] . Also , BIM expression was regulated in latently infected EBV cells through epigenetic modification and CpG methylation [11] . EBNA3C , regulates transcription of a wide range of viral and cellular genes [14] . EBNA3C was found to be associated with Nm23-H1 to regulate the transcription process of cellular genes which are critically involved in cell migration and invasion [82] . Recent reports also demonstrated that EBNA3C can physically interact and stabilize different host oncoproteins , including c-Myc and IRF4 [20] , [51] , and has a major role in regulation of the cell cycle regulatory protein complex Cyclin D1/CDK6 to drive B-cell malignancies [22] . Interestingly , some reports showed that Pim-1 levels are tightly controlled at many steps from the transcriptional to translational levels [60] . Our study now demonstrated upregulation of Pim-1 expression at the mRNA and protein levels with EBV infection in primary B-cells as well as EBV positive cancer cell lines . Interestingly , infection with the ΔEBNA3C BAC-GFP-EBV showed a much lower Pim-1 expression at 2 days post-infection . However , the expression patterns remain unchanged at later time points . Our results from the primary infection studies suggested a major contributory role of EBNA3C in inducing Pim-1 expression . We also observed a substantial reduction in Pim-1 expression levels only after siRNA mediated knockdown of EBNA3C but not with the knockdown of EBNA2 , EBNA3A and EBNA3B which further confirmed a direct role of EBNA3C in regulating Pim-1 expression levels in EBV-transformed cells . Moreover , our studies showed that EBNA3C has a strong physical association with Pim-1 , and that Pim-1 binds to the N-terminal 130–159 residues of EBNA3C . Interestingly , several other studies from our Lab also demonstrated that this region of EBNA3C specifically interacts with different important cellular proteins such as cyclin A , p53 , E2F1 , c-Myc , IRF4/IRF8 etc [20] , [21] , [46] , [51] , [83] . Therefore , this 130–159 aa residues of EBNA-3C have particular significance in deregulating major cellular process in EBV-infected cells . Further detailed investigation is needed to evaluate the functional role of this domain in connection with EBNA3C mediated oncogenesis . Our co-immunoprecipitation experiments in EBNA3C expressing Burkitt's lymphoma cells and EBV transformed Lymphoblastoid cells also demonstrated that Pim-1 forms a strong molecular complex with EBNA3C in infected cells . Previous studies suggested that nuclear localization of Pim-1 is essential for the regulation of its cellular substrates as well as additional biological activities of this kinase [76] . Importantly , our co-localization studies showed that in the absence of EBNA3C , localization of Pim-1 was mostly in the cytoplasm and predominantly in the nucleus in the presence of EBNA3C . Our immunofluorescence assay therefore revealed a strong co-localization with Pim-1 and EBNA3C in the nucleus . Previously it was shown that the Hsp90 protein is responsible for correct folding and stabilization of Pim-1 [84] . Further , studies from our group demonstrated an important role of EBNA3C in stabilizing different oncoproteins such as Gemin3 , Cyclin D1 , and IRF4 [19] , [22] , [51] to deregulate normal cellular functions which can drive development of neoplastic events . Our study clearly demonstrated that Pim-1 protein stabilization by EBNA3C can result in increased levels of Pim-1 in EBV infected cells . Additionally , the stability of the Pim-1 kinase is largely regulated by the ubiquitin/proteasome pathway [85] . Several reports suggested the important role of EBNA3C for deregulating the functions of different cellular proteins by manipulation of ubiquitin/proteasome pathways [86] . Our Lab previously demonstrated the interaction between EBNA3C with SCFSkp2 E3 ligase complex [87] . Also , the N-terminal domain of EBNA3C physically associated with the acidic domain of Mdm2 which is a known E3 ubiquitin-protein ligase [50] . Other studies also suggested that EBNA3C associates with the α-subunit of the 20S proteasome and is degraded in-vitro by purified 20S proteasomes [88] . EBNA3C was found to facilitate the degradation of E2F1 by targeting ubiquitin-proteasome pathways [46] . Recently , we have shown that EBNA3C deregulates total H2AX levels through involvement of the ubiquitin/proteasome degradation pathway [89] . Interestingly , other reports suggested the potential involvement of Pim-1 with ubiquitin/proteasome pathways as enhanced expression of Pim-1 increases the level of SCFSkp2 ubiquitin ligase through the direct binding and phosphorylation of multiple sites on this protein [90] . A previous study showed the role of heat shock proteins and the ubiquitin-proteasome pathway for regulating the stability of Pim-1 kinase [85] . Our poly-ubiquitination experiments clearly suggested that Pim-1 poly-ubiquitination was significantly inhibited by EBNA3C and so resulted in increased Pim-1 levels . Since enhanced levels of Pim-1 is linked to different hematological or non-hematological malignancies , it reveals the intricate mechanisms that are linked to ubiquitin-proteasome-mediated degradation of Pim-1 and is important for designing therapeutic interventions . Additionally , this approach could enhance new therapeutic avenues by targeting Pim-1 kinase and so enhance the efficiency of conventional therapeutic strategies against EBV mediated oncogenesis . Being a potent serine/threonine kinase , Pim-1 plays important roles in a number of cellular events . Most notably , Pim-1 can synergize with c-Myc to drive the rapid progression of B-cell lymphomas [91] . This synergism is likely to originate from the anti-apoptotic activity promoted by Pim-1 [92] . Among other Pim-1 substrates , the Cyclin-dependent kinase inhibitor 1 or p21 is important in the context of viral pathogenesis . Interestingly , p21 stability has been exploited by different tumor viruses . A number of viral proteins can affect the post-transcriptional regulation of p21 , thereby affecting cellular proliferation . The human papilloma virus E6 proteins can downregulate p21 independently of p53 [93] . Also , the hepatitis C virus and K-cyclin encoded by the human herpesvirus 8 stimulates p21 phosphorylation at the Ser130 residue by CDK6 without affecting its stability [94] . These findings further establish that targeting p21 is likely to be a common strategy for viruses to regulate cell cycle progression and apoptosis . Previous reports also showed that EBV acts downstream of the p53 and appears to prevent the inactivation of cyclin-dependent kinase CDK2 by p21WAF1/CIP1 by targeting p21 for degradation by the proteasome pathway [95] . Basically , participation of p21 in multiple cellular functions emphasizes its importance and that its precise regulation is crucial for maintenance of the normal cellular function . Importantly , its phosphorylation and interaction with other cellular proteins are crucial to p21 stability at the post-translational level . Previous reports suggested that the Thr145 residue of p21 is preferentially phosphorylated by Pim-1 [36] . Our current study clearly demonstrated a role for EBNA3C in enhancing Pim-1 kinase activity to phosphorylate p21 . We also observed that Pim-1 was not able to phosphorylate mutant p21 ( T145A ) even in the presence of EBNA3C . Interestingly , we identified a molecular association between Pim-1 and p21 , with EBNA3C and our competitive binding assay demonstrated that increasing doses of EBNA3C resulted in reduced association between Pim-1 and p21 , causing in destabilization of p21 by enhancing its proteasome-mediated degradation independent of etoposide induced DNA damage response . Several reports indicated that Pim-1 expression is associated with cell proliferation and survival [96] . Pim-1 also induces anti-cancer drug resistance by inhibiting the intrinsic mitochondrial apoptosis pathway [97] . In our studies , siRNA mediated knock down of Pim-1 showed reduced proliferation of EBV transformed cells . Moreover , Pim-1 silencing potentially activated the intrinsic apoptotic signaling in EBV transformed cells . Recent studies showed that upregulation of p21 activated the intrinsic apoptotic pathway [98] . Our results also support this finding showing that EBNA3C induced Pim-1 mediated downregulation of p21 which is also related to the inhibition of intrinsic apoptotic pathway in EBV transformed cells . Recent evidence suggested a role for the RNF126 E3 ubiquitin ligase in promoting cancer cell proliferation by p21 degradation [73] . Our results strongly suggested an important role for EBNA3C to effectively inhibit the growth suppressive effects of p21 in the presence of Pim-1 . Interestingly , we observed a lower rate of cell proliferation with the kinase dead Pim-1 mutant or P21T145A mutant even in the presence of EBNA3C . This supports a role for Pim-1-mediated phosphorylation of the Thr145 residue of p21 in cell proliferation . In summary , our current work demonstrated an important molecular mechanism which revealed a direct role for the EBV latent antigen 3C in enhancing expression of the oncoprotein Pim-1 in EBV transformed B-cells as well as in EBV-infected PBMCs . We also showed the physical interaction between EBNA3C and Pim-1 and further mapped the binding to the Amino-terminal domain of EBNA3C . Moreover , our study demonstrated that EBNA3C mediated stabilization of Pim-1 through abrogation of the proteasome/ubiquitin pathway . EBNA3C also facilitated the nuclear export of Pim-1 and promoted EBV transformed cell proliferation by altering Pim-1-mediated regulation of the cell-cycle inhibitor p21/WAF1 activity . Our study now demonstrated that EBNA3C directly contributes to Pim-1 mediated phosphorylation of p21 which facilitates its proteosomal degradation . In addition , significant reduction of EBV transformed cell proliferation as well as a substantial induction of apoptotic cell death was also observed upon stable knockdown of Pim-1 . Our study now provides a novel insight into the precise role of oncogenic Pim-1 in EBV-mediated oncogenesis ( Fig . 13 ) . Moreover , siRNA mediated knockdown of Pim-1 triggers the intrinsic apoptotic signaling pathway in LCLs and repressed proteasome-mediated degradation of p21 . Pim-1 knockdown further demonstrated a vital role in EBV-mediated proliferation of B-cells by impeding the process of apoptosis . Our findings thus contribute to a more indepth understanding of the role of EBNA3C expressed in EBV-infected B-cells and its interaction with the critical cellular kinase which leads to EBV induced B-cell transformation .
PBMC were obtained from University of Pennsylvania Human Immunology Core ( HIC ) and donated by the healthy donors . This study was approved by University of Pennsylvania Human Immunology Core ( HIC ) which maintains University of Pennsylvania IRB protocol . In this IRB approved protocol the declarations of Helsinki protocols were followed and each donor gave written , informed consent . There is no link between donors and their information with this study . Full length and truncated mutants of GST , Myc , Flag , and GFP tagged EBNA3C expression vectors were described previously [22] , [51] . Myc-tagged EBNA3C with C143N point mutation was generated by using standard PCR primer mutagenesis method [50] . Constructs for Myc-tagged Pim-1 , kinase dead ( KD ) version of Pim-1 as Pim-1 K67M ( mutated at the ATP binding Pocket ) , pGEX2T-Pim-1 were mentioned previously [43] . Wild type pGEX-P21 construct was generated by using Flag-P21 construct as template . The PCR amplified insert was subjected for EcoRI/NotI restriction enzyme digestion and ligated into pGEX2T vector . pGEX-P21T145A and Flag-P21T145A constructs were cloned by using PBK/CMV/LacZ P21T145A ( kindly provided by Dr . Nancy Magnuson ) as a template for the PCR amplification . pCDNA3-HA-Ub construct was kindly provided by George Mosialos ( Aristotle University of Thessaloniki , Greece ) and pGIPZ was used as the sh-RNA vector described previously [51] . Constructs used for lentiviral packaging were previously described [99] . Antibodies of Pim-1 ( E-16 ) , Ub ( FL-76 ) , PARP-1 ( F-2 ) , and GFP ( I-16 ) , Caspase-3 ( E-8 ) , Caspase-9 p10 ( H-83 ) , Apaf-1 ( H-324 ) , Bcl2 ( C-2 ) were purchased from Santa Cruz Biotechnology , Inc ( Santa Cruz , CA ) . P21 ( ab7960 ) antibody was purchased from Abcam ( Cambridge , MA ) . GAPDH antibody was procured from US-Biological Corp . ( Swampscott , MA ) . Flag ( M2 ) -epitope , anti-mouse antibody was purchased from Sigma-Aldrich Corp . ( St . Louis , MO ) . Hybridomas for mouse anti-Myc ( 9E10 ) , anti-Hemaggutinin ( 12CA5 ) , A10 were previously described [99] . HEK-293 ( human embryonic kidney cell line ) was kindly provided by Jon Aster ( Brigham and Woman's Hospital , Boston , MA , USA ) . HEK-293 , and MEF cells were grown in Dulbeccoo's modified Eagle's medium ( DMEM ) . EBV negative Burkitt's lymphoma cells BJAB , DG75 , Ramos were kindly provided by Elliot Kieff ( Harvard Medical School , Boston , MA ) . BJAB stably expressing EBNA3C ( BJAB7 , BJAB10 ) were previously described [76] . EBV transformed lymphoblastoid cell lines ( LCL1 , LCL2 ) were maintained in RPMI 1640 media Transfection in HEK-293 , MEF and B-cells were performed by electroporation system with Bio-Rad Gene Pulser II electroporator . Cells were electroporated at 210 V and 975 µF ( for HEK-293 , MEF cells ) or 220 V and 975 µF ( for DG75 , Ramos , LCL1 cells ) . PBMCs ( Peripheral blood mononuclear cells ) were obtained from healthy donors from University of Pennsylvania Immunology Core as mentioned previously [100] . As defined earlier [46] , 10 million PBMCs were mixed with wild type and EBNA3C knockout mutant ( BAC-GFP-ΔE3C-EBV ) virus supernatant in 1 ml of RPMI 1640 media containing 10% FBS for 4 hrs at 37°C and 5% co2 environment . Next , cells were centrifuged at 500×g for 5 minutes and pelleted cells were again re-suspended in 2 ml of complete medium . EBV-GFP expression was checked by fluorescence microscopy and used to evaluate the infection . Infected cells were harvested at specific time intervals to determine the Pim-1 protein and mRNA levels . For GST pull-down assays , cell lysates from BJAB , BJAB7 , BJAB10 , LCL1 , LCL2 cells were incubated with bacterially purified control GST protein and GST fusion proteins . Protein samples were washed using Binding Buffer ( 1× PBS , 0 . 1% NP-40 , 0 . 5 mM DTT , 10% glycerol , with protease inhibitors ) and resolved by 10% SDS-PAGE . A10 antibody was used for Western blot analysis . Cells were harvested and washed with 1× Phosphate Buffered Saline ( PBS ) . For the preparation of cell lysates , RIPA buffer ( 0 . 5% NP-40 , 10 mM Tris pH 7 . 5 , 2 mM EDTA , 150 mM NaCl , supplemented with 1 mM PMSF , and protease inhibitors ) was used . Cell lysates were then pre-cleared with normal mouse/rabbit serum rotating with 30 µl of Protein-A and Protein-G ( 1∶1 mixture ) -conjugated Sepharose beads for 1 hr at 4°C . 5% of the protein lysate was saved as input sample . Appropriate antibody ( 1 µg/ml ) was used to capture the specific protein of interest by rotating the sample overnight at 4°C . The immune-precipitated samples were washed with RIPA buffer . Protein samples were boiled in laemmli buffer [101] , resolved by SDS-PAGE and Western blotting was performed . The membranes were probed with appropriate antibodies and scanned using the Odyssey imager ( LiCor Inc . , Lincoln , NE ) . Ice-cold PBS was used to wash the cells prior to RNA isolation . Trizol reagent ( Invitrogen , Inc . , Carlsbad , CA ) was used for RNA extraction according to manufacturer's protocol . Next , Superscript II reverse transcriptase kit ( Invitrogen , Inc . , Carlsbad , CA ) was used for cDNA preparation according to the manufacturer's instructions . The primers for Pim-1 , EBNA3C , EBNA2 , EBNA3A , EBNA3B were 5′-CGAGCATGACGAAGAGATCAT-3′ and 5′-TCGAAGGTTGGCCTATCTGA-3′ [102] , 5′-AGAAGGGGAGCGTGTGTTGT-3′ and 5′-GGCTCGTTTTTGACGTCGGC-3′ , 5′- GAACTTCAACCCACACCATC-3′ and 5′- CGTGGTTCTGGACTATCTGG-3′ , 5′- GGTGAAACGCGAGAAGAAAG-3′ and 5′- CTCTCATCAGCAGCAACCTG-3′ , 5′- AGAAGAGGCCCTTGTGTCTT-3′ and 5′- GGATTTCAAGAGGGTCAGGT-3′ respectively . GAPDH primers were used as 5′-TGCACCACCAACTGCTTAG-3′ and 5′-GATGCAGGGATGATGTTC-3′ [22] . SYBER green Real-time master mix ( MJ Research Inc . , Waltham , MA ) was used for quantitative real-time PCR analysis . To check the specificity of the products a melting curve analysis was performed and the relative quantitation values were calculated by threshold cycle method . Triplicate sets were used to examine each sample . 300 thousand HEK-293 cells were transfected with different expression plasmids by Lipofectamine 2000 transfection reagents ( Invitrogen , Carlsbad , CA ) . Cells were fixed with 3% paraformaldehyde ( PFA ) with 0 . 1% Triton X-100 and 1% BSA was used for blocking purpose . Myc-tagged Pim-1 was detected by using anti-Myc ( 9E10 ) antibody and the expression of GFP-tagged EBNA3C was detected by GFP-fluorescence . BJAB , BJAB10 , and LCL1 cells were semi-air-dried on slides and fixed as mentioned above . Specific antibodies were used to check endogenous expressions of EBNA3C and Pim-1 . Nuclear staining was performed by using DAPI ( 4′ , 6′ , -diamidino-2-phenylindole; Pierce , Rockford , IL ) . After secondary antibody and DAPI incubation , cells were washed in 1× PBS and mounted with antifade mounting medium . The images were taken by Fluoview FV300 confocal microscope and FLUOVIEW software ( Olympus Inc . , Melville , NY ) was used for image analysis . HEK-293 cells were transfected with combinations of expression vectors . After 36 hrs of post-transfection , cells were washed with PBS and re-suspended into hypotonic buffer [5 mM Pipes ( KOH ) pH 8 . 0 , 85 mM KCl , 0 . 5% NP-40 supplemented with protease inhibitors ) . Cells were incubated on ice , and Dounce homogenizer was used to homogenize the cells with 20 strokes . Nuclei were pelleted down ( 2300×g for 5 min ) and the cytosolic fraction was collected . Nuclear pellets were washed again with PBS , re-suspended in nuclear lysis buffer ( 50 mM Tris , pH 8 . 0 , 2 mM EDTA , 150 mM NaCl , 1% NP-40 , and protease inhibitors ) and lysed by vortexing intermittently for 1 h . The soluble nuclear fraction was separated by centrifugation at 21 , 000×g for 10 min . To determine the efficiency of nuclear and cytoplasmic fractionation , Western blot analysis was done against the nuclear transcription factor SP1 and cytoplasmic protein GAPDH using specific antibodies . Expression vectors were transfected by electroporation in HEK-293 cells . Transfected cells were incubated for 36 hrs in fresh DMEM and treated with 20 µM MG132 ( Enzo Life Sciences International , Inc . , Plymouth Meeting , PA ) for another 6 hrs . Protein samples were immunoprecipitated with appropriate antibodies and resolved by SDS-PAGE . The level of ubiquitination was detected by HA-specific antibody ( 12CA5 ) . Myc-tagged-Pim-1 ( wild type or kinase dead mutant ) ( 5 mg ) , Flag-EBNA3C ( 5 mg ) expression constructs were transfected in HEK-293 cells . After 36 hrs of post-transfection , cell lysates were prepared and protein complexes were immunoprecipitated ( IP ) by using 9E10 ascites fluid . IP complexes were then washed with buffer A ( containing 25 mM Tris [pH 7 . 5] , 70 mM NaCl , 10 mM MgCl2 , 1 mM EGTA , 1 mM DTT , with protease and phosphatase inhibitors ) and incubated in 30 ml of kinase buffer B ( containing buffer A plus 10 mM cold ATP , and 0 . 2 mCi of [c-32P]-ATP/ml ) supplemented with bacterially purified GST-P21 ( wild type or T145A mutant ) for 30 min at 30°C . 2× laemmli buffer was added to stop the reaction and heated at 95°C for 10 min . 10% SDS-PAGE was used for resolving the labeled proteins . Quantitation of the band was performed by using Image Quant software ( GE Healthcare Biosciences , Pittsburgh , PA ) . Transient transfection was performed in 10 million HEK-293 cells using electroporation system with combinations of plasmids as mentioned in the text . After 36 hours transfection , transfected cells as well as B-cells were treated with 40 µg/ml cyclohexamide in specific time periods with DNA damage response and cell lysates were prepared with RIPA buffer . Protein samples were subjected to Western blot analysis . Odyssey 3 . 0 software was used to quantify the band intensities . Short-hairpin ( sh ) oligonucleotides directed against EBNA3C were described previously [22] . The Pim-1 target sequence 5′-GUGUACUUUAGGCAAAGGG-3′ was described previously [43] . sh-oligonucleotides used for EBNA2 , EBNA3A and EBNA3B knockdown were 5′- TCGAGTTGTTGACACGGATAGTCTTTCAAGAGAAGACTATCCGTGTCAACAATTTTTTA-3′ and 5′- CGCGTAAAAAATTGTTGACACGGATAGTCTTCTCTTGAAAGACTATCCGTGTCAACAAC-3′ , 5′- TCGAGGAACACTTCTTCAAGCGATTTCAAGAGAATCGCTTGAAGAAGTGTTCTTTTTTA-3′ and 5′- CGCGTAAAAAAGAACACTTCTTCAAGCGATTCTCTTGAAATCGCTTGAAGAAGTGTTCC-3′ , 5′- TCGAGTTGATTGTCATTGGTTTCATTCAAGAGATGAAACCAATGACAATCAATTTTTTA-3′ and 5′- CGCGTAAAAAATTGATTGTCATTGGTTTCATCTCTTGAATGAAACCAATGACAATCAAC-3′ respectively . EBNA3C and Pim-1 specific oligonucleotides were cloned into pGIPZ vector at XhoI and MluI restriction sites . Control shRNA sequence ( Dharmacon Research , Chicago , IL ) was used as 5′-TCTCGCTTGGGCGAGAGTAAG-3′ which lacks complementary sequences in the human genome , also cloned in pGIPZ vector . Lentivirus production and transduction of EBV-transformed LCL1 were described previously [51] . 10 million Human kidney embryonic cells were subjected to transient transfection with Ctrl-vector , Myc-Pim-1 , and Flag-tagged-EBNA3C by electroporation system . Transfected cells were allowed to grow in DMEM containing G418 as 1 mg/ml concentration . After selecting the cells up to 2-weeks , selected cells were fixed with 4% formaldehyde and stained with 0 . 1% crystal violet solution ( Sigma-Aldrich Corp . , St . Louis , MO ) . The area of the colonies was calculated by using Image J software ( Adobe Inc . , San Jose , CA ) . The data shown here are average of three independent experiments . HEK-293 , MEF cells were transfected with different combinations of expression vectors by electroporation as described in the text . Transfected cells were grown in DMEM and were selected with 1000 µg/ml G418 antibiotic for 2-weeks . After selection , Cells were incubated without serum and with etoposide ( MP Biomedicals , LLC ) treatment for 12 hrs . Cell lysates were prepared by RIPA buffer and protein expression was examined by Western blotting . From each transfected and selected set , 0 . 1×106 cells were plated and allowed to grow them for 6 days . Also , LCL1 , sh-Ctrl LCL1 and sh-Pim-1 LCL1 cells were plated and grown in RPMI media . Counting of viable cells at specific time points was performed by using Trypan Blue dye exclusion method . All experiments were performed in triplicates . HEK-293 cells were transfected with specific plasmid vectors as indicated in the text . After 36 hrs of post-transfection , BrdU was added and incubated cells for 2 hours in the presence of DNA damaging agents . Cells were fixed with 4% paraformaldehyde ( PFA ) for 15 min in room temperature . Cells were washed with PBS . 2 M HCl was then added and incubated for 20 min at room temperature . Next , 0 . 1 M sodium borate ( Na2B4O7 ) pH 8 . 5 was added and incubated for 2 min at room temperature . Cells were washed with PBS and incubated with 0 . 2% Triton X100 , 3% BSA in 1×PBS for 5 min at room temperature . Cells were washed three times with PBS/BSA for 10 min each . Cells were incubated with anti BrdU antibody in PBS/BSA solution . After washing three times with PBS/BSA solution , cells were incubated for 1 hr with secondary antibody . DAPI was added at the final washing steps to stain DNA . The images were observed by Fluoview FV300 confocal microscope . Data represented here are as the mean values with standard errors of means ( SEM ) . The significance of differences in the mean values was calculated by performing 2-tailed student's t-test . P-value of <0 . 05 was considered here as statistically significant . Homo sapiens pim-1- GenBank: M16750 . 1 , Homo sapiens cyclin-dependent kinase inhibitor 1A ( p21 , Cip1 ) - GenBank: BC001935 . 1 , Epstein-Barr virus ( EBV ) genome , strain B95-8- GenBank: V01555 . 2 , human Pim-1 protein- UniProtKB/Swiss-Prot: P11309 , human P21 protein- UniProtKB/Swiss-Prot: P38936 , EBNA3C protein- UniProtKB/Swiss-Prot: P03204 . 1 . | The oncogenic serine/threonine kinase Pim-1 is upregulated in a number of human cancers including lymphomas , gastric , colorectal and prostate carcinomas . EBV nuclear antigen 3C ( EBNA3C ) is essential for EBV-induced transformation of human primary B-lymphocytes . Our current study revealed that EBNA3C significantly enhances Pim-1 kinase expression at both the transcript and protein levels . EBNA3C also interacts with Pim-1 and can form a complex in EBV-transformed cells . Moreover , EBNA3C increases nuclear localization of Pim-1 and stabilizes Pim-1 protein levels by inhibiting its poly-ubiquitination . Additionally , EBNA3C augments Pim-1 mediated phosphorylation of p21 and its proteosomal degradation . Stable knockdown of Pim-1 using si-RNA showed a significant decrease in proliferation of EBV transformed lymphoblastoid cell lines and subsequent induction of apoptosis by triggering the intrinsic apoptotic pathway . Therefore , our study demonstrated a new mechanism by which the oncogenic Pim-1 kinase targeted by EBV latent antigen 3C can inhibit p21 function , and is therefore a potential therapeutic target for the treatment of EBV-associated malignancies . | [
"Abstract",
"Introduction",
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] | [
"biology",
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] | 2014 | EBNA3C Augments Pim-1 Mediated Phosphorylation and Degradation of p21 to Promote B-Cell Proliferation |
Intracellular bacterial pathogens have developed a variety of strategies to avoid degradation by the host innate immune defense mechanisms triggered upon phagocytocis . Upon infection of mammalian host cells , the intracellular pathogen Francisella replicates exclusively in the cytosolic compartment . Hence , its ability to escape rapidly from the phagosomal compartment is critical for its pathogenicity . Here , we show for the first time that a glutamate transporter of Francisella ( here designated GadC ) is critical for oxidative stress defense in the phagosome , thus impairing intra-macrophage multiplication and virulence in the mouse model . The gadC mutant failed to efficiently neutralize the production of reactive oxygen species . Remarkably , virulence of the gadC mutant was partially restored in mice defective in NADPH oxidase activity . The data presented highlight links between glutamate uptake , oxidative stress defense , the tricarboxylic acid cycle and phagosomal escape . This is the first report establishing the role of an amino acid transporter in the early stage of the Francisella intracellular lifecycle .
Francisella tularensis is a Gram-negative bacterium causing the disease tularemia in a large number of animal species . This highly infectious bacterial pathogen can be transmitted to humans in numerous ways [1] , including direct contact with sick animals , inhalation , ingestion of contaminated water or food , or by bites from ticks , mosquitoes or flies . Four different subspecies ( subsp . ) of F . tularensis that differ in virulence and geographic distribution exist , designated subsps . tularensis , holarctica , mediasiatica and novicida , respectively . The tularensis subspecies is the most virulent causing a severe disease in humans [2] , [3] . F . tularensis subsp . novicida ( F . novicida ) is rarely pathogenic to non-immuno-compromized humans but is fully virulent for mice and is therefore widely used as a model to study Francisella intracellular parasitism . F . novicida has the capacity to evade host defenses and to replicate to high numbers within the cytosol of eukaryotic cells [4] . The bacterium is able to enter and to replicate inside a variety of cells , and in particular in macrophages . After a transient passage through a phagosomal compartment , bacteria are released within 30–60 minutes in the host cell cytosol where they undergo several rounds of active replication [1] . Upon Francisella entry into macrophages , the phagosomal compartment transiently acidifies and the activation of NADPH oxidase leads to the production of noxious oxygen reactive species [5] . Although several genes required for phagosomal escape have been identified ( [6] , [7] and references therein ) , the molecular mechanisms underlying this complex process are still very poorly understood . Protection against oxidative stress includes the production of anti-oxidant molecules ( such as glutathione and NADPH ) and of enzymes ( such as catalases , superoxide dismutases glutaredoxin-related protein and alkylhydroperoxide reductases ) . Francisella subspecies encode a whole set of such oxidative stress-related enzymes [8] . Inactivation of the corresponding genes generally leads to increased sensitivity to oxidative stress , defective intracellular multiplication , and attenuated virulence [9] , [10] , [11] . Protection against oxidative and other stress also involves a number of dedicated protein chaperones and chaperone complexes [12] . In contrast , the importance of acid-resistance mechanisms in Francisella intracellular survival remains controversial [13] , [14] , [15] and their possible contribution to pathogenesis still largely unknown . One of the best characterized acid-resistance systems in bacteria couples the glutamate:γ-aminobutyrate exchanger GadC with the glutamate decarboxylase ( s ) GadA and/or GadB [16] . The decarboxylase replaces the α-carboxyl group of its amino acid substrate with a proton that is consumed from the cytoplasmic pool [17] . The capacity to produce γ-aminobutyric acid ( GABA ) through glutamate decarboxylation has been observed in both Gram-negative and Gram-positive bacteria . The GadC/GadB glutamate decarboxylase ( GAD ) system has been shown to play an essential role in acid tolerance in food-borne bacterial pathogens that must survive the potentially lethal acidic environments of the stomach before reaching the intestine . Some bacteria possess a unique permease-decarboxylase pair whereas others , like Listeria monocytogenes [18] , encode several paralogues of each component . Recent genome sequence analyses and genome-scale genetic studies suggest that an important proportion of genes related to metabolic and nutritional functions participate to Francisella virulence [19] . However , the relationship between nutrition and the in vivo life cycle of F . tularensis remain poorly understood . Francisella is predicted to possess numerous nutrient uptake systems to capture its necessary host-derived nutrients , some of which are probably available in limiting concentrations . Notably , we showed very recently that an asparagine transporter of the major facilitator superfamily of transporters was specifically required for cytoslic multiplication of Francisella and its systemic dissemination [20] . The amino acid-polyamine-organocation family of transporters ( APC ) is specifically involved in amino acid transport [19] . Remarkably , eight of the 11 APC members have been identified at least once in earlier genetic studies , and are likely to be involved in bacterial virulence . In particular , the gene encoding the GadC permease has been identified in several different genome-wide screens , performed in either F . tularensis subsp . holarctica [21] or F . novicida [22] , [23] , [24] . In the present work , we elucidate the functional role of the GadC protein in Francisella pathogenesis . We show that glutamate uptake plays a critical role in Francisella oxidative stress defense in the phagosomal compartment . Strikingly , the activity of GadC influences the expression of metabolic genes and the production of tricarboxylic acid ( TCA ) cycle intermediates , unraveling a relationship between oxidative stress defense , metabolism and Francisella virulence .
F . tularensis subspecies possess a unique putative GAD system , composed of the antiporter GadC and a decarboxylase GadB ( encoded by genes FTN_0571 and FTN_1701 in F . novicida and hereafter designated gadC and gadB , respectively for simplification ) ( Figure S1A ) . The transcription of gadC is initiated 27 nucleotides upstream of the translational start from a predicted σ70 promoter ( Figure S1B ) . This genetic organization is highly conserved in all the available F . tularensis genomes ( not shown ) . The gene gadC encodes a protein of 469 amino acids sharing 98 . 7% , 99 . 1% and 99 . 6% identity with its orthologues in the subspecies mediasiatica ( FTM_1423 ) , holartica ( FTL_1583 ) and tularensis ( FTT_0480c ) , respectively . The Francisella GadC protein is predicted as a putative glutamate:γ-aminobutyric acid ( GAD ) antiporter ( KEGG database ) . Although it shows only modest homology ( approximately 25% amino acid identity ) with GadC of E . coli [25] , secondary structure prediction ( using the method for prediction of transmembrane helices HMM available at the internet site www . cbs . dtu . dk ) indicates that the GadC transporter of Francisella also comprises 12 transmembrane helixes and has its N and C-terminal ends facing the cytoplasm ( not shown ) . The gadB gene encodes a putative glutamate decarboxylase protein of 448 amino acid residues that is highly conserved in F . tularensis subsp . tularensis ( 98 . 7% amino acid identity with FTT_1722c ) . However , the corresponding protein is truncated at its C-terminal end in the subspecies holarctica ( FTL_1863 ) and mediasiatica ( FTM_1673 , and noted as a pseudogene in the KEGG database ) . We constructed a strain with chromosomal deletion of the entire gadC gene in F . novicida by allelic replacement [26] . We confirmed that the ΔgadC mutation did not have any polar effect on the downstream gene FTN_0570 by quantitative qRT-PCR ( Figure S1C ) . The growth kinetics of the parental F . novicida strain and the ΔgadC mutant were indistinguishable in tryptic soya broth ( TSB ) and chemically defined medium ( CDM ) [27] liquid media at 37°C ( Figure S2 ) , indicating that inactivation of gadC had no impact on bacterial growth in broth . We examined the ability of wild-type F . novicida , the ΔgadC mutant and a ΔgadC mutant strain complemented with a plasmid-encoded copy of wild-type gadC , to survive in murine and human macrophage cell lines and primary bone marrow-derived mouse macrophages , over a 24 h-period . The ΔgadC mutant showed a severe growth defect in J774 . 1 cells , comparable to that of a mutant deleted of the entire Francisella pathogenicity island ( ΔFPI mutant ) , with more than a 30-fold reduction of intracellular bacteria after 10 h and a 1 , 000-fold reduction after 24 h ( Figure 1A ) . Impaired multiplication of the ΔgadC mutant was also observed in THP-1 macrophages ( Figure 1B ) as well as in bone marrow-derived macrophages ( Figure 1C ) . In all cell types tested , introduction of the complementing plasmid ( pKK-gadC ) restored bacterial viability to same level as in the wild-type parent , confirming the specific involvement of the gadC gene in intracellular survival . Next , in vivo competition assays in BALB/c mice were performed to determine if the GadC protein played a role in the ability of Francisella to cause disease . Five mice ( 6- to 8-week old ) were inoculated by the intraperitoneal ( i . p . ) route with a 1∶1 mixture of wild-type F . novicida and ΔgadC mutant strains . Bacterial multiplication in the liver and spleen was monitored at day 2 post-infection ( Figure 1D ) . The Competition Index ( CI ) , calculated for both organs , was extremely low ( 10−6 ) demonstrating that the gene gadC played an essential role in Francisella virulence in the mouse model . Upon Francisella entry into cells , Francisella initially resides in a phagosomal compartment that transiently acidifies and that acquires reactive oxygen species . We therefore examined the ability of wild-type and ΔgadC mutant strains to survive under acid or oxidative stress conditions . For this , bacteria were exposed either to pH 5 . 5 or to 500 µM H2O2 ( Figure 2 ) . Under the pH condition tested , the viability of two strains was unaffected ( Figure 2A ) . It should be noted that at the lower pH of 2 . 5 , the viability of both wild-type and ΔgadC mutant was equally reduced ( approximately 2 logs , not shown ) . In contrast , the ΔgadC mutant strain appeared to be significantly more sensitive to oxidative stress than the wild-type strain in TSB ( Figure 2B ) . After 40 min of exposure , it showed a 10-fold decrease in the number of viable bacteria and an approximately 50-fold decrease after 60 min of exposure to H2O2 . Remarkably , in CDM , the wild-type and ΔgadC mutant strains were equally sensitive to H2O2 in the absence of glutamate supplementation ( Figure 2C ) . However , upon glutamate supplementation , the wild-type strain showed increased resistance to H2O2 whereas the ΔgadC strain was unaffected ( Figure 2D ) . Confocal and electron microscopy analyses demonstrated that the ΔgadC mutant had lost the capacity to escape from the phagosomal compartment of infected macrophages . Earlier phylogenetic studies have distinguished ten distinct subfamilies within the APC family of transporters , inferring possible substrate specificities . Consensus signature motifs were defined for each of them [34] . Inspection of the Francisella GadC protein reveals a signature sequence of the Glutamate-GABA antiporter subfamily in its N-proximal portion ( Figure 4A ) , prompting us to test functional complementation of an E . coli gadC mutant by the Francisella gadC orthologue . Functional complementation ( Figure 4B ) was determined by comparing the acid resistance ( at pH 2 . 5 ) of a gadC-inactivated strain of E . coli ( EF491 ) to the same strain carrying a plasmid-borne F . novicida gadC gene ( pCRT-gadC ) . As a positive control , we used the E . coli gadC mutant complemented with the wild-type E . coli gadC gene ( EF547 ) . IPTG-induced expression of the Francisella gadC allele in the E . coli gadC mutant strain restored acid resistance to wild-type level , indicating that the Francisella GadC protein displays the acid-resistance function of the E . coli GadC protein . To further support the role of GadC in glutamate entry , we quantified the amounts of intracellular glutamate by HPLC analysis , in the wild-type and ΔgadC strains grown in CDM supplemented with 1 . 5 mM of glutamate ( in the presence or in the absence of hydrogen peroxide ) . As shown in Fig . 4C , the concentration of intracellular glutamate was significantly lower in the ΔgadC mutant than in the wild-type strain , both in the absence ( 84% reduction in concentration ) or in the presence ( 31% reduction ) of H2O2 . We also quantified the amount of glutamate in culture supernatants of the two strains in the presence of H2O2 ( not shown ) . External glutamate present in the culture medium of the wild-type strain was 39% lower than that of the ΔgadC mutant . Altogether these data are compatible with a reduced capacity of the ΔgadC mutant to take up external glutamate . We then directly evaluated the impact of gadC inactivation on glutamate uptake by live F . novicida . For this , we compared the uptake of radiolabeled glutamate ( 14C-Glu ) by wild-type F . novicida to that of the ΔgadC mutant , over a broad range of glutamate concentrations ( Fig . 4D ) . Incorporation of 14C-Glu was significantly affected in the ΔgadC mutant ( representing only approximately 50% of the wild-type values at each concentration tested ) , confirming that GadC is a genuine glutamate transporter . The fact that glutamate uptake was not totally abolished in the ΔgadC mutant suggests that other transporter ( s ) allow the entry of glutamate in this strain . We compared the amount of reactive oxygen species ( ROS ) in J774 . 1 cells infected either with wild-type F . novicida , ΔgadC or the ΔFPI strain , over a 60 min period . For this , we used the H2DCF-DA assay ( Sigma-Aldrich Co ) . H2DCF-DA is a non-fluorescent cell-permeable compound that has been widely used for the detection of ROS [35] . Once inside the cell , this compound is first cleaved by endogenous esterases to H2DCF . The de-esterified product becomes the highly fluorescent compound 2′ , 7′-dichlorofluorescein ( DCF ) upon oxidation by ROS . The ROS content increased by 25% after 60 min in cells infected with wild-type F . novicida ( Figure 5 ) . A comparable increase was recorded with the ΔFPI mutant . However , in cells infected with the ΔgadC mutant , the ROS content was significantly higher than that recorded with the two other strains at each time point ( 25% higher at 15 min , and 55% higher after 60 min ) . These results suggest that the ΔgadC mutant is affected in its ability to neutralize the production of ROS in the phagosomal compartment . Alternatively , the ΔgadC mutant may trigger an increased production of ROS . This result prompted us to evaluate the pathogenicity of the ΔgadC mutant in mice lacking a functional NADPH oxidase complex , both in vitro and in vivo . Intracellular glutamate plays a central role in a wide range of metabolic processes in bacteria . In order to evaluate the potential impact of the gadC inactivation on bacterial glutamate metabolism , we first quantitatively monitored the transcription of selected genes connecting glutamate utilization to either the TCA cycle or to glutathione biogenesis . This analysis was done for wild-type F . novicida and for the ΔgadC mutant strain , grown in broth with or without H2O2 ( Figure 7A ) . Expression of FTN_0593 ( sucD ) , FTN_0127 ( gabD ) and FTN_1532 ( gdhA ) , was significantly decreased in the ΔgadC mutant under oxidative stress , whereas their expression was moderately increased in the wild-type strain . Expression of FTN_0277 ( gshA ) and FTN_0804 ( gshB ) was reduced in both strains , under oxidative stress . However , the decrease was significantly less important ( app . 4-fold ) in the ΔgadC mutant than in the wild-type strain . Expression of FTN_1635 ( sucA ) was significantly decreased in both strains under oxidative stress . These data indicate that the absence of gadC affects the expression of several genes linked to glutamate metabolism under oxidative stress . The fact that expression of the gadC gene itself was significantly upregulated ( approximately 10-fold ) in the wild-type strain exposed to H2O2 stress ( not shown ) supports the importance of the GadC transporter in oxidative stress defense . Direct quantification of TCA cycle intermediates present in the cytoplasm of the wild-type and ΔgadC strains , by gas chromatography coupled with mass spectrometry ( see Materials and methods for details ) , revealed that gadC inactivation significantly affected succinate , fumarate , and oxoglutarate contents ( Figure 7C ) . Indeed , in the ΔgadC mutant , the concentrations of succinate and fumarate were reduced ca . 60% as compared to the wild-type strain , whereas oxoglutarate was below the detection threshold of the assay . The concentrations of the three molecules increased up to 40% in the wild-type strain exposed to oxidative stress , suggesting an activation of the TCA under this condition . The concentrations of succinate and fumarate were not significantly modified in the ΔgadC mutant upon oxidative stress and oxoglutarate production was still below detection . The concentration of citrate was similar in the wild-type and the ΔgadC mutant and did not vary upon oxidative stress , in any of the two strains . The intracellular concentrations of glutathione were also almost similar in the wild-type and ΔgadC mutant ( Figure 7B ) . Remarkably , under oxidative stress , the intracellular concentration of glutathione increased in both strains but only reached 65% of the level of the ΔgadC mutant in the wild-type strain . These observations prompted us to evaluate the impact of supplementation with different TCA cycle intermediates on survival of the ΔgadC mutant in response to H2O2 challenge . For this , exponential phase wild-type and ΔgadC mutant strains , diluted in CDM supplemented with glutamate , were subjected to oxidative stress , in the presence or absence of either fumarate , succinate or oxoglutarate ( Figure S5 ) . The sensitivity to H2O2 of the ΔgadC mutant was not modified neither by fumarate nor by oxoglutarate . In contrast , supplementation with succinate increased significantly the survival of the ΔgadC mutant , to nearly wild-type level .
Inactivation of gadC in F . novicida led to a severe growth defect in all cell types tested and in vivo assays further demonstrated the importance of GadC in Francisella virulence . Confocal and electron microscopy analyses revealed that the severe intracellular growth defect of the mutant was due to its inability to escape from the phagosomal compartment of infected macrophages . Interestingly , most of the mutant bacteria that remained trapped within the phagosome were still alive for at least 10 h post-infection , indicating that impaired escape was not due to bacterial death . Since the ΔgadC mutant showed increased susceptibility to oxidative stress in broth and failed to efficiently neutralize reactive oxygen species production in cells , it is likely that ROS may predominantly affect bacterial escape rather than survival . F . tularensis produces enzymes that can metabolize and neutralize ROS , such as a superoxide dismutases ( SodB , SodC ) , a catalase ( KatG ) , a glutathione peroxidase and a peroxireductase [9] , [10] . Acid phosphatases have also been implicated in the resistance of intracellular Francisella to H2O2 generated in the phagosomal compartment by the NADPH oxidase ( [37] , [38] and references therein ) . However , inactivation of the major phosphatase acpA in F . tularensis subsp . tularensis , had no impact on the activity of the NADPH oxidase in human neutrophils [5] , thus confirming that other Francisella factors were involved in NADPH oxidase inhibition . We show here that Francisella GadC is an important player specifically involved in oxidative stress defense . The existence of several paralogues of both the transporter GadC and the decarboxylase GadB in some bacterial species ( for example in L . monocytogenes ) might account for the fact that these have not yet been found to contribute to oxidative stress resistance and intracellular survival in standard genetic screens . Indeed , if functional paralogues exist , they must be simultaneously inactivated to observe a possible phenotypic defect . In addition , isofunctional antiporters with no significant amino acid sequence similarity to the GadC protein might exist in these bacteria . The contribution of the GAD system to intracellular survival critically depends on the cellular compartment where bacterial survive and multiply . Indeed , bacteria residing in vacuolar compartments ( such as Salmonella , Mycobacteria , Legionella , Brucella and Chlamydia ) encounter different types of stresses ( pH , oxidative , nutritional , … ) than bacteria able to multiply in the host cell cytosol ( such as Francisella , Listeria , Shigella and Rickettsia ) . L-glutamate is very abundant in the intracellular compartment ( reported concentrations vary between 2 and 20 mM ) when compared to the extracellular compartment ( app . 20 µM ) [39] . Human macrophages have both the cystine/glutamate transporter and the Na-dependent high-affinity glutamate transporters ( excitatory amino acid transporters , EAATs ) that transport glutamate and aspartate . To maintain their intracellular pool of glutamate , macrophages may use either these transporters to import glutamate from the extracellular milieu or enzymatically convert cytosolic glutamine ( via glutaminase ) and aspartate ( via aspartate transpeptidase ) to glutamate . Glutamate might also be produced spontaneously intracellularly from pyroglutamate . Currently , nothing is known with respect to the content of glutamate in the phagosomal compartment . This might prove extremely difficult to establish , especially for pathogens such as Francisella or Listeria that reside only very transiently in this compartment . A limited number of bacterial species have been shown to possess a GAD system [40] . These include E . coli , Lactobacillus , L . monocytogenes and Shigella species , in which the GAD system plays a major role in acid tolerance . It has been suggested that the GAD system is important for pathogenic microorganisms that , upon oral infection of mammalian hosts , need to survive the low pH of the stomach . However , some enteric pathogens like Salmonella do not possess a function GAD system and must thus rely on other anti-acidic pH strategies . Interestingly , the GAD system has been also found to contribute to oxidative stress defense in yeast and plant [41] . In bacteria , molecules such as the NADPH and NADH pools and glutathione ( GSH ) , contribute to oxidative stress defense . Reduced GSH , present at mM concentrations , maintain a strong reducing environment in the cell . Specific enzymes are also dedicated to control the levels of reactive oxygen species ( ROS ) . Remarkably , the ΔgadC mutant was still outcompeted by wild-type bacteria in phox-KO mice . The different environments and the immune pressure , encountered by the bacterium during its systemic dissemination , are probably far more complex than in culture systems . In vivo , Francisella GadC is thus likely to contribute to other functions than combat ROS in the phagosomal compartment . It may , for instance , fulfill classical nutritional functions during bacterial cytosolic multiplication ( in macrophages and/or in other infected non-phagocytic cells ) . Alternatively , GadC may be required during the bacterial blood stage multiplication and dissemination of the bacterium . In E . coli , GABA produced by the glutamate decarboxylase is metabolized via the GABA shunt pathway . This leads to the production of succinate via the consecutive action of two enzymes: a GABA/oxoglutarate amino-transferase ( GabT ) that removes the amino group from GABA to form succinic semialdehyde ( SSA ) and Glu; and a succinic semialdehyde dehydrogenase ( GabD ) that oxidises SSA to form succinate . Very recently , Karatzas and co-workers have shown [42] that L . monocytogenes also possessed functional GabT and GabD homologues that could provide a possible route for succinate biosynthesis in L . monocytogenes . The GABA shunt pathway , allowing the bypass of two enzymatic steps of the TCA ( from oxoglutarate to succinate; Figure 7 and S6 ) , is thought to play a role in glutamate metabolism , anaplerosis and antioxidant defense . However , its physiological role in pathogenesis is yet poorly understood . Francisella genomes possess a gabD orthologue but lack gabT . The GABA shunt pathway may therefore be non-functional in Francisella . Interestingly , the isogenic glutamate decarboxylase ΔgadB mutant ( Figure S4 ) that we constructed , showed a much less severe intracellular multiplication defect than the ΔgadC mutant , and as well as no ( or only a very mild ) attenuation of virulence . If the glutamate imported via GadC would serve to produce GadB-mediated GABA , one would expect gadB inactivation to cause the same defect as gadC inactivation . As already mentioned in the Introduction , the gadB orthologue encodes a truncated protein in the subspecies holarctica . Altogether , these data support the notion that GadC and GadB of Francisella do not function in concert , unlike in several other bacterial species , and that GABA production plays a marginal role in Francisella pathogenesis . Further work will be required to understand the exact contribution of GadB in Francisella metabolism . Our data indicate that GadC of Francisella encodes a genuine glutamate transporter involved in oxidative stress , unlike most other GadC orthologues described thus far . Glutamate can be converted in the bacterial cytoplasm into a number of compounds ( Figure 7 ) , such as glutamine , glutathione , GABA or the TCA cycle intermediate oxoglutarate . Oxoglutarate is known to be a potent anti-oxidant molecule that can be converted , in absence of any enzymatic reaction , into succinate in the presence of H2O2 . In addition , conversion of glutamate to oxoglutarate by the glutamate dehydrogenase GdhA increases the production of NADPH , which might also contribute to the anti-oxidant effect of glutamate acquisition . Quantitative analyses of the intra-bacterial content of TCA cycle intermediates ( Figure 7B ) revealed a significant reduction of succinate and fumarate in the gadC mutant , as compared to wild-type F . novicida , and a striking decrease of oxoglutarate . These data support the notion that reduced entry of glutamate directly affects the production of these TCA cycle intermediates . In contrast , the amount of citrate remained unchanged in the mutant , suggesting refueling of the TCA cycle via other entry points ( such as glycolysis or amino acid conversion ) . Of note , mutants in genes gdhA ( FTN_1533 ) and gabD ( FTN_0127 ) were identified as required for replication in D . melanogaster S2 cells in a recent screen , supporting a role for these genes in intracellular bacterial survival [43] . The production and utilization of oxoglutarate by Francisella may thus constitute an efficient mean to modulate its cytoplasmic concentration of ROS . In the absence of external glutamate , the pool of intracellular glutamate may be synthesized either from oxoglutarate , glutamine , GSH or even proline ( according to KEGG metabolic pathways ) . Therefore , we evaluated the impact of gadC inactivation on the expression of genes involved in glutamate metabolism , under oxidative stress conditions . qRT-PCR analyses were performed in wild-type F . novicida and in the ΔgadC mutant , grown in chemically defined medium containing glutamate , in the absence or in the presence of H2O2 ( Figure 7A ) . These assays revealed that gadC inactivation led to an important down-regulation of the genes involved the conversion of glutamate to oxoglutarate and succinate , upon oxidative stress ( FTN_1532 and FTN_0127 , respectively ) . Conversely , gadC inactivation only moderately decreased the expression of gshA and gshB , the two genes involved in glutathione biosynthesis ( FTN_0277 and FTN_0804 ) , upon oxidative stress whereas the expression of these genes was severely decreased in the wild-type strain . These data are compatible with the notion that , under oxidative stress , the wild-type strain may favor the conversion of a fraction of its cytoplasmic pool of glutamate ( neosynthesized and imported ) to produce oxoglutarate and succinate rather than GSH . In contrast , when the cytosolic pool of glutamate is restricted to neosynthesized glutamate ( i . e . in a gadC mutant or in a glutamate-depleted medium ) , the production of oxaloglutarate and succinate may be decreased to favor that of other molecules ( including GSH ) . In conclusion , we identified a glutamate transporter as a novel Francisella virulence attribute that suggests links between the oxidative stress response and the TCA cycle during the early stage of the bacterial intracellular life cycle . The importance of the TCA cycle in the homeostasis of reactive oxygen species has just started to be considered in pathogenic bacterial species [12] , [44] , [45] , [46] . The development of specific inhibitors of transport systems involved in intracellular adaptation might constitute interesting anti-bacterial therapeutic targets .
All experimental procedures involving animals were conducted in accordance with guidelines established by the French and European regulations for the care and use of laboratory animals ( Decree 87–848 , 2001–464 , 2001–486 and 2001–131 and European Directive 2010/63/UE ) and approved by the INSERM Ethics Committee ( Authorization Number: 75-906 ) . F . tularensis subsp . novicida ( F . novicida ) strain U112 , its ΔFPI derivative , and all the mutant strains constructed in this work , were grown as described in Supplementary Material . E . coli strains ( kindly provided by John Foster , University of South Alabama , USA ) were grown as described in Supplementary Material . All bacterial strains , plasmids , and primers used in this study are listed in Supplemental Table 1 . Details of the construction and characterization of mutant and complemented strains; macrophage preparation and infections , are described in Supplementary Material . Quantitative ( q ) RT-PCR ( real-time PCR ) was performed with gene-specific primers ( Supplemental Table 1 ) , using an ABI PRISM 7700 and SYBR green PCR master mix ( Applied Biosystems , Foster city , CA , USA ) . Electron and confocal microscopy complete descriptions; real time cell death and phagosome permeabilization assays , are described in Supplementary Material . J774 . 1 macrophage-like cells ( ATCC Number: TIB-67 ) were propagated in Dulbecco's Modified Eagle's Medium ( DMEM ) containing 10% fetal calf serum , whereas human monocyte-like cell line THP-1 ( ATCC Number: TIB-202 ) and bone marrow-derived macrophages ( BMM ) from BALB/c were propagated in RPMI Medium 1640 containing 10% fetal calf serum , respectively . J774 . 1 and BMM were seeded at a concentration of ∼2×105 cells per well in 12-well cell tissue plates and monolayers were used 24 h after seeding . THP-1 were seeded at a concentration of ∼2×105 cells per well in 12-well cell tissue plates 48 h before infection , and supplemented with phorbol myristate acetate ( PMA ) to induce cell differentiation ( 200 ng/ml ) . J774 . 1 , BMM and THP-1 were incubated for 60 min at 37°C with the bacterial suspensions ( approximately multiplicities of infection 100 ) to allow the bacteria to enter . After washing ( time zero of the kinetic analysis ) , the cells were incubated in fresh culture medium containing gentamicin ( 10 µg mL−1 ) to kill extracellular bacteria . At several time-points , cells were washed three times in DMEM or RPMI , macrophages were lysed by addition of water and the titer of viable bacteria released from the cells was determined by spreading preparations on Chocolate agar plates . For each strain and time in an experiment , the assay was performed in triplicate . Each experiment was independently repeated at least three times and the data presented originate from one typical experiment . Bacteria were centrifuged for 2 min in a microcentrifuge at room temperature and the pellet was quickly re-suspended in Trizol solution ( Invitrogen , Carlsbad , CA , USA ) . Samples were either processed immediately or frozen and stored at −80°C . Samples were treated with chloroform and the aqueous phase was used in the RNeasy Clean-up protocol ( Qiagen , Valencia , CA , USA ) with an on-column DNase digestion of 30 min [47] . RNA Reverse transcription ( RT ) -PCR experiments were carried out with 500 ng of RNA and 2 pmol of specific reverse primers . After denaturation at 65°C for 5 min , 6 µL of the mixture containing 4 µL of 5× first strand buffer and 2 µL of 0 , 1 M DTT were added . Samples were incubated 2 min at 42°C and , then , 1 µL of Superscript II RT ( Thermo Scientific ) was added . Samples were incubated for 50 min at 42°C , heated at 70°C for 15 min and chilled on ice . Samples were diluted with 180 µL of H2O and stored at −20°C . The following pair of primers was used to amplify the mRNA corresponding to the transcript of FTN_0570 ( p13/p14 ) , FTN_0571 ( p15/p16 ) , FTN_1700 ( p27/9p28 ) , FTN_1701 ( p29/p30 ) , FTN_1702 ( p31/p32 ) , FTN_1532 ( p33/p34 ) , FTN_0127 ( p35/p36 ) , FTN_0277 ( p37/p38 ) , FTN_0804 ( p39/p40 ) , FTN_0593 ( p41/p42 ) , FTN_1434 ( p43/p44 ) and FTN_1635 ( p45/p46 ) ( Supplemental Table 1 ) . Wild-type F . novicida and mutant strains were grown at 37°C from OD600 ∼0 . 1 . After 4 h of incubation , samples were harvested and RNA was isolated . For oxidative stress tests , samples were cultivated 30 min more with or without H2O2 ( 500 µM ) . The 25 µL reaction consisted of 5 µL of cDNA template , 12 . 5 µL of Fastart SYBR Green Master ( Roche Diagnostics ) , 2 µL of 10 µM of each primer and 3 . 5 µL of water . qRT-PCR was performed according manufacturer's protocol on Applied Biosystems - ABI PRISM 7700 instrument ( Applied Biosystems , Foster City , CA ) . To calculate the amount of gene-specific transcript , a standard curve was plotted for each primer set using a series of diluted genomic DNA from wild-type F . novicida . The amounts of each transcript were normalized to helicase rates ( FTN_1594 ) . Stationary-phase bacterial cultures were diluted at a final OD600 of 0 . 1 in TSB broth or CDM with or without glutamate ( 1 . 5 mM final ) . Exponential-phase bacterial cultures were diluted to a final concentration of 108 bacteria mL−1 and subjected to either 500 µM H2O2 or pH 5 . 5 . Oxidative stress response was also tested in CDM supplemented with glutamate , in the presence or absence of the TCA cycle intermediates: oxoglutarate , succinate or fumarate ( 1 . 5 mM final ) . The number of viable bacteria was determined by plating appropriate dilutions of bacterial cultures on Chocolate Polyvitex plates at the start of the experiment and after the indicated durations . Cultures ( 5 mL ) were incubated at 37°C with rotation ( 100 rpm ) and aliquots were removed at indicated times , serially diluted and plated immediately . Bacteria were enumerated after 48 h incubation at 37°C . Experiments were repeated independently at least twice and data represent the average of all experiments . J774 . 1 cells were infected with wild-type F . novicida , ΔgadC or ΔFPI strains for 1 h , 4 h and 10 h at 37°C , and were washed in KHM ( 110 mM potassium acetate , 20 mM Hepes , 2 mM MgCl2 ) . Cells were incubated for 1 min with digitonin ( 50 µg/mL ) to permeabilize membranes . Then cells were incubated for 15 min at 37°C with primary anti F . novicida mouse monoclonal antibody ( 1/500 final dilution , Immunoprecise ) . After washing , cells were incubated for 15 min at 37°C with secondary antibody ( Ab ) ( Alexa Fluor 488-labeled GAM , 1/400 final dilution , Abcam ) in the dark . After washing , cells were fixed with PFA 4% for 15 min at room temperature ( RT ) and incubated for 10 min at RT with 50 mM NH4Cl to quench residual aldehydes . After washing with PBS , cells were incubated for 30 min at RT with primary anti-LAMP1 Ab ( 1/100 final dilution , Abcam ) in a mix with PBS , 0 . 1% saponine and 5% goat serum . After washing with PBS , cells were incubated for 30 min at RT with secondary anti-rabbit Ab ( alexa 546-labeled , 1/400 dilution , Abcam ) . DAPI was added ( 1/5 , 000 final dilution ) for 1 min . After washing , the glass coverslips were mounted in Mowiol . Cells were examined using an X63 oil-immersion objective on a LeicaTSP SP5 confocal microscope . Co-localization tests were quantified by using Image J software; and mean numbers were calculated on more then 500 cells for each condition . Confocal microscopy analyses were performed at the Cell Imaging Facility ( Faculté de Médecine Necker Enfants-Malades ) . Infection of J774 . 1 cells was followed by thin section electron microscopy as previously described [48] . To evaluate the viability of F . tularensis , labelings were adapted to use the cell-impermeant nucleic acid dye propidium iodide ( PI ) . J774 . 1 macrophage-like cells were seeded at 5 . 105cells/ml on glass coverslips in 12-well bottom flat plates . Next day , cells were infected for 10 h with wild-type F . novicida or ΔgadC strain . After infection , cells were first permeabilized with digitonin for 1 min , washed three times with KHM and incubated for 12 min at 37°C with 2 . 6 µM PI ( Life technologies , L7007 ) in KHM buffer to label compromised bacteria in permeabilized cells . Cells were washed three times with KHM and incubated for 15 min at 37°C with primary anti F . novicida mouse monoclonal antibody ( 1/500 final dilution ) . After washing , cells were incubated for 15 min at 37°C with secondary antibody ( Ab ) ( Alexa Fluor 488-labeled GAM , 1/400 final dilution ) in the dark . After washing , cells were fixed with PFA 4% for 15 min at room temperature ( RT ) and incubated for 10 min at RT with 50 mM NH4Cl to quench residual aldehydes . After washing with PBS , cells were incubated for 30 min at RT with primary anti-LAMP1 Ab ( 1/100 final dilution ) in a mix with PBS , 0 . 1% saponine and 5% goat serum . After washing with PBS , cells were incubated for 30 min at RT with secondary anti-rabbit Ab ( alexa 405-labeled , 1/400 dilution ) . After washing , the glass coverslips were mounted in Mowiol . Cells were examined using an X63 oil-immersion objective on a LeicaTSP SP5 confocal microscope . Analysis of cell fluorescence was performed with Image J software ( http://rsb . info . nih . gov/ij ) . Intracellular reactive oxygen species ( ROS ) were detected by using the oxidation-sensitive fluorescent probe dye , 2′ , 7′-dichlorodihydrofluorescein diacetate ( H2DCF-DA ) as recommended by the manufacturer ( CM-H2DCF-DA , Molecular Probes , Eugene , OR ) . J774 . 1 cells were seeded at 5 . 104 cells/well . Cells were infected with bacteria for 15 min ( MOI of 100∶1 ) , washed three times with PBS and incubated with H2DCF-DA diluted in PBS ( concentration ) . DCF fluorescence was measured with the Victor2 D fluorometer ( Perkin-Elmer , Norwal , CT ) with the use of excitation and emission wavelengths of 480 nm and 525 nm , respectively . Values were normalized by protein concentration in each well ( Bradford ) . Samples were tested in triplicates in three experiments . J774 . 1 cells were seeded at 5 . 104 cells/well . Cells were infected with bacteria for 15 min ( MOI of 1 , 000∶1 ) , washed three times with PBS and incubated with H2DCF-DA diluted in PBS for 1 h ( 5 µM ) . Images of the cells were captured with an Olympus CKX41 microscope and treated with Image J software . Cell counts were performed over 10 images of approximately 50 cells . Acid resistance tests in E . coli were performed at pH 2 . 5 as described previously [49] , by comparing the number of survival treated cells after 1 h of treatment over the number of cells at T0 . We compared survival of wild-type E . coli strain ( WT ) with E . coliΔgadC ( Δ ) and E . coliΔgadC complemented with the F . novicida gadC gene ( PCR-amplified gene FTN-0571 introduced into plasmid pCR2 . 1-Topo , in the correct orientation downstream of the plac promoter ) ( Comp ) . Acid challenge was performed by diluting 1∶100 the overnight ( 22 h ) culture in LB , supplemented ( Comp + ) or not ( Comp − ) with 10−4 M final IPTG . Glutamate detection and quantification was done by using HPLC analysis . Wild-type F . novicida and ΔgadC strains were tested in CDM supplemented with 1 . 5 mM of glutamate , with or without H2O2 ( 500 µM ) . For each condition , three independent cultures were prepared by overnight growth in CDM . The overnight cultures were diluted with 50 mL of fresh medium to OD600 of 0 . 05 and cultivated to an OD600 of app . 0 . 35 . Bacteria were harvested by centrifugation at 4 , 000× g for 20 min , resuspended in 25 mL of pre-warmed appropriated medium and cultivated for 30 min . For extracellular glutamate dosage , 100 µL of each supernatant were resuspended with 400 µL of cold methanol and centrifuged at 12 , 000× g for 5 min . 20 µL of each preparation were derivatized with 80 µL of OPA . For intracellular dosage , each sample were resuspended in 600 µL of cold methanol . The bacterial suspensions were sonicated thrice for 30 sec at 4 . 0 output , 70% pulsed ( Branson Sonifier 250 ) . Lysates were then centrifuged at 3 , 300× g for 8 min , to remove debris . Following steps were done with the standard procedure of Agilent using ZORBAX Eclipse AAA high as HPLC column . An amount equivalent to 2 µL of each sample was injected on a Zorbax Eclipse-AAA column , 5 µm , 150×4 . 6 mm ( Agilent ) , at 40°C , with fluorescence detection . Aqueous mobile phase was 40 mM NaH2PO4 , adjusted to pH 7 . 8 with NaOH , while organic mobile phase was acetonitrile/methanol/water ( 45/45/10 v/v/v ) . The separation was obtained at a flow rate of 2 mL min−1 with a gradient program that allowed for 2 min at 0% B followed by a 16-min step that raised eluent B to 60% . Then washing at 100% B and equilibration at 0% B was performed in a total analysis time of 38 min . To evaluate glutamate concentration , glutamate standard curve was made in parallel . The procedure for the measurement of GSH was previously described [50] . Briefly , GSH were separated by HPLC , equipped with a Shimadzu Prominence solvent delivery system ( Shimadzu Corp . , Kyoto , Japan ) , using a reverse-phase C18 Kromasil ( 5 µm; 4 . 6×250 mm ) , obtained from AIT ( Paris , Fr ) . The mobile phase for isocratic elution consisted of 25 mmol L−1 monobasic sodium phosphate , 0 . 3 mmol L−1 of the ion-pairing agent 1-octane sulfonic acid , 4% ( v/v ) acetonitrile , pH 2 . 7 , adjusted with 85% phosphoric acid . The flow rate was 1 mL min−1 . Under these conditions , the separation of aminothiol was completed in 20 min . Deproteinated samples were injected directly onto the column using a Shimadzu autosampler ( Shimadzu Corp . ) . Following HPLC separation , GSH was detected with a model 2465 electrochemical detector ( Waters , MA , USA ) equipped with a 2 mm Glassy carbon ( GC ) analytical cell and potential of +700 mV were applied . Cells were grown in Chamberlain medium to mid-exponential phase and then harvested by centrifugation and washed twice with Chamberlain without amino acid . The cells were suspended at a final OD600 of 0 . 5 in the same medium containing 50 mg/ml of chloramphenicol . After 15 min of pre-incubation at 25°C , uptake was started by the addition of L-[U-14C] glutamic acid ( Perkin Elmer ) , at various concentrations ( 14C-Glu ranging from 1 to 50 µM ) . The radiolabeled 14C-Glu was at a specific activity of 9 . 25 GBq . mmol −1 . Samples ( 100 µL of bacterial suspension ) were removed after 5 min and collected by vacuum filtration on membrane filters ( Millipore type HA , 25 mm , 0 . 22 mm ) and rapidly washed with Chamberlain without amino acid ( 2×5 mL ) . The filters were transferred to scintillation vials and counted in a Hidex 300 scintillation counter . The counts per minute ( c . p . m . ) were converted to picomoles of amino acid taken up per sample , using a standard derived by counting a known quantity of the same isotope under similar conditions . Succinate , fumarate , citrate and oxoglutarate were quantified by gas chromatography coupled with mass spectrometry ( GC/MS ) . Wild-type F . novicida and ΔgadC strains were grown as for glutamate quantification . Briefly , after an overnight preculture in CDM , three independent cultures of wild-type and ΔgadC mutant were cultivated in 50 mL CDM to an OD600 of app . 0 . 35 . Bacteria were harvested by centrifugation at 4 , 000× g for 15 min , resuspended to the same OD600 in pre-warmed CDM supplemented with glutamate ( 1 . 5 mM ) and cultivated for 30 min±500 µM H2O2 . Metabolite measurements were normalized by checking that each sample contained equal amounts of total proteins . Wild-type F . novicida and ΔgadC mutant strains were grown in TSB to exponential growth phase and diluted to the appropriate concentrations . 6 to 8-week-old female BALB/c mice ( Janvier , Le Genest St Isle , France ) were intra-peritoneally ( i . p . ) inoculated with 200 µl of bacterial suspension . The actual number of viable bacteria in the inoculum was determined by plating appropriate dilutions of bacterial cultures on Chocolate Polyvitex plates . For competitive infections , wild-type F . novicida and mutant bacteria were mixed in 1∶1 ratio and a total of 100 bacteria were used for infection of each of five mice . After two days , mice were sacrificed . Homogenized spleen and liver tissue from the five mice in one experiment were mixed , diluted and spread on to chocolate agar plates . Kanamycin selection to distinguish wild-type and mutant bacteria were performed . Competitive index ( CI ) [ ( mutant output/WT output ) / ( mutant input/WT input ) ] . Statistical analysis for CI experiments was as described in [51] . Macrophage experiments were analyzed by using the Student's t-test . | Intracellular bacterial pathogens have developed a variety of strategies to avoid degradation by the host innate immune defense mechanisms triggered upon phagocytocis . We show here for the first time that glutamate acquisition is essential for phagosomal escape and virulence of an intracellular pathogen . Remarkably , inactivation of the glutamate transporter GadC of Francisella impaired the capacity of the bacterium to neutralize reactive oxygen species ( ROS ) production in the phagosome . Virulence of the gadC mutant was partially restored in mice with a defective NADPH oxidase . Importantly , we found that impaired glutamate uptake affected the production of tricarboxylic acid ( TCA ) cycle intermediates , highlighting novel links between the TCA cycle and bacterial phagosomal escape . Amino acid transporters are , thus , likely to constitute underscored players in microbial intracellular parasitism . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbial",
"metabolism",
"host-pathogen",
"interaction",
"biology",
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] | 2014 | Glutamate Utilization Couples Oxidative Stress Defense and the Tricarboxylic Acid Cycle in Francisella Phagosomal Escape |
Ocular pentastomiasis is a rare infection caused by the larval stage of pentastomids , an unusual group of crustacean-related parasites . Zoonotic pentastomids have a distinct geographical distribution and utilize reptiles or canids as final hosts . Recently , an increasing number of human abdominal infections have been reported in Africa , where pentastomiasis is an emerging , though severely neglected , tropical disease . Here we describe four ocular infections caused by pentastomids from the Democratic Republic of the Congo . Two cases underwent surgery and an Armillifer grandis infection was detected by morphological and molecular approaches . Thus far , 15 other cases of ocular pentastomiasis have been reported worldwide . Twelve cases were caused by Armillifer sp . , recorded almost exclusively in Africa , where such infections occur as a consequence of hunting and consuming snakes , their final hosts . Seven further cases were caused by Linguatula serrata , a cosmopolitan pentastomid whose final hosts are usually canids . Intraocular infections caused permanent visual damage in 69% and a total loss of vision in 31% of reported cases . In contrast , ocular adnexal cases had a benign clinical course . Further research is required to estimate the burden , therapeutic options and pathogenesis of this neglected disease .
Pentastomiasis is a neglected zoonotic disease caused by the larval stage ( nymphs ) of pentastomids , a unique and enigmatic group of crustacean-related parasites . The parasites usually have an indirect lifecycle , involving various intermediate and definitive hosts . Linguatula serrata , a species occurring in temperate climatic regions of the world , utilizes canids as definitive hosts , whereas Porocephalus species in America and Armillifer species in Africa and Asia ( Armillifer armillatus in West Africa , A . grandis in Central Africa , A . agkistrodontis and A . moniliformis in Asia ) inhabit snakes as final hosts [1] . In the respiratory tract of the final host , the adults produce a large number of infective eggs , which are excreted via respiratory and enteral secretions . The eggs then infect suitable intermediate hosts ( often rodents and small non-human primates in the case of Armillifer infection ) . Humans can become accidental intermediate ( dead-end ) hosts . After ingestion of infective ova , the nymphs hatch in the gut of the intermediate host and invade the viscera , where they grow and moult several times to become infective . Transmission to definitive hosts occurs when an infected intermediate host falls prey to a suitable predator . The nymphs then migrate to the respiratory tract of the predator , where they attach to the mucosa with two pairs of circumoral chitinous hooklets , develop into adults and then reproduce sexually [2] . In humans , pentastomid larvae typically invade the peritoneum , liver , spleen , mesentery and pleura , causing visceral pentastomiasis [1] . Infection is usually asymptomatic [1]; however , symptomatic [3] , severe [4] and even fatal [5] Armillifer infections have also been reported . Risk factors of this infection include the handling of snakes or snake products , consumption of undercooked snake meat , and possibly snake farming and snake totemism [1] , [6] , [7] . Armillifer armillatus is the second most encountered pentastomid species in humans after L . serrata , with the majority of cases reported from Ghana and the Congo region [8] . Disease due to A . grandis is rare [1] , [8] , the first case having been described in 1966 in the Congo Basin [9] . Ocular pentastomiasis is a rare manifestation . Here , we present four severe cases from the Democratic Republic of the Congo ( DRC ) detected by classical and/or molecular diagnostic methods . We also review all previously published ocular infections and discuss the epidemiology , clinical features , treatment and prevention of this neglected tropical disease .
The Ethics Committee of the St . Raphael Ophthalmological Center in Mbuji Mayi approved the present study . All adult subjects and the parents of child participants provided informed consent . Oral informed consent was obtained due to illiteracy and was documented in the outpatient files . The Ethics Committee approved the use of oral consent . From 2008 to 2012 , we examined approximately 4000 patients with eyesight problems during our ophthalmology missions to the Sankuru district , in the vicinity of Kole , DRC . Overall , we identified four patients with ocular pentastomiasis and associated eye damage . The calculated prevalence was , thus , 0 . 001 among inhabitants with ocular problems . An 11-year-old girl was referred to our outpatient ophthalmology mission , an annual two-week mobile clinic in Kole . The girl had been complaining of pain , redness and decreased vision in the left eye for four months . The visual acuity was severely impaired , with light perception only in all four quadrants of the left eye , while remaining 10/10 in the right eye . On examination , her right eye appeared normal . The left eye showed mild ciliary and conjunctival injection . The cornea was transparent , with some neovascularization . An annulated foreign body was identified in the anterior chamber with peristaltic motion ( Figure 1 ) consistent in morphology with a pentastomatid . The iris was covered by a fibrinous membrane , which also obstructed the pupil , rendering the rest of the eye unsuitable for examination . The eye was markedly hypotensive . The eye was clipped under retrobulbar anesthesia , and the cornea was incised at the limbus with a 15° knife; the parasite was extracted from the anterior chamber ( Appendix Video ) . The parasite was 10 mm long and 2 mm wide , with 31 clearly visible annulations . The organism was surrounded by a transparent capsular-like cuticle , and showed intense peristaltic movements after removal . Two pairs of hooklets were present on each side of the mouthpart , and the parasite was identified morphologically as a larval stage of Armillifer grandis ( Figure 2 ) . Using an 18S rRNA gene marker , a pentastomid-specific PCR [6] was performed on genomic DNA derived from the excised parasite specimen . The resultant amplicon of 377 bp was sequenced and deposited in the GenBank database ( accession no . KM023155 ) . The sequence had 99% similarity to those representing Armillifer armillatus ( GenBank accession no . HM756289; query coverage 94% , 0 gaps ) , A . agkistrodontis ( accession no . FJ607339; query coverage 100% , 1 gap ) and A . moniliformis ( accession no . HM04870; query coverage 100% , 1 gap ) . The present specimen was unequivocally identified as an A . grandis nymph based on size and number of annulations [2]; there was no sequence for A . grandis in any current database . Unfortunately , the patient lost vision in the left eye , despite surgery . This patient reported handling snakes regularly and suffered an eye-splash accident with body fluids from a snake during food preparation approximately six months prior to presentation . A 36-year-old male patient presented to one of our ophthalmology missions in Lokoko , from a village in the Pelenge area , DRC . He has been suffering from visual disturbances in the left eye for 3 years . Symptoms had begun with redness and pain . The pain stopped some time ago , but the visual problem persisted . The right eye was normal with intact vision . Vision in the left eye was severely impaired with light perception only . The conjunctiva , cornea , lens and the anterior chamber were without any detectable abnormalities , but an approximately 15 mm long and 5–6 mm wide parasite reminiscent of a pentastomid nymph was floating freely in the vitreous body , directly behind the lens , encapsulated in a translucent cyst . The annulations could not be precisely counted , but were estimated to be over 20 . There was a remarkable absence of any sign of inflammation in the eye . The retina was detached approximately 270 degrees , with the detached part floating freely . The patient declined surgical intervention and was reexamined one year later . At that time , the retina had completely detached ( 360 degrees ) , and the patient lost all light perception in the affected eye . Surgery was again offered , but the patient did not consent . He could not recall any trauma , but admitted to consume snake meat regularly . A 63-year-old woman from the Pelenge region presented to the same mission as case 2 . She had lost vision in the right eye 3 years prior to presentation . The left eye was assessed as normal during slit lamp and fundus examination . In the right eye , there was an annulated , crescent-shaped parasite in a subretinal localization , positioned nasally from the papilla . There were no signs of retinal detachment . The parasite showed no movement , even upon stimulation by pressing on the eye , and , thus , appeared to be dead . The parasite was ∼8 mm long , 1 . 5 mm wide and had >20 annulations . Based on these findings , the diagnosis of ocular pentastomiasis caused by an unidentified species was made . Due to the localization of the parasite , surgery was not attempted . The patient consumed snake meat regularly . A 25-year-old male presented with blindness and pain in his left eye . Ophthalmologic examination showed a shrunken , non-functional eye . The pupil was nonreactive to light . Using a slit lamp , a vermiform foreign body was seen in the anterior chamber . The parasite was motile and was removed under local anesthesia . This case was previously reported as a photo quiz [10] . Subsequently , the extracted nymph was morphologically and molecularly examined ( unpublished data ) . The parasite was 9 mm long and 2 mm wide , had 30 annulations , and was surrounded by a partially shed transparent cuticle . The 18S rRNA sequence was the same as that isolated from case 1 . Thus , this case was caused by A . grandis ( unpublished data ) . This patient also regularly consumed snake meat . An electronic literature search was conducted using PubMed ( MEDLINE ) . The following Medical Subject Heading terms were used: Pentastomida; Eye infections , Parasitic; Eye/Parasitology . The full texts of the articles selected were reviewed by all authors . The references in all publications were also reviewed to identify additional articles that did not appear in the initial search . Articles in German , French , and Portuguese were also included .
The true number of patients affected by pentastomiasis is unknown , even estimates are lacking . This can be explained by the fact that visceral pentastomiasis is often asymptomatic [1] . However , this disease might be more prevalent than expected in some parts of the world , as autopsy studies in Nigeria and West Malaysia have shown prevalences up to 33–45% in some populations [11] , [12] . Ocular pentastomiasis , though a rare form of the disease , is likely to be detected more readily than the visceral manifestation , because an eye infection produces overt symptoms . The 0 . 001 prevalence among patients with vision problems is a clear indication that pentastomiasis is relatively prevalent in this geographic region where inhabitants frequently consume snakes . Thus , it is likely that ocular pentastomiasis represents only the ‘tip of the iceberg’ of all pentastomiasis forms . Ocular pentastomiasis may thus be regarded as a sentinel form of all forms of pentastomiasis that might otherwise remain undetected . In this region of the DRC , local villagers often find adult pentastomids in the snakes they consume ( Figure 3 ) . Snakes can also be eaten ritually as part of the ju ju rituals in Africa ( e . g . , Benin , Nigeria , Cote d' Ivoire , Cameroon , DRC ) , or Malaysia ( Temuan tribe ) [13] . Epidemiological risk factors and possible routes of transmission were not determined in previously published reports of ocular pentastomiasis . The consumption of poorly cooked snake meat had occurred regularly in all of our four cases . In case 1 , an eye-splashing accident with body fluid from a snake occurred two months prior to the onset of clinical symptoms . No similar accidents had been reported in any other published cases . However , this patient also consumed snakes regularly , so the possible direct transmission to the eye of A . grandis nymphs remains speculative . Interestingly , in case of Linguatula eye infections , ocular trauma was described in two cases prior to the onset of symptoms [14] , [15] . In one case , a fly had hit the eye , and in the other a ball . Three Linguatula patients had kept pet dogs [16] , [17] , [18] . These circumstances may all be coincidental , but in theory , pentastomid eggs might be mechanically transmitted to the conjunctiva , although such direct transmission has yet to be proven . Besides our 4 patients described here , 15 other cases of ocular pentastomiasis have been reported in the literature ( Table 1 and Table 2 ) . Among these 19 cases , 12 were male , 6 were female and in one case gender was not reported . The median age at diagnosis was 14 years . Most cases ( 11/19 ) were reported from sub-Saharan Africa or found in patients originating from this region . The remaining cases were reported from the United States , Europe , India , Israel and South America . All patients from Africa had Armillifer infections , most often relating to A . armillatus . However , some degree of uncertainty surrounds the specific detection of A . armillatus , which closely resembles A . grandis . To this point , we observe that in all but one previous case reports , the parasites were identified by morphological examination only ( the single exception is a Linguatula infection verified by PCR [18] ) . In two of our cases , the larvae were not removed , so that the only diagnostic clue was through fundoscopic examination . However , our other two cases represent the first published unequivocally proven intraocular infections by A . grandis . There is a case of A . grandis infestation extraocularly in the eyelid of a patient from the neighboring region , Kisangani District , Zaire but diagnosis was based exclusively on morphological features of the parasite [19] . Cases of ocular pentastomiasis from outside Africa have been almost exclusively caused by L . serrata . Only one patient was described to have an infection with Porocephalus sp . Given that this particular patient had formerly visited both continents , the parasite in his eye could either be a South-American Porocephalus or an African Armillifer species [20] . The time from the onset of symptoms to diagnosis ( where reported; 15 cases ) varied from 4 days to 36 months . The parasite resided in the ocular adnexa in six cases ( 4 subconjunctival , 1 nasolacrimal and 1 eyelid infestation ) and was found intraocularly in the remaining 13 cases . In nine patients , the pentastomid nymphs were located in the anterior and in four patients in the posterior chamber ( Table 1 . ) . Given the observed motility of the parasite , pentastomid larvae can possibly change their location within the eyeball . A nymph was observed to escape from the anterior to the posterior chamber during an attempt of extraction [18] . There are no published studies assessing antiparasitic treatment of human pentastomiasis; however , the use of ivermectin , praziquantel and mebendazole have been suggested [13] . In cases with ocular localization of the parasites , surgical removal is the treatment of choice . However , in rural areas , where most cases of pentastomiasis occur , medical and surgical services are often unavailable . Parasites were surgically removed in 15 cases ( Table 2 . ) , while in four patients , no intervention or medical therapy was attempted . The optimal timing of surgical extraction is unknown . Considering that the nymphs are viable for approximately two years in the human body [1] , and that dying and antigen-releasing parasites may provoke stronger host immune responses [1] , removal as early as possible seems to be advisable . An early extraction will not only improve the quality of life rapidly but also will prevent further organ damage , as living nymphs are motile and feed on components of the eyeball . Ingested hemoglobin found in parasites from our cases 1 and 4 suggest that pentastomids cause direct damage to intraocular structures . In cases of intraocular localization , nymphs were removed through a corneoscleral/limbal incision . Vitrectomy , iridectomy and lens removal were also performed in two cases of posterior chamber localization . The prevention of pentastomiasis should focus on personal hygiene measures when handling snakes and snake products , such as proper hand washing after snake contact . The consumption of undercooked reptile meat and organs should be avoided . Since livestock production in the Congo Basin rainforests is limited , inhabitants rely on other sources of protein for their diet; consequently , “bushmeat” consumption plays an important role in this region . As populations of the most desired mammals are being increasingly exploited , people in rural areas predictably turn to consuming more reptiles [27] . On the other hand , developed countries increasingly import reptiles from tropical countries as pets , which represents a potential threat to the public . It should be kept in mind that pet owners , as well as veterinarians , zoo and snake farm workers can be infected via respiratory secretions or faeces . Nymphal linguatuliasis can be prevented by hand washing after contact with dogs or their excreta/secreta [28] . Pentastomiasis is usually an asymptomatic infection . However , when pentastomid larvae occur in the eye , the consequences can be devastating . Our case series suggests that in central DRC , this disease is more common than previously thought . Case reports from Liberia [22] , [24] , [29] indicate a similar situation . Here we conclude from a review of all reported cases in the medical literature , and our own experience , that extraocular localization of pentastomid nymphs has good prognosis after surgical treatment , while intraocular parasites usually cause permanent visual damage , despite surgical intervention . The early removal of intraocular nymphs in a well-equipped medical center seems to be crucial to conserve sight [18] , while cases in rural sub-Saharan regions with no available medical services have a poor prognosis . Interestingly , A . grandis , an otherwise very rarely encountered pentastomid species , was responsible for at least two of the four cases described here . Although the pathogenesis of ocular pentastomiasis is unknown , pentastomid nymphs likely reach the eye via the bloodstream , similar to cases of neurocysticercosis after the oral ingestion of infective Taenia solium eggs [30] . Direct contamination , although unlikely , cannot been ruled out , particularly in cases involving the ocular adnexa after traumatic injuries . Preventive measures should include the proper cooking of snake meat before consumption and hand washing after handling snakes . Further epidemiological studies in this region of relatively high prevalence are required to estimate disease burden , study the pathogenesis and evaluate therapeutical options of this seriously neglected disease . | Ocular pentastomiasis is a severely neglected parasitic disease . Most human infections are linked to the consumption and/or handling of snakes . Here we present a series of cases from a remote area of the Democratic Republic of the Congo . Two patients had an infection with Armillifer grandis . As livestock production in Congo Basin countries is limited , people consume “bushmeat” from wild animals , including snakes . On the other hand , developed countries increasingly import reptiles from tropical countries as pet animals , which represents a potential threat to the public . Fifteen human cases of ocular pentastomiasis have been described in the literature; however , this disease may be under-reported in rural areas of the tropics . Here , we review human cases as well as key aspects of the epidemiology , clinical features , treatment and prevention of this forgotten disease . | [
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] | 2014 | Ocular Pentastomiasis in the Democratic Republic of the Congo |
Buruli ulcer ( BU ) is a subcutaneous skin disease listed among the neglected tropical diseases by the World Health Organization ( WHO ) . Early case detection and management is very important to reduce morbidity and the accompanied characteristic disfiguring nature of BU . Since diagnosis based on clinical evidence can lead to misdiagnosis , microbiological confirmation is essential to reduce abuse of drugs; since the anti-mycobacterial drugs are also used for TB treatment . The current WHO gold standard PCR method is expensive , requires infrastructure and expertise are usually not available at the peripheral centers where BU cases are managed . Thus one of the main research agendas is to develop methods that can be applied at the point of care . In this study we selected aptamers , which are emerging novel class of detection molecules , for detecting mycolactone , the first to be conducted in a BUD endemic country . Aptamers that bind to mycolactone were isolated by the SELEX process . To measure their affinity and specificity to mycolactone , the selected aptamers were screened by means of isothermal titration calorimetry ( ITC ) and an enzyme-linked oligonucleotide assay ( ELONA ) . Selected aptamers were assessed by ELONA using swab samples from forty-one suspected BU patients with IS2404 PCR and culture as standard methods . ROC analysis was used to evaluate their accuracy and cutoff-points . Five out of the nine selected aptamers bound significantly ( p< 0 . 05 ) to mycolactone , of these , three were able to distinguish between mycolactone producing mycobacteria , M . marinum ( CC240299 , Israel ) and other bacteria whilst two others also bounded significantly to Mycobacterium smegmatis . Their dissociation constants were in the micro-molar range . At 95% confidence interval , the ROC curve analysis among the aptamers at OD450 ranged from 0 . 5–0 . 7 . Using this cut-off for the ELONA assay , the aptamers had 100% specificity and sensitivity between 0 . 0% and 50 . 0% . The most promising aptamer , Apt-3683 showed a discernible cleavage difference relative to the non-specific autocatalysis over a 3-minute time course . This preliminary proof-of-concept indicates that diagnosis of BUD with RNA aptamers is feasible and can be used as point of care upon incorporation into a diagnostic platform .
Buruli ulcer Disease ( BUD ) has been listed among neglected tropical diseases , the causative agent is an ecological pathogen known as M . ulcerans [1 , 2] . Among mycobacterial diseases , it is the third utmost after tuberculosis and leprosy . It has been recounted in more than 30 tropical countries . The main problem however , is concentrated in West Africa where it has assumed the second most imperative mycobacterioses [3] . It is characterized by widespread debilitation of soft tissues and skin with the development of huge ulcers typically located on body extremities [4 , 5] . Although mortality is low , indisposition and resulting functional disability can be severe [6–9] . As a result , the societal and financial burden of BUD can also be high , especially in poor rural areas . The mode of pathogen transmission and host immune response to infection is not fully understood; hence current control strategy is centered primarily on early identification of cases , antimycobacterial administration and wound management . The present World Health Organization ( WHO ) treatment includes everyday administration of oral and intramuscular streptomycin and rifampicin respectively for eight weeks . Surgical removal of foreign materials and dead tissues from progressing wounds and/or skin grafting , may be required to prevent secondary infections , enhance healing , and to rectify disfigurements . [10] . The administration of antimycobacterial drugs has made laboratory validation of clinically assumed cases very critical for treatment of BU . Even though the overall observation is that diagnosis centered on clinical decision only is satisfactory , instances of wrong diagnosis have been described [11–13] . Due to cost , infrastructural and expertise demand , the current WHO recommended gold standard diagnostic protocol ( IS2404 detecting PCR ) has rendered bacteriological validation to a quality control means for diagnosing BUD . There is therefore the need to research into development of simpler methods that can be applied at the point of care . A distinguishing feature of M . ulcerans amongst human mycobacteria , is the secretion of mycolactone [14] , the virulent factor responsible for the pathogenesis of the disease . Intact mycolactone has been found to be present in biological materials collected from all forms and stage of BUD [15] . Furthermore , there is proof from mouse and human experimentation that mycolactone is detectable in peripheral blood [16] and has been postulated as a useful marker for diagnosis . However , the chemical nature of mycolactone as a poor immunogenic lipid molecule has impeded efforts to produce an immunodiagnostic based detection of mycolactone . An aptamer is a nucleic acid molecule ( single-stranded DNA or RNA ) that binds to its target with high specificity and affinity [17 , 18] . Aptamers do not carry genetic information but work through affinity binding to their target [19] . They interact with their targets via secondary and/or tertiary structures [20] . Aptamer , upon binding to its target via the binding domain , allosterically transfers stability to other component of the structure . Aptamers are raised in vitro by a PCR-based , iterative procedure called systemic evolution of ligands by exponential enrichment ( SELEX ) . Likened to protein antibodies , aptamers have several benefits like quick synthesis , high affinity , low-temperature sensitivity , can be manufactured on a large scale and can be easily adjusted biochemically [21 , 22] . Aptamers have been applied in many areas such as biosensor , medical diagnosis and as a therapeutic tool [23 , 24] . To date , this novel , profound and explicit class of recognition molecules , has previously not been explored for BU diagnosis . In this proof of concept study we explored the feasibility of raising aptamers against mycolactone as a simpler diagnostic tool for BUD .
The protocol for this study was reviewed and ethical approval granted by the Institutional Review Committee of the Noguchi Memorial Institute for Medical Research ( NMIMR ) , of the University of Ghana . Consent was sought from grown-up participants and legal guardians of all minors . A clinical M . ulcerans strain ( NM209 ) was used as an in-house reference . This strain was isolated from skin lesion of a female patient in December , 2009 . This strain was sub-cultured into Middlebrook 7H9 broth enriched with 10% OADC ( Sigma-Aldrich , MO , USA ) and incubated at 32°C . The third week culture was further sub-cultured into six more Middlebrook 7H9 broth for four weeks into mid-log phase with intermittent shaking . The bacteria cells were harvested , dried , and weighed . Chloroform-methanol ( 2:1 , vol , /vol ) and a magnetic stirrer were used to stripped lipids from the cell wall at 4°C for 24hrs [25] . A rota-evaporator was then used to dry the organic phase after which it was re-suspended in ice-cold acetone . This was then incubated for 20hrs at -20°C after which it was loaded on a TLC silica gel plate with chloroform , methanol and water as mobile phase ( 90:9:1 ) . A yellow band , which is indicative of mycolactone was scraped and eluted from the silica using 2/1 chloroform , methanol ( v/v ) [26] . Synthetic mycolactone was used as control . Purified mycolactone was re-suspended in ethanol after solvent evaporation . UV absorption was used to determine the concentration of the resulting solution [27] . The initial aptamer library template and primers were synthesized by IDT ( Coralville , IA ) as single-stranded DNA . The library was then primer extended to provide double-stranded DNA ( dsDNA ) using Titanium Taq DNA polymerase from Clontech ( Mountain View , CA ) . The extracted mycolactone and bacterial cell protein extract were used as target and counter- target respectively . For a given generation of the library , RNA was transcribed from the previous dsDNA with AmpliScribe T7 Transcription kits from Epicentre ( Madison , WI ) and purified using a 10% denaturing polyacrylamide gel electrophoresis ( PAGE ) . The purified RNA was combined with selection buffer , which was then diluted to 1X concentration ( 1X PBS [pH 7 . 4] and 10 mM MgCl2 ) for negative selection . After incubation , non-cleaved RNA was separated from cleaved RNA using 10% denaturing PAGE . Recovered non-cleaved material was combined with counter-target and buffer , target and buffer , or buffer alone depending on the selection step , incubated , and partitioned on 10% denaturing PAGE . Recovery and another selection step was implemented . cDNA was then generated from eluted post-selection library using SuperScript II Reverse Transcriptase ( Life Technologies; Carlsbad , CA ) , then PCR-amplified with Titanium Taq DNA polymerase ( Clontech; Mountain View , CA ) to complete the round of selection . Selection steps for a particular round varied depending on what condition needed to be improved based on results from the prior round . After selections were completed , cDNA of the resultant enriched library was PCR amplified , transcribed , and then divided into three samples , with one exposed to the selection buffer alone , one exposed to the counter-target protein in selection buffer , and the last exposed to the target ( mycolactone ) in selection buffer . Material that cleaved after binding to elements of its treatment condition was recovered , reverse transcribed , PCR amplified and sequenced . The Illumina MiSeq system ( San Diego , CA ) was employed to sequence the parallel library product after the selections to generate single-end reads . Bioinformatics analysis of the sequencing data identified candidate aptamer molecules . The deep sequencing and subsequent data analysis reduced the traditional approach of performing a large number of selections , which may introduce error and bias due to the screening process [28] . Sequence family construction focused on motif presence . Three libraries were collected from the parallel assessment: the positive target-exposed library , the buffer-only negative library , and the counter-target-exposed library . All libraries were analyzed to discover any sequences that have yet to be removed during a negative- or counter-selection step , but still have affinity for both the target and counter-target . Sequence families with lower numbers but a higher representation in the positive population were identified as potential candidates [29] . To estimate the affinity of the RNA aptamers to mycolactone , kinetic analysis were done with isothermal titration calorimetry ( VP-ITC ) , ( MicroCal , LLC , MA ) . The first injection was disregarded . The models independent and blank ( constant ) were used for all runs . Running conditions consisted of 33 injections of 3ul at 25°C with 70ul of aptamer and 300ul of mycolactone . Control titrations ( mycolactone titrated into buffer ) were subtracted from the data before fitting . The integration width used in all cases was 180s . The evaluation of the data was done using NanoAnalyze software version 3 . 1 Specific aptamers selected by proprietary bioinformatics were checked for their binding affinity to mycolactone using ELONA assay [18 , 30] . Briefly , each RNA aptamer was biotinylated by IDT ( Coralville , IA ) . Fifty microliters ( 50ul ) of mycolactone in 10mls of NaCO3 buffer were aliquoted into a 96-well microtiter plates at 4°C and left overnight . A 5% fat-free milk was used to block the solution at 4°C for 1 hour , TBS buffer was then used to wash the wells four times . 500 nM of biotinylated aptamers were aliquoted into each well and incubated at room temperature for 2 hours . Subsequently , the wells were washed again with TBS buffer . Streptavidin-horseradish peroxidase conjugate ( KPL ) was diluted 1:15 000 in TBS buffer , and 100ul was aliquoted to each well . The plates were incubated at 37°C for 2 hours and washed as previously described . Next , 50ul of Turbo-3 , 3’ , 5 , 5’-tetramethylbenzidine ( TMB , Pierce ) was aliquoted into individual wells and incubated at 37°C for 15 minutes . The reaction was stopped by the addition of 50ul of 1M H2SO4 and the resulting complexes were measured at absorbance 450 nm using a MultiSkan Go plate reader ( Thermo Scientific ) . To evaluate their specificity , all the five RNA aptamers were assessed in an ELONA assay to determine their ability to distinguish among several bacterial lysate mostly associated with wounds and ulcers . The bacteria include: Mycobacterium bovis BCG , Staphylococcus aureus , Pseudomonas aeruginosa , Proteus mirabilis , Mycobacterium smegmatis , Mycobacterium tuberculosis , Mycobacterium ulcerans , Mycobacterium marinum ( CC240299 , Israel ) TB H3Ra , Enterobacter , Klebsiella pneumonia and Escherichia coli . Two hundred microliters ( 200ul ) of bead-beaten lysates from each bacteria culture was used . Equal amount of these bacterial lysate were aliquoted into microtitre plates using M . ulcerans as the positive control . To evaluate the use of the selected RNA aptamers as a recognition technique , the aptamers were tested in a case-controlled study . Swabs and FNA specimen were taken from forty-one BU suspected patients from three districts in the West and South districts of the Greater Accra region . Specimens were collected using WHO prescribed standard procedures [2] . Swab samples were taken from ulcerative lesions by swabbing the surface underneath the undermined edges . In patients with pre-ulcerative lesions , FNA specimen was taken from the core of the lesion [31] . The collected swabs were put into 15mL falcon tubes with transport medium that covers the whole tip of the swab . On the other hand , FNA samples were drained into an Eppendorf tube containing 500μL phosphate buffered saline ( PBS ) . For processing and analysis , specimens were sent to the Noguchi Memorial Institute for Medical Research ( NMIMR ) . The Institutional Review Committee of the NMIMR provided the ethical approval for the study . Consent was sought from grown-up participants and legal guardians of all minors . The samples were analyzed by PCR for the IS2404 sequence repeat , culture , and ELONA assay . An ELONA was done to determine if the aptamers will be able to identify mycolactone present in the samples . Culture and IS2404 PCR results from the same samples were used as standard . Specificity and sensitivity was compared between the ELONA assay , IS2404 PCR and culture results . The most specific aptamer Apt-3683 was tested for its ability to detect differences in percentage cleavage in the presence and absence of mycolactone . Specific Apta-3683 ( 0 . 5mM ) mediated by the presence of mycolactone ( 100 mM ) over a 3 minute time course was done . The cleavage percentages were generated by running sample against 20/100 DNA Ladder from IDT ( Coralville , IA ) , stained with Gel Star ( Lonza; Basel , CH ) and photo-documented under UV trans-illumination . Reaction conditions , volumes and the subsequent PAGE analysis also included the additional time points of 120 seconds and 180 seconds . For all the ELONA binding tests , an aptamer alone control was used for each plate . The average was taken and subtracted from the individual well to get rid of background noise . Individual aptamers or aptamer-mycolactone/bacterial lysate mixture were tested in duplicate . This was also averaged and used to calculate the standard deviation . NanoAnalyze software was used to calculate for the dissociation constant . The ROC curve analysis was used to determine the diagnostic accuracy of the aptamer based test ( GraphPad Prism 5 . 04 , GraphPad Software , Inc . )
Mycolactone ( 0 . 2mg/ml ) in 0 . 5 ml ethanol was obtained of which 1000pmoles were used as target in the aptamer selection process . After seven rounds of selection cycle , the library responded to the target at a higher rate than the counter-target or buffer-only conditions . Sequence family construction focused on motif presence . In all , 197007 sequences were analyzed from the positive target-exposed library . From this set of data , 6859 sequence families were constructed containing between 10 and 583 members each . The M-fold Zuker algorithm dependent program was used to predict the secondary structure of a given candidate [32] to ensure that the aptamer can structurally shift to an active form . Fig 1 shows library enrichment over the course of SELEX process indicating percentage cleavage for positive , negative and counter-selections . Nine RNA aptamers were selected , of these five aptamers designated APT-3659 , APT-0017 , APT-2039 , APT-3683 and APT-0001 were identified to be specific to mycolactone . Out of the nine RNA aptamers screened against mycolactone , five bound significantly ( p , 0 . 0035 ) displaying high OD450 readings and were selected for additional analysis . Fig 2 show how the various isolated RNA aptamers bound to mycolactone . To evaluate the binding kinetics of selected aptamers , isothermal titration calorimetry method was used to obtain the constant of dissociation ( kd ) values for each aptamer . The aptamers gave kd results in the lower micro-molar range from 1 . 59–73 . 0 uM . Fig 3 depicts isothermal titration calorimetry thermogram of the aptamers . They all gave a sigmoidal curve indicative of strong affinity of the aptamers to mycolactone . The specificity of the RNA aptamers were tested by lysates prepared from bacteria mostly found inhabiting wounds and ulcers and were checked for their ability to bind to aptamers . The five selected aptamers had varied binding abilities even though all aptamers were able to recognize M . ulcerans lysates , two aptamers , however , bound to the lysates of M . smegmatis . Table 1 shows ELONA results of RNA aptamers tested on various bacterial lysates . ( Table 1 ) . Additionally , for sensitivity testing , decreasing concentrations of the most promising aptamer ( 0–100mM ) was used to determine the least concentration at which the aptamer would cleave in the presence of mycolactone ( 1μM ) as shown in supplementary 1 ( S1 Fig ) . To assess the aptamers capacity to detect M . ulcerans infection using clinical swab samples , the aptamers were tested in forty-one clinical swab samples in an ELONA assay with culture and IS2404 PCR as standard methods . Fourteen swab samples tested positive for both culture and IS2404 PCR whilst positivity observed among the aptamers ranged from one to seven ( Table 2 ) . ROC curve analysis with a 95% confidence interval observed among the aptamers at OD450 ranged from 0 . 5–0 . 7 . Using this cut-off for the ELONA assay , the aptamers had 100% specificity and sensitivity between 0 . 0% and 50 . 0% . Table 2 shows the aptamer’s ability to detect M . ulcerans infection using clinical swab samples in an ELONA assay with culture and IS2404 PCR as standard methods , while Table 3 indicate ELONA results of five RNA aptamers tested as a detection reagent for BU from clinical samples with culture and IS2404 PCR as standard . Fig 4 shows the most significant and specific aptamer Apt-3683 showing a discernible cleavage difference relative to the non-specific auto-catalysis that occurs in the presence and absence of mycolactone or buffer alone over time a 3 minute time course .
The current study used the allosteric ribozyme design as a template to select aptamers . The aptamers were selected with mycolactone as a target to be used as a potential M . ulcerans detection assay . In all , nine individual RNA aptamer candidates were isolated from which five bound significantly to mycolactone . Further characterizations of the aptamers indicate aptamers had a high affinity to mycolactone , moreover , the selected aptamers had constants of dissociation in the lower micro-molar range . The selected aptamers were further shown to discriminate between mycolactone producing mycobacteria , M . marinum ( CC240299 , Israel ) and other gram positive and gram negative bacteria normally present in wounds and ulcers [33] . The binding ability of the aptamers were tested against the various bacterial lysates and it was observed that the aptamers were able to discern among most of the bacteria and mycolactone producing M . ulcerans . However , Apt-0017 and apt-0001 , also bound significantly to M . smegmatis . This may be due to the fact that aptamers bind preferentially to functional parts of proteins , and thus a functional protein within the binding domain of M . smegmatis may be interacting with the two aptamers . It is interesting to note that these two aptamers also bound significantly to M . ulcerans . The aptamer-based assay was used in a case control study and had a sensitivity of 50% and a specificity of 100% . This is promising for development of the aptamers as recognition molecules for diagnosing BU . The aptamer-based test had a sensitivity of 50% which is comparable to that of microscopy and culture [6 , 34] whilst the specificity is comparable to that of the IS2404 PCR [35 , 36] . In the current study , it was further observed that the aptamer-based detection assay did reasonably well in patients with active BUD . Compared to routine diagnostic tests , aptamer-based detection assay have better stability under a varied circumstances , and can be used repetitively . They can thus , serve as detection molecules for the development of better diagnostic assays [37] . Furthermore , the current study and other experimentations have revealed that aptamers can be synthesized chemically , this will allow for the aptamers to be manufactured rapidly on a large scale [37] . A limitation is the absence of a speedy readout , as it takes many hours to finish an ELONA , in addition to antigenic cross-reactivity . The optimization of this aptamer-based detection assay will improve its sensitivity and reduce the effect of cross-reactivity , this can be done by truncation of the present 88-mer mother aptamers to just the necessary sequences needed for binding to the target , and this can also improve the kinetics of the aptamers [38] . This will further reduce cost of synthesizing the aptamers and prevent non-specific binding . Furthermore , changing the communication module with renowned modules like flavin mononucleotide ( FMN ) and theophylline communications modules can improve the sensitivity of the assay . The current study has thus revealed that it is practical to select specific aptamers to M . ulcerans target and identify this target in patients with active form of the disease . However , further optimization is required to improve the sensitivity and hence performance outcomes , followed by justification in bigger a study using clinical specimen at different stage of the disease . This will form the basis for aptamers to be included in point-of-care diagnostic platforms . | Buruli ulcer disease is a subcutaneous necrotizing skin disease caused by ecological Mycobacterium ulcerans . The availability of antibiotic treatment makes it imperative for early detection of the disease to avoid the disfiguring and morbid effects associated with the disease . Currently available detection methods include: microscopy for detecting acid-fast bacilli , culture to isolate viable organism , and PCR for detecting pathogen specific DNA which is usually IS2404 and histopathology . However , most of these methods have major drawbacks , they are done in referenced laboratories and cannot be used in the field where the disease is most prevalent . M . ulcerans produces a lipid chemical called mycolactone , which is accountable for the virulent nature of the disease and has been postulated as a diagnostic target . The lipid nature of the mycolactone however makes it difficult for the body to produce antibodies against it . We therefore designed nucleic acid detection molecules that have high affinity to its target called aptamers . This aptamers were selected to detect mycolactone and hence M . ulcerans . The selected aptamers were tested with clinical samples and concluded preliminarily that , aptamers can be raised against mycolactone and this be used as point of care upon optimization and subsequent incorporation into a diagnostic platform . | [
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] | 2016 | RNA Aptamer That Specifically Binds to Mycolactone and Serves as a Diagnostic Tool for Diagnosis of Buruli Ulcer |
Visceral leishmaniasis is caused by the protozoan parasites Leishmania infantum and Leishmania donovani . This infection is characterized by an uncontrolled parasitization of internal organs which , when left untreated , leads to death . Disease progression is linked with the type of immune response generated and a strong correlation was found between disease progression and serum levels of the immunosuppressive cytokine IL-10 . Other studies have suggested a role for B cells in the pathology of this parasitic infection and the recent identification of a B-cell population in humans with regulatory functions , which secretes large amounts of IL-10 following activation , have sparked our interest in the context of visceral leishmaniasis . We report here that incubation of human B cells with Leishmania infantum amastigotes resulted in upregulation of multiple cell surface activation markers and a dose-dependent secretion of IL-10 . Conditioned media from B cells incubated with Leishmania infantum amastigotes were shown to strongly inhibit CD4+ T-cell activation , proliferation and function ( i . e . as monitored by TNF and IFNγ secretion ) . Blockade of IL-10 activity using a soluble IL-10 receptor restored only partially TNF and IFNγ production to control levels . The parasite-mediated IL-10 secretion was shown to rely on the activity of Syk , phosphatidylinositol-3 kinase and p38 , as well as to require intracellular calcium mobilization . Cell sorting experiments allowed us to identify the IL-10-secreting B-cell subset ( i . e . CD19+CD24+CD27- ) . In summary , exposure of human B cells to Leishmania infantum amastigotes triggers B cells with regulatory activities mediated in part by IL-10 , which could favor parasite dissemination in the organism .
Leishmaniasis is an infection caused by protozoan parasites of the genus Leishmania and is one of the most significant neglected tropical diseases , with 350 million people in 98 countries worldwide at risk of developing one of the forms of the disease [1] . Visceral leishmaniasis ( VL ) is the most severe form of the disease and it represents nearly 40 , 000 deaths per year [1] . VL is characterized by an uncontrolled parasitization of organs , such as spleen , liver and bone marrow , and is caused by the species Leishmania infantum ( L . infantum ) ( known as L . chagasi in South America ) and L . donovani . All parasites of the genus Leishmania are obligate , intracellular protozoa that infect cells of the macrophage-dendritic cell lineage of their vertebrate hosts ( primarily macrophages ) [2 , 3] . The parasite exists under two distinct morphologic forms , i . e . either as motile promastigotes within the alimentary canal of their phlebotomine sandfly vector or as nonmotile amastigotes that reside within phagolysosomes of mammalian mononuclear phagocytes . Infection of the mammalian host is initiated when the female sandfly regurgitates infectious promastigotes during its blood meal . Promastigotes are quickly internalized by tissue phagocytes recruited to the site of infection . Following phagocytosis , promastigotes are engulfed in phagolysosomes , where they transform into the non-motile intracellular amastigotes . Thereafter , amastigotes replicate within acidic phagolysosomes , eventually lysing the cell and freeing themselves to interact with adjacent cells . According to a recent report , amastigotes could also be transferred directly to other target cells via LAMP-rich parasitophorous extrusions [4] , exploiting an alternative mechanism of transmission that would minimize exposure to the immune system . In the context of L . major infection in the BALB/c mouse model , the non-healing and disseminating form of leishmaniasis has been associated with a Th2 immune response , which is dominated by IL-4 ( reviewed in [5] ) . Multiple reports suggest however that such polarized immune responses are not observed in humans and that elevated levels of interferon gamma ( IFNγ ) can be found in lesional tissues even during the acute phase of the disease [6–8] . Moreover , elevated levels of IL-10 in blood and tissues of VL patients are a better correlate of susceptibility than IL-4 . The complexity of the immune response towards Leishmania and the extent of the cytokine network involved in humans is underscored by the wide variety of manifestations of the disease [9 , 10] . Studies in mice suggest that IFNγ-driven Th1 immune responses and IL-12 secretion play an important role for the control of the parasite and development of acquired immunity [11 , 12] . The overall immune response in the context of leishmaniasis requires the maintenance of a delicate balance between CD4+ and CD8+ T cells that are required for optimal cytokine secretion . These two cell populations play distinct but cooperative roles in disease resolution with CD4+ T cells being more involved in primary infection while CD8+ T cells are more important in secondary immune responses [13] . Evidence from mouse studies indicates that B-cell activation leads to disease exacerbation [14 , 15] . Furthermore , it has been shown that B-cell-deficient C57BL/6 mice are highly resistant to VL [16] . Very high titers of Leishmania-specific antibodies can be found in the serum of VL patients soon after infection but before the appearance of cellular immunological abnormalities [17 , 18] . This humoral response can persist for many years following treatment , thus suggesting a role for antibody-mediated immunity in protection against reinfection [19] . However , a strong correlation was found between seropositivity and progression to clinical diseases in healthy individual [20] , suggesting a role for antibody production in disease pathogenesis . Indeed , some signs of B-cell dysfunctions are observed in human VL , including hypergammaglobulinemia and the presence of non-specific polyclonal and/or autoimmune antibodies [21–24] . In cutaneous leishmaniasis , polyclonal B-cell activation has been detected in response to L . major infection [25 , 26] and antibody production and antigen presentation by B cells have been shown to exacerbate disease also in L . major infection [27] . The importance of B cells for the development of a Th2 response and the susceptibility in BALB/c mice infected with L . major have been reported previously [27] . It has also been well established that the ability of B cells to direct the immune response in BALB/c mice toward a Th2 phenotype ( associated with a non-healing disease ) was dependent upon their capacity to present antigens to T cells rather than upon their production of specific IgG antibodies [27 , 28] . The regulatory cytokine IL-10 has repeatedly been implicated as an immunosuppressive factor in both human and experimental leishmaniasis ( i . e . murine ) . For example , it has been reported that IL-10-deficient BALB/c and C57BL6 mice are highly resistant to L . donovani infection and blockade of IL-10 receptor in wild-type mice leads to control of the infection [29] . More recently , Deak and co-workers have shown that B cells are crucial for visceralization of L . donovani in the susceptible Balb/c mice model but that such effect is independent of IL-10 [14] . However the exact role of IL-10 secretion by B cells in the visceralization of L . donovani in humans still remains undefined . It has been shown that patients with an advanced state of disease display elevated levels of IL-10 mRNA in lesional tissues [6–8] and high amounts of IL-10 in serum [30–33] . Moreover , neutralization of IL-10 promotes clearance of the parasite in splenic aspirate cells from patients with VL [34] . Recent studies conducted in humans and mice have revealed that IL-10 can be produced by different cell types following Leishmania infection , including regulatory T cells ( Tregs ) , Th1 cells [32 , 35–37] , CD8+ T cells [38] , natural killer cells , regulatory dendritic cells , macrophages [39] , neutrophils [40] and B cells [14 , 41] . IL-10 acts as a multifactorial cytokine in human infectious diseases . By interfering with both innate and adaptative responses , it contributes to favourable conditions for the persistence of microbes and chronic infections . On the other hand , it prevents the development of immunopathological lesions that result from an exacerbated immune response to acute and chronic infections that can lead to deleterious tissue lesions [42] . Therefore , although high levels of IL-10 found in VL might help to diminish immunopathologies and tissue damage , the immunosuppressive effect of IL-10 might promote parasite replication , dissemination and disease progression . Depending on the species and the disease studied but also on the triggering signals used , various populations of IL-10-secreting B cells with regulatory functions have been described . Yanaba and co-workers have described a rather unique murine CD1dhiCD5+ B-cell subset that is able to efficiently control T-cell-dependent inflammatory responses through IL-10 secretion [43] . Although a similar B-cell subset has been rarely described in humans , patients suffering from Chagas disease were shown to have a slightly higher frequency of CD1d+CD5+CD19+ B cells that produce IL-10 and an increased frequency of circulating CD1d+CD5+CD19+ B cells was correlated with inhibition of Th17 responses in tuberculosis patients [44] . A direct relationship between the CD1d+CD5+ regulatory B cells found in mice and humans must not necessarily be assumed however , as mice only express CD1d while humans can express other genes of the CD1 family ( e . g . CD1a , b , c and e ) [45] . The role of regulatory B cells in the development of autoimmune diseases and chronic infections in humans has been addressed previously and it was established that a CD24hiCD38hi B-cell subset that secretes high amounts of IL-10 upon stimulation can regulate T-cell functions and is positively associated with renal graft acceptance [46 , 47] . Lastly , a human CD24hiCD27+ B-cell subpopulation was described as having regulatory properties upon stimulation by CPG/anti-CD40 and elevated frequencies of these cells were associated with various autoimmune diseases [48] . There seems to be no single marker to identify regulatory B cells and different subtypes of regulatory B cells might exist depending of various factors such as the disease and the nature of stimulation . As already mentioned , B-cell responses and IL-10 are involved in the pathogenesis of human VL . Given that colonization of secondary lymphoid organs occurs during VL and B cells are found at high concentrations in these tissues , it can be postulated that Leishmania parasites can interact with B cells . We investigated whether human B cells isolated do secrete IL-10 in response to L . infantum amastigotes . We assessed also the phenotypic characteristics of IL-10-secreting B cells and their capacity to modulate T-helper cell functions .
The current study was approved by the Institutional Bioethics Committee ( IBC ) from the Centre Hospitalier Universitaire ( CHU ) de Québec—Pavillon CHUL . Clinical samples from tonsillar tissues were obtained from minor patients in accordance with the guidelines of the IBC and parents/guardians provided a written consent on behalf of all minor participants . Human primary CD4+ T cells were obtained from peripheral blood mononuclear cells ( PBMCs ) purified from the blood of healthy subjects in accordance with the guidelines of the IBC . A written ethics board-approved informed consent form was obtained from each donor for the CD4+ T cells . Tonsillar tissues were obtained from 2- to 4-year old patients undergoing elective tonsillectomy ( due to physiological reasons such as snoring , difficulty to swallow and obstructive sleep apnea ) at the CHU de Québec—Pavillon CHUL . Briefly , tonsillar tissues were dissected into small pieces and resuspended in endotoxin-free phosphate-buffered saline ( PBS ) ( Sigma , Oakville , ON ) containing penicillin ( 100 U/ml ) ( Wisent , St-Bruno , QC ) , streptomycin ( 100 μg/ml ) ( Wisent , St-Bruno , QC ) , Fungizone/Amphotericin B ( 2 . 5 μg/ml ) ( Gibco , Burlington , ON ) and Primocin ( 100 μg/ml ) ( InvivoGen , San Diego , CA ) . The tissue suspension was incubated with collagenase D ( Roche Diagnostic , Montreal , QC ) at a final concentration of 2 mg/ml for 45 min at 37°C . Next , partially digested tissue was processed with a GentleMacs device ( Miltenyi Biotec inc . Auburn , CA ) in a C tube using standard “program B” . DNAse I ( Roche Diagnostic , Montreal , QC ) was then added at a final concentration of 100 U/ml and the suspension was further incubated for 15 min at room temperature . Cell suspension was finally processed with the GentleMacs device ( Miltenyi Biotec Inc . , Auburn , CA ) in a C tube using standard program “m_spleen_04” . The resulting cell suspension was diluted in a solution of PBS supplemented with 2mM EDTA and 0 . 5% BSA , filtered first through a 100-μm nylon mesh cell strainer ( Partec , Swedesboro , NJ ) and then through a 30-μm nylon mesh cell strainer ( Partec ) , before being finally separated using a StemSep human B cell enrichment kit ( StemCell Technologies , Vancouver , BC ) . Purity of human B cells was assessed by the expression of CD19 by flow cytometry , and populations with >95% of purity were used for the experiments . Isolated B cells ( CD19+ ) were maintained at a density of 1 × 107 cells/ml in RPMI-1640 medium ( Gibco Life Technologies , Life Technologies , Burlington , ON ) supplemented with 10% fetal bovine serum ( FBS ) ( Wisent , St-Bruno , QC ) , penicillin ( 100 U/ml ) , streptomycin ( 100 μg/ml ) and Primocin ( 100μg/ml ) . Various B-cell subsets were isolated with a BD influx high-speed cell sorter based on their expression of CD24 , CD27 and CD38 . Alternatively , CD27- and CD27+ cells were separated using CD27 Microbeads ( Miltenyi Biotec Inc . ) . The L . infantum strain clone 1 ( MHOM/MA/67/ ITMAP-263 ) used in this study and its maintenance as axenic amastigotes have been described previously [49] . Briefly , axenically-grown amastigote forms of L . infantum were maintained at 37°C under a 5% CO2 atmosphere by biweekly sub-passages in MAA/20 culture medium in 25 cm2 flasks . MAA/20 consists of modified medium 199 with Hank’s salts ( Gibco , Burlington , ON ) , supplemented with 0 . 5% soybean tryptocasein ( Pasteur Diagnostics , Marne la Coquette , France ) , 15 mM D-glucose , 5 mM L-glutamine , 4 mM NaHCO3 , 0 . 023 mM bovine haemin and 25 mM HEPES ( all purchased from Sigma , Oakville , ON ) at a final pH of 6 . 5 . Axenically grown amastigotes show morphological , biochemical and biological characteristics similar to those of in vivo amastigotes [49] . Purified human B cells were cultured at a final concentration of 1 x 107/ml in RPMI-1640 culture medium containing L-glutamine supplemented with 10% FBS , penicillin ( 100 U/mL ) , streptomycin ( 100 μg/mL ) and Primocin ( 100 μg/ml ) . Cells were incubated for 24 h with axenic amastigotes at a 3:1 ratio ( unless otherwise indicated ) . Cells and/or cell-free supernatants were harvested for further analysis . In some experiments , cell culture inserts with a pore size of 1 μm ( BD Falcon , BD Biosciences , Mississsauga , ON ) were used to separate amastigotes from human B cells . Freshly isolated B cells were exposed to L . infantum amastigotes for 24 h after which cells were incubated for 20 min in PBS supplemented with 200 mM D-galactose ( osmolarity was adjusted to 317 mOsml/l by reducing NaCl concentration accordingly ) to detach the parasites from B cells . Control unexposed B cell cultures were also washed with D-galactose-supplemented PBS . Cells were then centrifuged at 300 x g , resuspended in the same solution and filtered through a 30-μm nylon mesh cell strainer ( Miltenyi Biotec Inc . , Auburn , CA ) , washed again and finally resuspended at 1 x 107/ml in PBS containing 2 mM EDTA and 0 . 5% BSA ( staining buffer ) . To monitor intracellular expression of IL-10 , cells were incubated with GolgiPlug ( BD Biosciences ) during the last 5 h of stimulation along with ionomycin ( 1 μg/ml; Sigma , Oakville , ON ) and phorbol 12-myristate 13-acetate ( PMA ) ( 50 ng/ml; Sigma , Oakville , ON ) . Viability of B cells was assessed by staining with the Fixable Viability Dye eFluor 780 in PBS/EDTA 2 mM , following manufacturer’s instructions ( eBiosciences , San Diego , CA ) . Finally , cells were treated with 10% pooled human sera and 20% goat sera for 15 min at 4°C to block nonspecific binding sites and Fc receptors and cells were washed with cold staining buffer . Cells were centrifuged and resuspended at a final concentration of 1 × 107 cells per 100 μl of cold staining buffer . For intracellular detection of IL-10 , cells were stained with a combination of CD19-PE , CD27-FITC , CD24-PE and CD38-FITC monoclonal antibodies ( mAbs ) ( BD Biosciences ) . Cells were washed , fixed and permeabilized using the Cytofix Cytoperm kit ( BD Biosciences ) . Next , cells were stained with an APC-conjugated mouse anti-human IL-10 Ab ( Miltenyi Biotec Inc . ) . B-cell activation markers following parasite exposure were evaluated by staining with anti-human CD25-PE , CD54-PE , CD80-PE , CD69-FITC , CD83-FITC , or CD86-FITC mAb ( BD Biosciences ) . FMO controls including the Fixable Viability Dye and all the mAbs used were performed . Cells were analysed with a FACS Canto flow cytometer ( BD Biosciences ) and data were processed using FCS Express 4 ( De Novo Software , Los Angeles , CA ) . For CD27 and CD24/CD38 cell sorting experiments , magnetically-enriched B cells stained as described above with corresponding fluorochrome-conjugated antibodies were sorted using a BD Influx high-speed cell sorter . Freshly purified B cells or sorted B cells ( 1 x 107 cells/ml ) were incubated for 24 h in the presence of L . infantum amastigotes at the previously indicated ratio . Control supernatants were prepared by leaving B cells untreated for 24 h in culture media . To evaluate the effect of soluble factors produced by L . infantum amastigotes , an additional control was prepared by incubating parasites in the same condition but in the absence of B cells . After incubation , supernatants were harvested and centrifuged at 2 , 000 x g for 10 min to eliminate cell debris and parasites . Finally , supernatants were filtered with a 0 . 22 μm sterile syringe filter , aliquoted and frozen at-80°C until used . All the cell-free supernatants were tested for IL-10 content using a commercial ELISA kit ( see below for more details ) . Primary human CD4+ T cells were isolated from PBMCs using a magnetic Easysep CD4+ T-cell enrichment kit ( Stemcell Technologies ) and CD25+ T cells ( both activated CD4+ T cells and Tregs ) were depleted with a CD25 positive selection kit ( Stemcell Technologies ) . For activation experiments , CD25-CD4+ T cells were activated with plate-bound mAbs directed against CD3 ( clone OKT3; used at 5 μg/ml ) and CD28 ( clone 9 . 3; used at 2 μg/ml ) for 48 h in the presence or absence of a 1:4 dilution of B-cell conditioned medium . Cells were then washed and stained with anti-human CD25-PE and CD69-FITC and analysed by flow cytometry . For proliferation experiments , resting CD4+ T cells were first stained with the carboxyfluorescein succinimidyl ester ( CFSE ) dye following manufacturer’s instructions ( Invitrogen , Burlington , ON ) , activated as previously described for 5 days and analysed by flow cytometry . Production of TNF was evaluated by activating CD25-CD4+ T cells with plate-bound anti-CD3 ( 0 . 5 μg/ml ) in the presence or absence of a 1:4 dilution of B-cell conditioned medium for 72 h . Cells were then washed , fixed and permeabilized using the Cytofix Cytoperm kit ( BD Biosciences ) and finally stained with a PE-conjugated anti-human TNF ( BD Biosciences , Mississauga , ON ) before being analysed by flow cytometry . In some cases , cell-free supernatants were pre-treated with a recombinant soluble IL-10 receptor alpha ( ED50: 0 . 05–0 . 25 μg/ml in presence of 2 ng/ml of recombinant human IL-10 ) ( R&D Systems , Minneapolis , MN ) at a final concentration of 1 μg/ml . In all experiments , dead cells were excluded by 7-AAD staining as previously described [50] . B cells were stimulated as previously described . Thereafter , cell-free supernatants were harvested and IL-10 secretion was quantified using a commercial BD OptEIA Human IL-10 ELISA set ( BD Biosciences ) . Total RNA was isolated using the illustra RNAspin Mini Kit ( GE Healthcare , Mississauga , ON ) . RNA was reverse transcribed using Moloney-Murine Leukemia Virus ( M-MLV ) reverse transcriptase ( Promega , Madison , WI ) and expression levels of IL-10 transcripts were determined by quantitative PCR ( qPCR ) using SYBR Green master mix ( Applied Biosystems , CA ) on an ABI-PRISM 7500 Sequence Detector ( Applied Biosystems ) . Each sample was run in triplicate and relative changes in IL-10 expression were calculated using the 2ΔΔCt method [51] . This method was used once validation experiments showed that the efficiencies of the target and endogenous reference ( 18S ) were comparable . The primers used for IL-10 were: Fwd: 5’-TTACCTGGAGGAGGTGATGC-3’ and Rev: 5’-GGCCTTGCTCTTGTTTTCAC-3’ . These primers were designed using the coding sequence of human IL-10 ( Accession NM_000572 . 2 ) and Primer3 web application ( http://biotools . umassmed . edu/bioapps/primer3_www . cgi ) . The primers for 18S were: Fwd: 5’-TAGAGGGACAAGTGGCGTTC-3’ and Rev: 5’-CGCTGAGCCAGTCAGTGT-3’ [52] . B cells were pre-treated for 45 min with the following pharmacological inhibitors: phosphatidylinositol 3-kinase ( PI3K ) inhibitor Wortmannin ( 0 . 625 , 1 . 25 , 2 . 5 and 5 nM ) ( InvivoGen , San Diego , CA ) , p38 MAP kinase inhibitor SB203580 ( 1 . 25 , 2 . 5 , 5 and 10 μM ) ( Invitrogen , Burlington , ON ) , Syk inhibitor IV ( 62 . 5 , 125 , 250 , 500 and 1000 nM ) ( Calbiochem , EMD Millipore , Billerica , MA ) and calcium chelator BAPTA/AM ( 1 . 25 , 2 . 5 , 5 and 10 μM ) ( Calbiochem ) . Dimethyl sulfoxide ( DMSO ) was used as a drug carrier control for each of the studied pharmacological inhibitors . After incubation with L . infantum amastigotes , cell-free supernatants were harvested and IL-10 was quantified as previously described . Cell viability was evaluated using 7-AAD staining ( Biolegend , San Diego , CA ) as previously described [50] . Human B cells incubated or not with axenic amastigotes were cultured in FBS-containing RPMI-1640 culture medium or in serum-free XVIVO culture medium ( Gibco Life Technologies ) . Cell-free supernatants were assayed for TGF-β using a commercial ELISA following the manufacturer’s instructions ( R&D Systems ) . All supernatants were either assayed without treatment or following an acid/base treatment to activate latent TGF-β . Statistical analyses were performed using GraphPad Prism version 6 for Windows ( GraphPad Software , La Jolla , CA ) . Two-tailed Student’s t-test was performed and a threshold of p < 0 . 05 was considered statistically significant .
The potential outcome of an intimate contact between purified human B cells and L . infantum amastigotes was first assessed by monitoring surface expression of various cell activation markers by flow cytometry . Representative histograms of each activation marker tested are shown in Fig . 1A . Results depicted in Fig . 1B indicate that a statistically significant increase in the percentage of CD25- and CD83-expressing B cells is seen following an interaction with the parasite . The mean fluorescence intensities ( MFI ) for all studied activation markers ( i . e . CD25 , CD83 , CD86 , CD69 , CD54 and CD80 ) were augmented in a statistically significant manner by a contact with parasites ( Fig . 1C ) . On the other hand , the broadest lineage-specific surface marker for B cells , CD19 , remains stable despite an incubation step with the parasite . Further experiments performed with cell culture inserts to separate amastigotes and B cells indicate that a physical contact between the two distinct entities is required to modulate B-cell activation ( S1 Fig . ) . As circulating IL-10 levels are elevated in VL patient , we assessed whether the parasite can drive IL-10 expression and secretion in human tonsillar B cells . A dose-dependent increase in IL-10 mRNA expression was seen in response to L . infantum amastigotes ( Fig . 2A ) . Similar results were obtained when measuring IL-10 secretion in cell-free supernatants using a commercial ELISA kit ( Fig . 2B ) . Interestingly , the parasite-dependent induction of IL-10 mRNA was not seen when using heat-killed parasites ( Fig . 2C ) , thus suggesting that induction of IL-10 in B cells is an active mechanism mediated by live parasites . Although promastigotes can promote IL-10 mRNA synthesis at lower levels than amastigotes ( Fig . 2C ) , they are unable to induce IL-10 at the protein level ( Fig . 2D ) . Experiments performed with cell culture inserts to separate amastigotes and B cells indicate that a physical contact between human B cells and L . infantum amastigotes is necessary to achieve IL-10 secretion ( S2 Fig . ) . The production of IL-10 was also determined by intracellular staining . The percentage of IL-10-expressing cells was higher in B cells exposed to the parasite compared to untreated controls ( Fig . 2E ) , and were identified as CD19-expressing cells as previously described [46 , 48] . Importantly , human peripheral blood ( circulating ) B cells were also capable of secreting IL-10 upon incubation with the parasite and activation markers were also enhanced upon an exposure to L . infantum amastigotes ( S3 Fig . ) . It has been previously described in mice that TGF-β is secreted by B cells with modulatory functions and is able to modulate CD4+ T-cell functions [53] . However , we were not able to detect TGF-β secretion or activation of latent TGF-β in B-cell culture media following exposure to parasites or in the supernatant of CD4+ T cells exposed to culture media from human B cells treated with amastigotes . Taken together these results demonstrate that a contact with L . infantum amastigotes activates human B cells and leads to secretion of T-cell suppressive factors such as IL-10 . However , we have no evidence for a contribution of TGF-β in the suppressive phenotype . Secretion of IL-10 by macrophages and T cells has been shown to be mostly dependent on ERK and c-Maf signaling pathways although other second messengers such as p38 and NF-kappa B were involved as well [54–61] . In B cells , and particularly in murine regulatory B cells , IL-10 secretion has been suggested to rely on B-cell receptor ( BCR ) -derived signals leading to Syk activation and an increase in intracellular calcium but also on Toll-like receptor ( TLR ) expression [43 , 62 , 63] . Therefore , a panel of various signal transduction inhibitors was tested in order to shed light on the signal transduction pathways and molecular mechanisms that drive IL-10 production once human B cells are put in contact with L . infantum amastigotes . To this end , B cells were pre-treated with increasing concentrations of inhibitors directed against Btk ( i . e . LFM-A13 ) , ERK ( i . e . PD98059 ) , p38 ( i . e . SB203580 ) , protein kinase C ( i . e . Rö318220 ) , Syk ( i . e . BAY 61–3606 , also called Syk Inhibitor IV ) , and PI3K ( i . e . Wortmannin ) prior to exposure to the parasite . A dose-dependent inhibition of IL-10 secretion was observed only in the presence of inhibitors against Syk , PI3K and p38 ( Fig . 3 , panels A to C ) . No such decrease in the parasite-mediated induction of IL-10 was seen with compounds directed either against protein kinase C , ERK , or Btk . The role of intracellular calcium signalling was also investigated by pretreating B cells with increasing concentrations of the intracellular calcium chelator BAPTA/AM . A dose-dependent inhibition of IL-10 secretion in the presence of BAPTA/AM was detected ( Fig . 3D ) , which is in line with a previous observation made with murine B cells [62] . Cell viability following drug treatment was evaluated by flow cytometry using 7-AAD staining of dead cells and no differences were observed between control samples and those treated with each drug at the various concentrations tested . Although there is currently no cell surface or intracellular phenotypic marker or set of markers unique to IL-10-producing B cells , CD27 is a well-characterized marker for memory B cells which has been detected on some occasions in IL-10-secreting B cells [46 , 48 , 64] . It has also been established that transitional immature B cells ( CD24highCD38high ) are the main source of IL-10 produced by circulating B cells [46] . Moreover , it has been demonstrated that IL-10 is generated by CD24highCD27+ B cells and that an enhancement of CD24 and CD38 is seen in IL-10-expressing cells [48] . Therefore , we monitored surface expression of CD27 , CD24 and CD38 on the total B-cell population after contact with L . infantum amastigotes . Data depicted in Fig . 4A demonstrate that the overall surface expression of CD27 diminished after an exposure to parasites compared to control cells . Similar results were obtained for CD24 ( Fig . 4B ) , thus suggesting B-cell activation [65] . In contrast , CD38 was not affected upon exposure to the parasite ( Fig . 4C ) . To determine the possible contribution of CD27 in the parasite-mediated B-cell response with respect to IL-10 secretion , experiments were performed using two distinct purified cell subpopulations ( i . e . CD27- and CD27+ ) . Results showed that IL-10 is mainly secreted by the CD27- subpopulation upon parasitic stimulation ( Fig . 5A ) . To analyse the importance of CD24+ and/or CD38+ cells in response to L . infantum amastigotes , subsequent experiments were performed with three distinct purified B-cell subsets ( i . e . CD24+CD38+ , CD24+CD38- and CD24-CD38+ ) . The parasite-mediated induction of IL-10 is seen exclusively in the two CD24-expressing B-cell subsets irrespective of CD38 expression ( i . e . CD24+CD38+ and CD24+CD38- ) ( Fig . 5B ) . These observations are in agreement with some previous studies , which were however not using a protozoan parasite such as L . infantum [46 , 48] . Regulatory B cells have been shown previously to modulate CD4+ T-cell functions through different mechanisms involving soluble factors such as IL-10 [46 , 64] . Based on this information , we assessed whether conditioned medium from human B cells incubated with L . infantum amastigotes can modulate some basic functions of CD4+ T cells . It should be noted that CD25-expressing CD4+ T cells were depleted initially to remove both already activated cells and Tregs because it might represent a confounding factor . Results depicted in Fig . 6 indicate that soluble factors present in conditioned media from parasite-treated human B cells induce a statistically significant decrease in expression of the classical activation markers CD25 and CD69 on the surface of CD4+ T cells stimulated with plate-bound anti-CD3 and anti-CD28 antibodies ( an experimental condition mimicking antigen presentation ) . A similar finding was made when measuring CD4+ T-cell proliferation with the CFSE dye ( Fig . 7 ) . Secretion of TNF by activated CD4+ T cells is a well-recognized marker of their pro-inflammatory function [66 , 67] , and it was shown to be modulated by regulatory B cells in both humans and mice [46 , 48] . We thus assessed intracellular TNF expression in CD4+ T cells treated with conditioned media from parasite-treated human B cells . A statistically significant diminution in the percentage of TNF-producing CD4+ T cells was seen following incubation with conditioned media from human B cells incubated with L . infantum amastigotes ( Fig . 8 ) , which was partially but not completely inhibited in the presence of a blocking agent ( i . e . neutralizing soluble IL-10 receptor ) . Given the importance of IFNγ in the immune response directed against Leishmania parasites , we also performed intracellular staining for this cytokine . We found that cell-free supernatants from human B cells incubated with L . infantum amastigotes mediate production of IFNγ in CD4+ T cells , a process which was again not totally restored in presence of the neutralizing soluble IL-10 receptor ( Fig . 9 ) . It was previously shown that inhibition of CD4+ T-cell proliferation was partially mediated by IL-10 [64] . However we were unable to restore CD4+ T-cell proliferation by using the neutralizing soluble IL-10 receptor . Altogether our observations suggest a partial involvement of IL-10 in the parasite-mediated modulatory effect on human CD4+ T cells .
The biological contribution of IL-10 for the visceralization of L . infantum in mice has been well established [29 , 68–70] . Moreover , IL-10 levels correlate with disease progression in humans [6 , 71] and dogs [72] . Finally , the importance of B cells as a source of IL-10 in the susceptible Balb/c murine model of L . major infection has been already described [41] and their potential to regulate T-cell responses was described in mice infected with L . donovani [15] . However , while Deak and colleagues have recently demonstrated the role of B cells in the visceralisation of Leishmania in a murine model of VL , they have shown that this effect is not relying on IL-10 [14] . In the spleen of VL patients , IL-10 mRNA was mostly associated with T lymphocytes and a reduced B/T ratio was observed compared to exposed controls . This would suggest a minor role for B cells in the elevated plasma IL-10 levels . However , our results in Fig . 2 ( panels C and D ) with B cells exposed to L . infantum promastigotes suggest that IL-10 mRNA levels are not necessarily correlated to IL-10 cytokine production . The direct involvement of human B cells to the elevated levels of plasma IL-10 following L . infantum infection and their role in the visceralization of L . infantum in humans still remains unclear . In the current study , we first demonstrate that in vitro incubation of L . infantum amastigotes with purified human B cells increases expression of numerous activation markers ( Fig . 1 ) and the parasite-directed effect on B-cell activation requires an intimate contact between the two distinct entities ( S1 Fig . ) . This is in agreement with the demonstrated B-cell activation in patients with localized cutaneous leishmaniasis [73] . We provide evidence that L . infantum amastigotes induce a dose-dependent increase in IL-10 mRNA expression and cytokine secretion ( Fig . 2 ) . An enhancement of IL-10 production was also observed when using peripheral blood B cells ( S3 Fig . ) . The parasite-mediated production of IL-10 by B cells was not seen with parasites under the flagellated promastigote form , paraformaldehyde-killed amastigotes ( Fig . 2 ) and when abrogating an intimate contact between parasites and B cells ( S2 Fig . ) , therefore suggesting that a direct contact of live parasites under the nonflagellated amastigote stage ( normally found in the macrophage phagolysosomal compartment of the mammalian host ) is required to mediate the observed phenomenon . Intracellular staining by flow cytometry revealed that IL-10-expressing cells correspond to the CD19 compartment , as it has been previously described for other types of stimulation [46 , 48] . Our results therefore suggest that while B cell-derived IL-10 driven by amastigotes could contribute significantly to the elevated levels of IL-10 observed in VL patients , this cytokine is probably not the sole factor involved in the amastigote-induced B-cell regulatory activity . A very close physical contact between human B cells and L . infantum amastigotes was observed under the microscope and the use of a galactose-modified PBS/EDTA solution was required to efficiently separate the two distinct entities and obtain single-cell suspensions prior to flow cytometry analyses . The ability of galactose to take apart B cells and parasites suggests the involvement of a galactose-specific lectin . Multiple lectins can transduce signals in response to ligand binding and some of them have been shown to bear one or multiple intracellular tyrosine-based activation motifs ( ITAM ) that can signal through Syk , intracellular calcium and PI3K to activate endocytosis , cell proliferation and gene transcription [74–77] . The protozoan parasite Leishmania has been shown to be bound by various lectins , including plant lectins as well as mammalian C-type lectins and galectins [78–84] . Lectin ligands on Leishmania have been shown to be both species- and stage-specific , which complicate their identification and the study of their functionality [81 , 82 , 84] . Leishmania can also trigger TLR signaling , mostly via TLR2 , TLR4 and TLR9 [40 , 68 , 85–89] . In mice , TLR signaling through Myd88 was shown to be essential for the clearance of the pathogen [90] . It has been show that TLR4 and TLR9 agonists induce IL-10 in murine B cells , which can modulate T-cell responses [43 , 48] . Altogether , these observations suggest that Leishmania can induce activation of regulatory B cells via a TLR-dependent pathway . Experiments aimed at evaluating the putative contribution of TLR- and Myd88-mediated signal transduction pathways in the parasite-induced IL-10 secretion were unsuccessful due to technical limitations related to low transfection efficiency in primary human B cells and unstable or inefficient commercial inhibitory peptides . However , we could not detect any parasite-induced secretion of type-I IFN in human B cells , as monitored with the reporter cell line HEK-Blue™ IFN-alpha/beta using various parasites to cell ratios and different incubation periods . As type-I IFN production is a hallmark output of endosomal TLR signaling ( i . e . TLR3 , 7 , 8 and 9 ) [91 , 92] and as human tonsillar B cells do not express TLR4 and do not respond to LPS [93 , 94] , we can at least propose that the parasite-triggered IL-10 secretion does not rely on these receptors . Production of IL-10 by regulatory B cells in peripheral blood was also shown to be dependent on BCR , ERK and intracellular calcium [43 , 62] . We could not observe any modulatory effect of the highly specific ERK inhibitor PD98059 on IL-10 secretion following incubation with L . infantum amastigotes . Similarly , the BTK inhibitor LFM-A13 and the PKC inhibitor Rö318220 did not affect the parasite-mediated IL-10 secretion . ERK , BTK and PKC are known to be important for signal transduction via the BCR [95–98] . Our experiments instead suggest that the parasite-mediated IL-10 secretion in human B cells is dependent on Syk , PI3K , p38 and intracellular calcium ( Fig . 3 ) , which would again point towards a C-type lectin-mediated signal transduction pathway . Additional studies are required to identify the ligand ( s ) of amastigotes on the surface of human B cells . Although regulatory B cells and IL-10 secretion by B cells have been observed in humans [46 , 48 , 99] , the precise phenotype of such cells is still elusive . It has been previously shown that stimulation of human B cells with CpG and anti-IgG drives IL-10 secretion by memory B cells ( IgD+CD27+ ) after a short period of treatment ( i . e . 5 h ) , whereas naïve B cells were shown to produce IL-10 following a longer period of stimulation with the same agonists ( i . e . 48 h ) [64] . The controversy surrounding the phenotypic characterization of IL-10-producing B cells is further supported by the previous findings made by Iwata an colleagues who have shown that the CD27+ subpopulation can respond to CpG and CD40L by expressing IL-10 [48] and Blair and co-workers who have established that IL-10-producing B cells following stimulation with CD40L correspond to the CD27- subset [46] . Our results indicate that CD27 expression decreases in the whole population of human B cells following treatment with L . infantum amastigotes ( Fig . 4A ) . This might suggest a decrease of the memory B-cell population , as it was observed for other pathologies including HIV-1 infection [100 , 101] . The transitional immature B-cell subset ( CD24highCD38high ) has been proposed as the main producer of IL-10 by the B-cell population [46] . However , in another study , the IL-10-secreting cells were predominantly found in the CD24highCD27+ B-cell population [48] . Considering that a consensus about CD24 expression seemed to emerge as a hallmark for IL-10-secreting human B cells , we monitored expression of this surface marker upon incubation of human B cells with L . infantum amastigotes . We report herein that the parasite leads to a significant reduction in the expression of CD24 ( Fig . 4B ) , indicative of B-cell activation [102 , 103] , with no effect on CD38 ( Fig . 4C ) . Altogether , these results suggest that the phenotype of IL-10-secreting B cells following incubation with L . infantum is different from those that were described previously . In a related set of experiments , we studied different purified B-cell subsets ( i . e . CD24+ , CD27+ and CD38+ ) and surprisingly , only the CD27-negative B-cell subpopulation responded to the parasite by producing IL-10 ( Fig . 5A ) . Furthermore , we were able to reveal the importance of CD24+ cells , but not CD38+ , for the secretion of IL-10 by B cells in response to L . infantum amastigotes ( Fig . 5B ) . This suggests a quite different IL-10-secreting B-cell subset from those that were previously described both in human in vitro and murine in vivo models [41 , 43 , 46 , 48] . While it is assumed that phenotypic characteristics of IL-10-producer cells might differ according to the stimuli to which they are exposed , this CD24+CD27- human B-cell subpopulation seems to respond to L . infantum amastigotes by secreting high levels of IL-10 . Given that our experimental procedures involved a short incubation period between human B cells and parasites ( i . e . 24 h ) without involvement of CD40 signaling , we postulate that such a CD40-independent activation of B cells would arise as part of an innate response , which could be exploited by the parasite . Data from additional experiments suggest that soluble factors secreted by human B cells exposed to L . infantum amastigotes can inhibit activation and proliferation of CD25-CD4+ T cells ( Figs . 6 and 7 ) , which corroborates a previous work using human B cells treated with a combination of a TLR9 agonist ( i . e . CpG-B ) and anti-Ig antibodies [64] . Regulatory B cells are characterized by their ability to modulate different functions of CD4+ T cells including activation , proliferation and production of pro-inflammatory cytokines such as TNF and IFNγ . Such cells can also significantly regulate some biological functions of monocytes and dendritic cells [46 , 48 , 64 , 104] . Among their described mechanisms of action , IL-10 remains the major soluble factor involved but modulation of cell function though TGF-β has also been described in the murine model [53] . A more direct interaction may also be at play since Blair et al . have reported that CD80 and CD86 interactions between B cells and CD4+ T cells act synergistically with B cell-derived IL-10 to suppress CD4+ T-cell cytokine production [46] . Although it was previously shown that inhibition of CD4+ T-cell proliferation was mediated by IL-10 [64] , we did not see a significant restoration of cell proliferation by neutralizing IL-10 in the cell supernatant via addition of a soluble IL-10 receptor . These results lead us to propose that impairment of CD25-CD4+ T-cell proliferation by soluble factors secreted by human B cells incubated with L . infantum amastigotes is independent of IL-10 . Production of TNF by CD4+ T cells following antigenic stimulation can be used as a functional marker [66 , 67] . It was established that conditioned media from CPG/anti-Ig-treated B cells could inhibit TNF production by antigen-stimulated CD4+ T cells but that this effect was not relying on IL-10 [48] . Interestingly , TNF secretion by monocytes was demonstrated to be affected by conditioned media from B cells in an IL-10-dependent manner [48] . In our hands , when CD25-CD4+ T cells were activated in the presence of conditioned media from B cells exposed to L . infantum amastigotes , we observed a significant decrease in TNF production , which was partially reverted when adding a soluble IL-10 receptor ( Fig . 8 ) . Interestingly , similar observations were made when IFNγ was used as readout ( Fig . 9 ) . It can be hypothesized that modulation of CD4+ T-cell functions exerted by conditioned media from parasite-treated human B cells is due to a multifactorial process that includes IL-10 and some unknown factor ( s ) . The parasite-directed enhancement in CD80 and CD86 expression in human B cells ( Fig . 1 ) might be involved in modulating CD4+ T-cell functions [46] , however we were unable to evaluate this possibility due to technical limitations related to parasite growth . The systemic dissemination of pathogens such as Staphylococcus aureus , Porphyromonas gingivalis , Mycobacterium tuberculosis and Herpesviruses has been linked with their ability to induce IL-10 secretion in immune cells [105–109] . More relevant to the present study , IL-10 secretion is crucial for the visceralization of Leishmania parasites in mice [29 , 110–112] and increased levels of IL-10 are found in the serum of patients suffering from VL [23 , 31 , 71 , 73] . The contribution of B cells to the production of IL-10 in response to Leishmania has also been described in the mice model [14 , 15 , 41] . However , VL in humans has been associated with a reduced B/T ratio in the spleen and elevated IL-10 mRNA mainly associated with T lymphocytes [32] . Herein , we report that L . infantum amastigotes can induce secretion of IL-10 and other ( s ) unknown suppressive factors in a specific subset of human B cells ( i . e . CD19+CD24+CD27- ) that can modulate CD4+ T-cell functions . Further studies are warranted to determine the clinical importance of this phenomenon and whether induction of B regulatory functions by L . infantum amastigotes is part of an immune evasion strategy that would allow the dissemination and visceralization of this parasite . | Leishmaniasis is an infection caused by protozoan parasites of the genus Leishmania and is a significant neglected tropical disease , with 350 million people in 98 countries at risk of developing one of the forms of the disease . Visceral leishmaniasis is characterized by an uncontrolled parasitization of internal organs , which leads to death when left untreated . Disease progression is linked with the type of immune response generated and a strong correlation was found between disease progression and serum levels of the immunosuppressive cytokine IL-10 . We demonstrate that a contact between human B cells with Leishmania infantum amastigotes resulted in upregulation of multiple cell surface activation markers and a dose-dependent secretion of IL-10 . Conditioned media from B cells incubated with Leishmania infantum amastigotes were shown to strongly inhibit CD4+ T-cell activation , proliferation and function ( i . e . TNF and IFNγ production ) . Blockade of IL-10 activity using a soluble IL-10 receptor restored to some degree TNF and IFNγ secretion . Cell sorting experiments allowed us to identify a major IL-10-secreting B cell subset characterized as CD24+ and CD27- . Exposure of human B cells to Leishmania infantum amastigotes thus triggers B cells with regulatory activities mediated in part by IL-10 , which could promote parasite dissemination in the organism . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Leishmania infantum Amastigotes Trigger a Subpopulation of Human B Cells with an Immunoregulatory Phenotype |
The sensitivity and specificity of two in-house MAC-ELISA assays were tested and compared with the performance of commercially-available CTK lateral flow rapid test and EUROIMMUN IFA assays for the detection of anti-Chikungunya virus ( CHIKV ) IgM . Each MAC-ELISA assay used a whole virus-based antigen derived from genetically distinct CHIKV strains involved in two chikungunya disease outbreaks in Singapore ( 2008 ) ; a January outbreak strain with alanine at amino acid residue 226 of the E1 glycoprotein ( CHIKV-A226 ) and a May-to-September outbreak strain that possessed valine at the same residue ( CHIKV-226V ) . We report differences in IgM detection efficacy of different assays between the two outbreaks . The sensitivities of two PCR protocols were also tested . For sera from January outbreak , the average detection threshold of CTK lateral flow test , MAC-ELISAs and EUROIMMUN IFA assays was 3 . 75 , 4 . 38 and 4 . 88 days post fever onset respectively . In contrast , IgM detection using CTK lateral flow test was delayed to more than 7 days after fever onset in the second outbreak sera . However , MAC-ELISA using CHIKV-226V detected IgM in the second outbreak sera 3 . 96 days after fever onset , which was approximately one day earlier compared to the same assay using CHIKV-A226 ( 4 . 86 days ) . Specificity was 100% for both commercial assays , and 95 . 6% for the in-house MAC-ELISAs . For sensitivity determination of the PCR protocols , the probe-based real time RT-PCR method was found to be 10 times more sensitive than one based on SYBR Green . Our findings suggested that the two strains of CHIKV using variants A226 and 226V resulted in variation in sensitivities of the assays evaluated . We postulated that the observed difference in antigen efficacy could be due to the amino acid substitution differences in viral E1 and E2 envelope proteins , especially the E1-A226V substitution . This evaluation demonstrates the importance of appraisal of different diagnostic assays before their application in clinical and operational settings .
Chikungunya virus ( CHIKV ) has seen a resurgence in recent years , with outbreaks being described in Republic of Congo in 2000 , La R'eunion in 2005 , India , Sri Lanka , Malaysia and Gabon in 2006 , Italy in 2007 , Singapore and Thailand in 2008 [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] . The current pandemic involves a newer CHIKV strain of the East-Central South African ( ECSA ) genotype . Extensive research and analysis demonstrated the role of a viral mutation , A226V , in the changed epidemiology of the disease . There is no evidence that this particular mutation caused any alteration in virulence of the CHIKV or clinical manifestations of the disease , but the mutation , residing in the viral envelop protein , has been shown to facilitate enhanced transmissibility of the virus by Aedes ( Ae . ) albopictus . Several sophisticated studies have established that the A226V mutation rendered higher viral replication and dissemination rates in Ae . albopictus , and thus shortening the extrinsic incubation period in the vector [10] , [11] . The length of extrinsic incubation period determines the infective life span of a vector , and consequently has great influence on the epidemic potential of the virus-vector partnership . Since 2006 , in response to the outbreaks in the region , the Environmental Health Institute ( EHI ) , a national public health laboratory in Singapore , has initiated laboratory surveillance for CHIKV . Two main outbreaks were detected: the first occurring in January 2008 was a small outbreak with 13 local cases [12] , [13]; and the second commenced in May 2008 and peaked two months later , resulting in 231 local cases by the end of September 2009 [13] . Phylogenetic analysis concluded that viruses isolated from these two outbreaks were related to the ECSA genotype [13] . Interestingly , the viruses from the first outbreak showed alanine at amino acid residue 226 ( A226 ) of E1 gene and those from the second outbreak showed valine ( 226V ) at the same codon . While Ae . aegypti was the implicated vector in the first outbreak , Ae . albopictus was the confirmed vector of the second outbreak [13] . Though CHIKV was not isolated from any field caught Ae . aegypti during the first outbreak , entomological investigations in the affected area found only Ae . aegypti adults , and data from routine surveillance ( part of Singapore dengue control programme ) also showed that Ae . aegypti was the predominant species in the area . On the other hand , CHIKV was isolated from Ae . albopictus caught during the second outbreak . Laboratory confirmation of CHIKV infection is critical , especially in dengue endemic areas , as clinical symptoms of the two diseases are similar . However the two viruses may be transmitted by different vectors ( Ae . aegypti and Ae . albopictus ) , which require different control strategies . RT-PCR is an excellent tool for the early phase confirmation of CHIKV infections , and many protocols have been established for this purpose [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Unfortunately , this viral detection method is limited to the viraemic phase , which is usually one to five days after fever onset . Thereafter , confirmation of CHIKV infection requires serological tests . In recent years , a few commercial CHIKV diagnostic kits have emerged in the market , but there are very few reports on the systematic and comparative evaluation of these commercial products . The most recent CHIKV diagnostic assay on the market is the indirect immunofluorescence assay ( IFA ) from EUROIMMUN AG ( Lübeck , Germany ) , whose IgM assay presented a specificity of 98 . 3% and a sensitivity of 96 . 9% [22] . This assay , together with an IgM lateral flow rapid test kit by CTK Biotech Inc ( San Diego , USA ) , was evaluated alongside an in-house IgM Capture Enzyme-Linked Immunosorbant Assay ( MAC-ELISA ) . Sensitivities , specificities and approximate time antibodies first became detectable in an infected patient were determined . A comparison between the sensitivity of two PCR protocols was also performed using RNA standards derived from cell-cultured viruses . The evaluation , using samples from two independent and epidemiologically distinct CHIKV outbreaks in Singapore , was done to establish diagnostic capability in the laboratory . The IgM titres were not determined , and thus the kinetics of the antibody has not been established . However , the availability of a reliable assay allows antibodies to be titred for each sample , and thus facilitating an ongoing antibody kinetics study , that will be reported later .
Environmental Health Institute is a public health laboratory that functions as a licensed diagnostic laboratory , with an ISO9001 accreditation . It served as the national reference laboratory during the CHIKV outbreaks in 2008 . The three diagnostic techniques were evaluated using three characterized panels of sera . Persons with an acute febrile illness , signs or symptoms compatible with chikungunya fever ( fever , joint pain , or rash ) were tested with CHIKV RT-PCR [12] , a routine test which has been offered under EHI's quality assured programme as required for the national license . Sera panel A and B were multiple consecutive samples collected from RT-PCR CHIKV confirmed patients , during the first and second CHIKV outbreaks respectively ( See supporting information “Supporting Data S1” ) . The patients were warded at the Tan Tock Seng Hospital Communicable Disease Centre ( TTSH CDC ) . The first samples were collected on the day of first medical consultation and subsequently , more samples were collected as the disease progressed , till convalescence . The daily samples were used to determine the sensitivity of IgM serology on each day of illness . Panel A , comprising residual blood from eight CHIKV-confirmed patients from the first outbreak ( January 2008 ) , were collected for clinical management and to determine when the patient could be discharged . Six to 11 samples were collected from each patient , resulting in a total of 60 samples ( See supporting information “Supporting Figure S1” ) . Panel B , from the second outbreak ( May to September 2008 ) was collected from 28 CHIKV-confirmed patients in August 2008 prospectively . Each patient had five to 12 samples collected , leading to a total of 225 samples . All sera samples were kept at 4°C after phlebotomy , transported on ice , and reached the laboratory within 24 hours . The samples were either tested on the same day , or stored at −80°C until testing . Panel C sera were used for specificity tests and consisted of 45 flavivirus-confirmed sera ( 44 Dengue , one Japanese Encephalitis ) and five non-CHIKV alphavirus-confirmed sera ( two Barmah Forest , three Ross River ) . Analysts of the serology tests were not blinded to the RT-PCR results of the first samples . However , they were blinded to the serology results derived from the other tests . Panels A and C were residual samples of sera sent to EHI for diagnosis . Use of residual samples for evaluation of diagnostic assays to establish in-house capability is exempted from internal review by the National Environment Agency Bioethics Review Committee . Use of sera panel B , which was collected for a larger study , was approved by the National Healthcare Group Domain Specific Review Board , and written informed consent was obtained from the study participants . To determine the sensitivities of PCR protocols , two previously published protocols were tested . The first was a one-step SYBR Green based RT-PCR , from Hasebe et al . 2002 , for the detection of a fragment of the non-structural protein 1 ( nsP1 ) gene of CHIKV [15] . The PCR conditions were described in Hapuarachchi et al . 2010 . The other assay was a taqman probe-based RT-PCR protocol adapted from Pastorino et al . 2005 , with slight modifications to the primers which target the E1 region ( Table 1 ) and included following modifications to the PCR assay: PCR assay was performed using the Qiagen QuantiTect Probe RT-PCR kit , in a final reaction volume of 20 µl containing 5 µl of template , 1× of buffer mix , 0 . 2 µl of RT enzyme mix , 0 . 25 µM of probe , 0 . 25 µM and 0 . 4 µM of forward and reverse primers respectively . The amplification cycles were extended to 50 with denaturation at 94°C for 5 sec and annealing/extension step at 60°C for 1 min . The analytical sensitivity and reproducibility of both assays were determined using 10-fold dilutions of cultured CHIKV strains D67Y08 ( A226 ) and D1225Y08 ( 226V ) ( 2 . 7×10−1 to 2 . 7×108 pfu/ml ) . CHIKV RNA was extracted from the dilutions using QIAamp viral RNA mini kit ( Qiagen , Hilden , Germany ) . A total of three analysts were involved in the evaluations . The two commercial IgM assays were performed by one analyst , and the in-house ELISA assays were performed by another . The third analyst performed the PCR sensitivity tests . All analysts were trained in-house , certified by the Director of the diagnostic laboratory , and regularly passed the external ( RCPA ) and internal proficiency tests , under the EHI quality assurance programme . The sensitivity and specificity of assays were calculated in Microsoft Excel 2007 . ANOVA , to test for variance amongst results obtained by the serological assays , and Student t-tests to determine any significant differences between the different assays were calculated using SPSS 13 . 0 software .
The commercial lateral flow rapid test and IFA , along with in-house MAC-ELISAs using both D67Y08 ( 226A ) and D1225Y08 ( 226V ) , were evaluated using three panels of sera . Sera panels A and B were collected during the two outbreaks in January and May to September in 2008 respectively , and were from CHIKV RT-PCR-confirmed patients ( See supporting information “Supporting Data S1” ) . Average IgM detection threshold , according to day after fever onset was determined . During the first outbreak , the lateral flow ( CTK ) kit enabled the detection of IgM on an average of 3 . 75 days after fever onset . IFA ( EUROIMMUN ) and in-house MAC-ELISA detected IgM on 4 . 88 day and 4 . 38 day respectively . The use of CHIKV-A226 or -226V did not alter the effectiveness of the in-house ELISA assays ( Table 2 ) during the January outbreak . Though CTK's lateral flow assay had the best performance in the January outbreak , its performance was not repeated in the second outbreak . Among the first 10 CHIKV- confirmed patient sera ( total of 74 samples from Panel B ) , none had detectable IgM within seven days after fever onset . The earliest IgM detection attained by the lateral flow assay , was day nine after the onset of fever ( n = 1 ) and eight of the 10 patients did not show seroconversion even after 14 days . To investigate if the drop in performance was due to batch variability of the CTK kit , 30 CHIKV-A226 IgM positive and 10 CHIKV negative samples from the first outbreak were retested with the second batch of CTK kits . No difference in results interpretation was observed . As the ineffectiveness of the kit was clearly demonstrated by the 74 samples from the 10 patients , and both batches of kits were no longer available , the rest of the samples from the second outbreak ( 151 samples from 18 patients ) were not tested with the CTK assay . Using IFA ( EUROIMMUN ) and MAC-ELISA ( A226 ) on the panel collected from the second outbreak , the average day of IgM detection was 4 . 86 after fever onset . Interestingly , the use of CHIKV-226V as antigen in MAC-ELISA increased the sensitivity to 3 . 96 day after onset of fever ( p<0 . 0001 ) . Overall , the sensitivity of assays increased along with the progression of the disease ( Figure 1 ) . Sensitivities of all assays were very low from day zero to day four of the disease , ranging from 0 to 66 . 7% ( Figure 1a ) . Sensitivity improved from day five , when MAC-ELISA ( 226V ) fared the best at 93 . 94%; followed by MAC-ELISA ( A226 ) at 84 . 85%; and IFA ( EUROIMMUN ) at 75 . 76% . Lateral flow ( CTK ) remained insensitive at 12 . 12% . By the sixth day , 100% sensitivity was attained by MAC-ELISA ( 226V ) , and by day seven , MAC-ELISA ( A226 ) and IFA ( EUROIMMUN ) also achieved 100% . In view of the possible variation in sensitivity between the two outbreaks , the samples from the CHIKV-A226 and CHIK-226V outbreaks were analysed separately ( Figure 1b ) . In the first outbreak involving CHIKV-A226 , lateral flow ( CTK ) test was positive in two out of five samples that were collected one day after fever onset . The sensitivity steadily increased to 100% by seven days . The other three assays started to register sensitivities greater than 50% on day five after fever onset . In the second outbreak , involving CHIKV-226V , MAC-ELISA ( 226V ) had the highest sensitivity of 75% at four days after fever onset and increased to 100% by day six ( Figure 1c ) . IFA ( EUROIMMUN ) and MAC-ELISA ( A226 ) detected less than 50% of samples on day four , and attained 100% only on day seven . It appeared that in the second outbreak , MAC-ELISA ( 226V ) , which utilized the virus isolated in the same outbreak , was the most sensitive . Panel C comprising of non-CHIKV sera was utilized to determine the specificity that turned out to be 100% for both commercial assays . The sensitivity of in-house MAC-ELISA was 95 . 6% . The latter picked up two dengue confirmed sera which were paired samples from a single patient collected 14 days apart . Though MAC-ELISA yielded positive results with the two samples , no increase in titre was observed and negative CHIKV PRNT results were obtained ( data not shown ) . Two real-time PCR protocols were evaluated on RNAs extracted from a serial dilution of each of the cell-cultured viruses , D67Y08 ( A226 ) and D1225Y08 ( 226V ) , isolated from the two outbreaks . As relative sensitivity of PCR protocols can be validated using RNA from isolated virus , no patient samples were used for this purpose . The sensitivities of each protocol for both viruses were equivalent . However , the sensitivity of the taqman probe-based protocol was found to be 10 times ( 2 . 17×100 pfu/ml ) more than the SYBR Green assay ( 2 . 17×101 pfu/ml ) ( Table 3 ) . The inverse relationship of Cp values and virus titres showed very good linear correlation between the detection of CHIKV-A226 and -226V when either assays were used ( Figure 2 ) .
As dengue and chikungunya infections elicit similar symptoms and can be present in the same locations , clinical differentiation may be difficult . In Singapore , it was found that the major chikungunya outbreak in the second half of 2008 was transmitted by Ae . albopictus , an outdoor mosquito . Control of chikungunya fever was thus different from the strategy employed for control of dengue fever , which is transmitted by Ae . aegypti , a peri-domesticated mosquito . It is thus important to ascertain the cause of a cluster of febrile illness . This study was carried out to ensure that accurate and robust diagnostic tools were used to diagnose chikungunya fever in Singapore . Our findings suggested that two variants of CHIKV , A226 and 226V , had resulted in variation in sensitivities of the assays evaluated . Though CTK's lateral flow rapid test was found to be a reliable kit in the first outbreak ( January 2008 ) , it was ineffective in the second ( ( May to September 2008 ) . Retesting a panel of CHIKV characterized samples from the first outbreak showed that the inconsistency was not a result of batch variation . We postulated that the inconsistency may be due to the different CHIKV variants involved in two outbreaks . The CTK rapid test kit used a recombinant antigen covering the 226 residue of E1 gene derived from the CHIKV-A226 [personal comm . ] , which was similar to the strain involved in the first outbreak . It is thus suggested that CHIKV-A226 derived recombinant antigen was specific for recognition by antibodies elicited by the A226 virus circulating during the first outbreak , but not for those elicited against the 226V virus of the second outbreak . Therefore , it is highly likely that the sensitivity of the rapid test can be improved by including the recombinant antigen derived from the variant virus . The variation in sensitivity of an assay due to different antigens used was also demonstrated by our in-house MAC-ELISA , where CHIKV-226V antigens yielded higher sensitivity than CHIKV-A226 , when tested on sera obtained from patients infected with the CHIKV-226V strain . Similarly , the decrease in sensitivity of the EUROIMMUN IFA was probably attributed to the use of CHIKV-A226 [22] , [24] . The less striking sensitivity differences in these assays may be attributed to the use of whole viruses that offer more epitopes for recognition . Interestingly , both A226 and 226V viral strains offered the same sensitivity among samples collected from the first outbreak ( A226 ) . Notwithstanding the latter observation , our results indicated that sensitivity of a test could be improved by using the circulating virus isolated during a particular outbreak . It is unlikely that the difference in sensitivity was due to differences in quality of the antigen , as a single batch of antigen was prepared from each virus and yet , sera from the two outbreaks were giving different results for the A226 antigen ( but not for the 226V antigen ) . Both antigens were prepared in the same way , and all sera were tested in one experiment using the same reagents and controls . Our results suggest the disadvantage of using a recombinant antigen that is too specific . The commercial assays displayed excellent specificity , but the in-house ELISAs picked up two paired dengue IgM samples . These samples did not demonstrate increase in CHIKV IgM titres , and were CHIKV PRNT negative . As such , these were not Dengue and Chikungunya co-infected samples , rather a false positive due to non-specific IgM reactions . In our experience with dengue diagnosis , this phenomenon is not unknown , especially among adults who suffer from conditions such as Systemic Lupus Erythematosus or other immunological conditions . Though the phenomenon is poorly understood , we believe that false positives in IgM serology could be due to other immunological factors , and this may also be the case for CHIKV infection . The cross reaction may not be due to Dengue virus cross- reacting with the CHIKV assay . Our investigation and analysis in Singapore using geographical information system had also revealed that Singapore's major outbreak , due to CHIKV-226V and Aedes albopictus , did not overlap spatially with dengue fever , which is transmitted by Ae . aegypti . The likelihood of co-infection in Singapore was thus assessed to be very low . A comparison of the amino acid sequences of the non-structural and structural polyproteins of D67Y08 ( A226 ) and D1225Y08 ( 226V ) isolates revealed 5 amino acid substitutions in the non-structural polyprotein and 4 in the structural polyprotein ( Table 4 ) . The A226V substitution was the only variation in the E1 envelope protein and the remaining 3 amino acid substitutions in the structural polyprotein were in E2 envelope protein . At the same time , among the 162 epitopes predicted by the Kolaskar & Tongaonkar algorithm in JEMBOSS ( ANTIGENIC ) version 1 . 5 [25] , only 2 epitopes coincided with the amino acid differences observed in the structural polyprotein between DS67Y08 ( CHIKV-A226 ) and DS1225Y08 ( CHIKV-226V ) isolates . Those amino acid substitutions were at residues 677 ( E2 envelope protein ) and 1035 ( A226V in E1 envelope protein ) of the structural polyprotein . Though the epitope was similar for both isolates at residue 677 , the programme predicted different configurations for the two viral variants at residue 1035 ( A226V ) . CHIKV-226V had a single linear , 34 amino acids long ( amino acid positions 1019–1052 ) epitope , while CHIKV-A226 had 2 short epitopes flanking the same region: a 15 amino acid epitope ( amino acid positions 1019–1033 ) and a 18 amino acid epitope ( amino acid positions 1035–1052 ) . Based on these observations , we hypothesized that the structural differences due to the A226V variation may have resulted in 226V antigen being more specific to the paratope of IgM of sera infected with the variant ( 226V ) virus . In the absence of other recognition epitopes , a recombinant E1 antigen with A226 could have much reduced affinity to antibodies produced against the 226V variant , thus rendering a test that relies on an inappropriate A226 E1 gene ineffective during an outbreak involving CHIKV-226V strain . Using whole virus as antigen ( as in the case of MAC-ELISA and EUROIMMUN IFA ) offers more antibody recognition sites . As a result , the difference in sensitivity affected by CHIKV-A226 and 226V as antigens was not very prominent . However , as E1 and E2 envelope proteins exist as a heterodimer on the alphavirus surface , contribution of amino acid substitutions in the E2 protein to the observed differences between two antigens could not be underestimated and will be of future interest . The sensitivity of each RT-PCR protocol was not altered by the virus used . However , the probe-based PCR protocol was at least 10 times more sensitive than the SYBR Green assay . Nevertheless , the SYBR Green method was maintained as the routine test at EHI , due to the following considerations: 1 ) the cost of the SYBR Green assay was half of that of the probe-based; 2 ) the SYBR Green assay took 30 minutes , while the probe-based assay required 2 . 5 hours; and 3 ) the SYBR Green assay was sensitive enough for routine diagnosis of acute cases . Our previous study has shown that the SYBR Green method was able to detect viral RNA after resolution of fever in 30% of cases [12] . The method also detected three asymptomatic viraemic cases , one day prior to their onset of fever [13] . Taken together , it was concluded that the SYBR method was a cost effective tool for the diagnosis and surveillance of chikungunya fever . The kinetics of viraemia in patient samples were previously examined and high levels of viraemia were observed during the first 5 days of illness [12] . Combining previous molecular findings [12] and current serology findings , medical practitioners in Singapore have been encouraged to request for PCR-based assays for patients who present within five days of fever and IgM assays for those with fever for more than 5 days . We have found that both EUROIMMUN IFA and MAC-ELISA assays were suitable for outbreaks involving both A226 and 226V variant viruses . For IgM test , MAC-ELISA has the advantage of being cost effective and easy to perform , whereas commercial EUROIMMUN IFA is suitable in laboratories with limited capacity for setting up in-house ELISA systems . An improved rapid test would benefit the community too . For PCR , the SYBR Green protocol was cost effective for the diagnosis of acute patients . This study demonstrates the importance of evaluation of commercial kits and published protocols before application of a diagnostic tool in the clinical and operational settings . With a cost effective and reliable in-house ELISA assay , as demonstrated in this study , the time course of IgM in CHIKV infected individuals is currently being investigated . | Chikungunya is a mounting public health concern in many parts of the world . Definitive diagnosis is critical in differentiating the diseases , especially in dengue endemic areas . There are some commercial chikungunya kits and published molecular protocols available , but no comprehensive comparative evaluation of them was performed . Using sera collected in outbreaks caused by two variants of Chikungunya virus ( A226 and 226V ) , we tested 2 commercial IgM tests ( CTK lateral flow rapid test and EUROIMMUN IFA ) alongside our in-house IgM assays ( using both variants of the virus ) . Sensitivities of 2 published PCR protocols were also evaluated based on RNA standards derived from cell-cultured viruses . The commercial assays had different performances in each outbreak , with CTK's lateral flow test having the best performance in the first outbreak and EUROIMMUN IFA being more sensitive in the second outbreak . Use of the current circulating virus in a test assay improves sensitivity of the MAC-ELISAs . For PCR , a probe-based real time RT-PCR method was found to be 10 times more sensitive than the SYBR Green method . Despite this , the latter protocol is found to be more suitable and cost-effective for our diagnostic laboratory . This evaluation demonstrates the importance of appraisal of commercial kits and published protocols before application of a diagnostic tool in the clinical and operational setting . | [
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] | 2010 | Evaluation of Chikungunya Diagnostic Assays: Differences in Sensitivity of Serology Assays in Two Independent Outbreaks |
Sri Lankan Russell’s viper ( Daboia russelii ) envenoming is reported to cause myotoxicity and neurotoxicity , which are different to the effects of envenoming by most other populations of Russell’s vipers . This study aimed to investigate evidence of myotoxicity in Russell’s viper envenoming , response to antivenom and the toxins responsible for myotoxicity . Clinical features of myotoxicity were assessed in authenticated Russell’s viper bite patients admitted to a Sri Lankan teaching hospital . Toxins were isolated using high-performance liquid chromatography . In-vitro myotoxicity of the venom and toxins was investigated in chick biventer nerve-muscle preparations . Of 245 enrolled patients , 177 ( 72 . 2% ) had local myalgia and 173 ( 70 . 6% ) had local muscle tenderness . Generalized myalgia and muscle tenderness were present in 35 ( 14 . 2% ) and 29 ( 11 . 8% ) patients , respectively . Thirty-seven patients had high ( >300 U/l ) serum creatine kinase ( CK ) concentrations in samples 24h post-bite ( median: 666 U/l; maximum: 1066 U/l ) . Peak venom and 24h CK concentrations were not associated ( Spearman’s correlation; p = 0 . 48 ) . The 24h CK concentrations differed in patients without myotoxicity ( median 58 U/l ) , compared to those with local ( 137 U/l ) and generalised signs/symptoms of myotoxicity ( 107 U/l; p = 0 . 049 ) . Venom caused concentration-dependent inhibition of direct twitches in the chick biventer cervicis nerve-muscle preparation , without completely abolishing direct twitches after 3 h even at 80 μg/ml . Indian polyvalent antivenom did not prevent in-vitro myotoxicity at recommended concentrations . Two phospholipase A2 toxins with molecular weights of 13kDa , U1-viperitoxin-Dr1a ( 19 . 2% of venom ) and U1-viperitoxin-Dr1b ( 22 . 7% of venom ) , concentration dependently inhibited direct twitches in the chick biventer cervicis nerve-muscle preparation . At 3 μM , U1-viperitoxin-Dr1a abolished twitches , while U1-viperitoxin-Dr1b caused 70% inhibition of twitch force after 3h . Removal of both toxins from whole venom resulted in no in-vitro myotoxicity . The study shows that myotoxicity in Sri Lankan Russell’s viper envenoming is mild and non-life threatening , and due to two PLA2 toxins with weak myotoxic properties .
Snake bite is a significant public health issue in the tropics [1] . Coagulopathy , neuromuscular paralysis , acute kidney injury and local effects are the most important clinical syndromes of snake envenoming [2] . Russell’s viper bites cause a large number of envenomings across Asia , and are more medically important than any other snake in the region [3 , 4] . Both species of Russell’s vipers , i . e . D . russelii ( found in Sri Lanka , India , Pakistan , Nepal , Bangladesh ) and D . siamensis ( found in some parts of southeast and east Asia such as Cambodia , Myanmar , Thailand , Taiwan , South China and , some parts of Indonesia including East Java ) , commonly cause venom-induced consumption coagulopathy , acute kidney injury and mild local effects throughout their distribution [3 , 5] . However , there is geographical variation in the clinical effects of Russell’s viper envenoming with neuromuscular paralysis and rhabdomyolysis only reported from Sri Lanka and South India [3 , 6–9] . In a recent clinical and neurophysiological investigation of neuromuscular paralysis in Sri Lankan Russell’s viper envenoming , we showed that the paralysis is mild , non-life threatening with no long term effects [6] . Clinical evidence of myotoxicity , including local and generalized myalgia , muscle tenderness , and dark red or black coloured urine suggestive of myoglobinuria , has been reported in cases of Russell’s viper envenoming in Sri Lanka [7–10] . Elevation of plasma and urinary myoglobin concentrations were reported in 19 Russell’s viper bite patients from Sri Lanka further suggesting the existence of myotoxicity in Sri Lankan Russell’s viper patients [10] . A recent study of Sri Lankan Russell’s viper venom injected into mice reported an elevation in creatine kinase , also suggesting that the venom is myotoxic in-vivo [11] . Myotoxicity is an important effect of snake envenoming and can manifest locally and systemically [12] . Local muscle necrosis is a component of the local necrotic effects [12 , 13] . Systemic myotoxicity has been reported following envenoming by some vipers [14 , 15] , sea snakes [16–18] , Australasian elapids [19 , 20] and some Asian kraits [21 , 22] . Systemic myotoxicity ranges in severity . Loss of functioning muscle cells due to myotoxicity can aggravate co-existing weakness due to neuromuscular block caused by neurotoxins . More importantly rhabdomyolysis can cause secondary acute kidney injury [23 , 24] and itself can result in life-threatening hyperkalaemia due to extensive muscle cell damage [15] . Although many cytotoxic components in snake venoms may contribute to the development of myotoxicity , the most important snake venom myotoxins are phospholipase A2 ( PLA2 ) toxins [12 , 25] . Three types of PLA2 myotoxins , commonly referred as Asp49 , Ser49 and Lys49 PLA2 , have been characterised in viperid venoms . Despite structural similarity , the latter two types lack catalytic activity , and are referred to as ‘PLA2 like’ peptides [26–28] . It is important to note that the enzymatic activity and the myotoxic activity of PLA2 myotoxins are independent [29] . Myotoxic PLA2s cause muscle damage primarily by destruction of the sarcolemma [12 , 30] . Several PLA2 toxins have been isolated from Sri Lankan Russell’s viper venom and biochemically characterised , [31 , 32] Some of these PLA2 toxins contain a unique Serine residue at the N-terminus ( S-type ) , while others have an Asparagine residue at the N-terminus ( N-type ) [32] . We have recently shown that the pre-synaptic neurotoxicity of the Sri Lankan Russell’s viper venom is primarily due to one of the S-type PLA2 toxins , which we named U1-viperitoxin-Dr1a [33] . There are several gaps in our understanding of the myotoxicity associated with Sri Lankan Russell’s viper envenoming . It is unclear if Sri Lankan Russell’s viper envenoming causes severe myotoxicity and whether any resultant myotoxicity can be treated with Indian Polyvalent antivenom . Further , the in vitro myotoxicity of Sri Lankan Russell’s viper venom has not been pharmacologically studied . This requires isolation and pharmacological characterisation of the myotoxins from the venom . The present study aims to investigate the clinical severity of myotoxicity from Russell’s viper envenoming and isolate the toxins responsible for this activity .
The plasma CK concentrations of rats injected with Sri Lankan Russell’s viper venom 250 μg/kg ( i . m . ) were not different tocontrol rats and were within the normal range until 6 h of the venom injection ( S1 Fig ) . Analysis of U1-viperitoxin-Dr1b by MALDI-TOF mass spectrometry indicated a single species with a mass/charge ratio of 13564 Da ( Fig 4 ) . A doubly charged ion with a mass/charge ratio of 6779 . 8 Da was also observed .
The present study demonstrates that myotoxicity seen in humans following Sri Lankan Russell’s viper envenoming is mild , based on the relatively low frequency of generalized myotoxic features and only a very mild elevation in CK . There were small but statistically significant association between clinical features of myotoxicity and the 24 h CK measurements . The rats injected intramuscularly with Sri Lankan Russell’s viper venom did not show an elevation of CK within the first 6 h . Although Sri Lankan Russell’s viper venom displays concentration-dependent myotoxic effects in-vitro , very high venom concentrations ( i . e . >50 μg/ml ) are required to completely inhibit direct twitches in the chick biventer nerve-muscle preparation and the response to KCl . Antivenom at recommended concentrations was unable to neutralise the myotoxic properties of the venom in-vitro . The myotoxicity of the whole venom appears to be due to two abundant PLA2 toxins in the venom , U1-viper1toxin-Dr1a and U1-viperitoxin-Dr1b ( named herein ) , which both have in vitro myotoxic activity , but only at very high concentrations ( 3 μM ) . Features of systemic myotoxicity such as generalized muscle tenderness and myalgia , and dark coloured urine were observed in less than 15% of the patients . In addition , three of the six patients with dark coloured urine did not have 24 h CK concentrations outside the normal limit , so the dark urine was more likely due to macroscopic haematuria ( due to haemorrhage ) which occurs with Russell’s viper envenoming . Peak CK concentrations were abnormally high in less than 17% of patients , and even in these cases there were only modest elevations with the median and maximum being 666 and 1066 U/l , respectively . In comparison , the reported median peak CK values following envenoming by mulga snakes and tiger snakes in Australia were 3100 U/l [20] and 4749 U/l [19] , with severe myotoxicity being associated with CK values over 100 , 000 U/L . This means that myotoxicity in Russell’s viper envenoming is uncommon and mild when it occurs compared to other myotoxic snakes . The median 24 h CK concentrations were higher in symptomatic patients demonstrating that the clinical features were consistent with biochemical evidence of mild muscle injury . Anaesthetised rats injected with venom showed no significant increase in serum CK concentrations compared to saline injected rats , even 6 h after the venom injection . Assuming that the blood volume of a rat with a body weight of 330g is approximately 25 ml , the maximum serum venom concentration that this venom dose ( i . e . 250 μg/kg ) , with 100% systemic absorbance , could give rise to is 3300 ng/ml . This venom concentration is greater than the maximum venom concentration observed in the Russell’s viper envenomed patients in this study , hence the venom dose used for the envenoming is clinically relevant . The absence of a significant CK rise in the envenomed rats during the 6 h observation period may be due to a combined effect of the weak myotoxicity of the venom and the period of observation being too short to observe any CK elevation . These results are not in agreement with the previously reported high CK concentrations ( mean: 16 , 000 U/l ) in mice , 3 h after the intramuscular injection of 5 μg ( 250 μg/kg dose in 20 g mouse ) of Sri Lankan Russell’s viper venom [11] , despite similar venom doses in the two studies . The reason for the discrepancy is unclear but such a large early increase in CK concentrations is unusual and the mouse model has not previously been validated . The clinical findings were consistent with the in vitro studies in which two low potency myotoxins were found to be relatively abundant in the venom . Very high venom concentrations up to 80 μg/ml ( 80 times higher than the maximum peak venom concentration observed in envenomed patients ) were required to decrease the direct twitch force by two thirds within 3 h . The low potency of both myotoxins was evident from the fact that high concentrations ( 3 μM ) were required for significant in-vitro myotoxic activity . Venom with these two toxins removed did not cause myotoxicity confirming that these toxins are the only major myotoxins in Sri Lankan Russell’s viper venom . Three previous studies between 1984 and 2000 report much higher rates of myotoxicity , based mainly on myalgia and muscle tenderness . In 1984 , Jeyarajah [9] reported myotoxic symptoms in 77% of the cases but no biochemical confirmation of myotoxicity . This study included only severe envenoming with 19/22 with acute kidney injury and 6 deaths . In 1988 , Phillips et . al . [10] reported myotoxic symptoms in 32% of the cases , but again this only included more severe envenoming ( 4/23 died ) . The mean venom concentration of 375 ng /ml ( range: 16 . 5–702 ng/ml ) [10] was much higher compared to the median peak venom concentration of 25 ng/ml ( range:2 . 5–2316 ng/ml ) in our study [6] . Phillips et al . reported serum and urine myoglobin , which are difficult to interpret because no studies have correlated these measurements with outcomes . They detected myoglobin in the plasma of all 19 patients tested ( range: 100->8000; median: 2745 , normal value:<50 ng/ml ) and in the urine in 14 of 18 patients ( 110 to >16 000; median: 4000; normal: <21 ng/ml ) [10] . Although these appear to be high , much higher values are reported in Australian mulga snake envenoming , where urinary myogolobin values in three patients were 4129 , 28200 and 127000 ng/ml[20] . In a larger study of 336 patients in 2000 by Kularatne et al [7] there were 47 patients ( 14% ) with myotoxicity based on generalised muscle tenderness . This is consistent with our study in terms of clinical effects but they did not provide any biochemical confirmation . Our 245 patients are typical of current patients . They had high circulating peak venom concentrations with a median of 25ng/ml , with 24 patients having venom concentrations >1000 ng/ml [6] . Further , nearly all had local envenoming , 68% had coagulopathy ( half with bleeding manifestations ) , and 53% had neuromuscular paralysis [6] . Therefore , the mild myotoxic effects in Sri Lankan Russell’s viper envenoming are most likely due to the weak myotoxicity of the venom . In patients with Australian Mulga snake ( Pseudechis australis ) envenoming , a delay in antivenom treatment has led to an increase in the severity of the myotoxicity [20] . Since there was no control group of patients who did not receive antivenom in this study , it could be argued that the weak myotoxicity observed in Sri Lankan Russell’s viper envenoming is due to an effect of antivenom . However , there was no association between the delay in antivenom treatment and the number of patients having CK >300 U/l , indicating that the weak myotoxicity observed in the patients is not due to an effect of the antivenom . Indian Polyvalent antivenom was used at concentrations equivalent to the tested venom as recommended by the manufacturer . At these concentrations antivenom failed to prevent the myotoxic effects of Sri Lankan Russell’s viper venom in-vitro . Although this may be due to the low efficacy of the antivenom against the myotoxins [39] , testing higher antivenom concentrations was not possible due to the practical limitations of increasing antivenom concentration in the tissue organ bath environment without affecting the osmolarity of the physiological salt solution . It was therefore not possible to determine the efficacy of the Indian polyvalent antivenom to neutralise the myotoxic effects of Sri Lankan Russell’s viper venom , because such a large amount of venom was required to cause myotoxicity requiring very high concentrations of antivenom . Of the several ‘s’ type PLA2 toxins isolated from Sri Lankan Russell’s viper venom , VRV-PL-VIIIa [40] has 100% match with the aligned trypsin digested peptide fragments of U1-viperitoxin-Dr1b . The N-terminal sequence of the first 50 amino acids of the toxin ‘P1’ [31] and the N-terminal sequence of first 21 amino acids of the toxin PLA2 4 [32] are a 100% match for the sequence of U1-viperitoxin-Dr1b . Following the suggested rational nomenclature for toxins [41] , and given that the 94% sequence homology of the toxin with U1-viperitoxin-Dr1a[33] , we have named the above toxin as U1-viperitoxin-Dr1b ( Fig 6 ) . The recent study on the venom proteome of the Sri Lankan Russell’s viper [11] reported that five PLA2 toxins make up a relative abundance of 35% of the whole venom , with VRV-PL-VIIIa ( U1-viperitoxin-Dr1b ) making 13 . 9% ( as opposed to 22 . 2% in our study ) of the whole venom . However , the same study did not match a PLA2 with VRV-PL-V ( U1-viperitoxin-Dr1a ) , which makes 19 . 2% of the venom in our analysis as previously published [33] . In conclusion , myotoxicity in Sri Lankan Russell’s viper envenoming is mild and non-life threatening , as evident from the low frequency of generalized myotoxicity and low concentrations of serum creatine kinase in envenomed patients . The whole venom of Sri Lankan Russell’s viper has weak myotoxic properties in-vitro . Two PLA2 toxins , U1-viperitoxin-Dr1a and U1-viperitoxin-Dr1b that make 42% of the whole venom , are the major myotoxins in the venom , but display weak myotoxicity . There is a small possibility that myotoxicity may occur in patients with severe Sri Lankan Russell’s viper envenoming in which antivenom is delayed . | There are many gaps in our knowledge of muscle damage caused by snake venoms . Russell’s vipers are more medically important than any other snake in Asia . Sri Lankan Russell’s viper ( Daboia russelii ) bites have been reported to cause muscle damage in humans , which is not reported for other Russell’s vipers . The aim of the present study was to investigate the onset , severity and resolution of the muscle damage and to identify the toxins responsible for myotoxicity . For this , we studied muscle damage in 245 patients with confirmed Sri Lankan Russell’s viper bites . Patients reported local muscle pain in 72% of cases and generalised muscle pain in 15% . None had severe muscle damage and the symptoms resolved in 80% of patients within 4 days . Measurement of biomarkers of muscle damage in patient blood was consistent with only mild muscle injury , even in patients with symptoms . Two toxins were isolated from Sri Lankan Russell’s viper venom that had similar myotoxic activity to whole venom in chick muscle preparations . Both toxins were weak myotoxins , consistent with what was seen in patients . | [
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] | 2016 | Clinical and Pharmacological Investigation of Myotoxicity in Sri Lankan Russell’s Viper (Daboia russelii) Envenoming |
The ability of Mycobacterium tuberculosis ( Mtb ) to thrive in its phagosomal niche is critical for its establishment of a chronic infection . This requires that Mtb senses and responds to intraphagosomal signals such as pH . We hypothesized that Mtb would respond to additional intraphagosomal factors that correlate with maturation . Here , we demonstrate that [Cl−] and pH correlate inversely with phagosome maturation , and identify Cl− as a novel environmental cue for Mtb . Mtb responds to Cl− and pH synergistically , in part through the activity of the two-component regulator phoPR . Following identification of promoters responsive to Cl− and pH , we generated a reporter Mtb strain that detected immune-mediated changes in the phagosomal environment during infection in a mouse model . Our study establishes Cl− and pH as linked environmental cues for Mtb , and illustrates the utility of reporter bacterial strains for the study of Mtb-host interactions in vivo .
Mycobacterium tuberculosis ( Mtb ) causes a chronic infection in approximately one third of the human population and remains an important public health problem [1] . The macrophage ( MØ ) is the major host cell for much of Mtb's life cycle , and a defining feature of Mtb's pathogenesis is its ability to arrest full maturation of the phagosome in which it resides [2] , [3] . Indeed , Mtb mutants that fail to arrest phagosomal maturation have reduced survival during MØ infection [4] . However , Mtb remains subject to multiple stresses within the phagosome , which may act as important environmental cues for Mtb [5] . Proper sensing of such signals informs Mtb of its surroundings , allowing the bacterium to respond appropriately to ensure its survival and replication . Elucidating the cues that Mtb recognizes during infection , and the possible interplay between such signals , is critical for a complete understanding of the impact of the microenvironment on Mtb pathogenesis and persistence , and Mtb's interaction with fundamental host cell processes . One environmental cue that has received particular attention is pH; the Mtb phagosome acidifies to an intermediate pH of 6 . 4 [3] , [4] , [6] , and even in medium , the bacterium exhibits a profound transcriptional response to acidic pH [5] , [7] , [8] . The abolition of phagosome acidification during bacterial uptake by MØs , through treatment with concanamycin A , eliminates a majority of Mtb's transcriptional response , indicating the importance of pH as a signal for the bacterium in sensing and responding to its environment [5] . The process of acidification does not , however , proceed in isolation . Specifically , acidification ( increase in [H+] ) must be counterbalanced by efflux of other cations from the phagosome , and/or by the uptake of a counter anion . We hypothesized that Mtb might also take advantage of this counterbalancing factor as an environmental cue , expanding the sensitivity and dynamic range of its ability to define its immediate environment . Cell biological studies have established Cl− as a major counter anion during acidification of the endosome [9]–[11] . Several Cl− channels are known to be present on the endosomal membrane [12] , [13] , although it remains controversial which of these channels are involved in the counter-balancing of increased [H+] during endosomal maturation [14] , [15] . More recent studies have also proposed efflux of cations , such as K+ , as a counter mechanism to increased [H+] in the lysosome [16] . The existence of such mechanisms have not , however , been formally shown for phagosomes . In this context , it is of particular note that the Mtb phagosome has been reported to possess a high [Cl−] [17] . The impact of common ions and changes in their concentration on Mtb during infection is a concept that is just beginning to be appreciated [18]; however , much remains to be determined regarding their physiological significance . In this study , we show that [Cl−] increases during phagosome maturation , mirroring a decrease in pH within the compartment . Mtb modulates its transcriptional profile in response to [Cl−] , and reacts to the environmental cues of pH and [Cl−] in a synergistic manner , with the two-component regulatory system phoPR playing a central role in this response . By constructing a fluorescent reporter Mtb strain responsive to both Cl− and pH , we were further able to directly examine the microenvironment of Mtb during in vivo infection in a mouse model . Maturation of Mtb-containing phagosomes is known to be impacted by the immune status of the MØ . Infection of wild type versus immune-deficient interferon-γ−/− mice revealed differential induction of fluorescence in vivo , and demonstrated the influence of host immune pressure on the microenvironment in which Mtb resides . These data were further validated with a second Mtb reporter strain , expressing GFP under the regulation of the more fully-characterized hypoxia and nitric oxide-responsive dosR regulon [19]–[22] . The results confirm existing hypotheses concerning localized immune-mediated pressure within infection foci , and provide a new generation of tools to probe the fitness and viability of Mtb in in vivo infection models .
We first sought to establish the dynamics of [Cl−] during maturation of the phagosome with model particles . The fluorescent Cl−-sensitive , pH-insensitive compound 10 , 10′-Bis[3-carboxylpropyl]-9 , 9′-biacridinium ( BAC ) [9] was synthesized as a trifluoroacetate salt , and coupled to IgG beads . As previously reported , BAC fluorescence is quenched by Cl− in a concentration-dependent manner , and is unaffected by pH changes ( Figure S1 in Text S1 ) [9] . To track [Cl−] changes during phagosomal maturation we attached Alexa Fluor 594 ( AF594 ) as a calibration fluorophore to the BAC-IgG beads . These dual-color Cl− sensor beads were added to murine bone marrow-derived MØs and fluorescence measured in a microplate reader . We observed an increase in AF594/BAC fluorescence ratios over time , indicating an increase in [Cl−] as the phagosome matured ( Figure 1A ) . This increase in [Cl−] was also observed with phagosome maturation in MØs derived from human monocytes ( Figure 1B ) . To calibrate AF594/BAC ratios to actual [Cl−] , we treated MØs that had phagocytosed Cl− sensor beads with bafilomycin A1 and the ionophores nigericin and monensin in buffers of known [Cl−] . By fitting a polynomial regression to the standard curve ( Figure S2 in Text S1 ) , we calculate that phagosomal [Cl−] reached a maximal concentration of ∼70–95 mM . As this is a population-based measurement , we note that this value range underestimates the [Cl−] that can be reached in individual phagosomes ( see below ) . We further examined the dynamics of [Cl−] increase during phagosome maturation by tracking individual beads during phagocytosis by live-cell time-lapse microscopy . These experiments showed that the switch from low [Cl−] to high [Cl−] occurred for most beads , although a subset remained in phagosomes with low [Cl−] ( Figure 1C and Video S1 ) . Imaging of populations of Cl− sensor bead-containing cells at given time points illustrated the heterogeneity in [Cl−] attained in individual phagosomes , with measurements indicating that a [Cl−] greater than 120 mM was reached in some phagosomes ( Figure 1D ) . Similar results were obtained in MØs derived from human monocytes ( data not shown ) . Cl− sensor beads present in media alone and imaged in parallel did not show significant changes in fluorescence , demonstrating that the decrease in BAC fluorescence observed in the phagocytosed beads has a biological basis and is not due to bleaching of the fluorescent signal during imaging ( Video S2 ) . In examining these results , we noted that the increase in [Cl−] mirrored the kinetics of the decrease in phagosomal pH previously reported [23] . In order to quantify this correlation directly within a single experiment , we coupled BAC to IgG beads in combination with the red fluorescent pH sensor pHrodo , which exhibits an increase in fluorescence as pH decreases . Measurement of the fluorescence profile of the beads during phagocytosis by MØs showed the previously observed quenching of BAC signal indicative of increased [Cl−] as the phagosome matured ( Figure 1E ) . pHrodo fluorescence on the same particles exhibited an inverse profile , increasing in intensity over time , signaling a decrease in pH ( Figure 1E ) . Analysis of the phagocytosis of the BAC/pHrodo indicator beads by live-cell time-lapse microscopy further verified these results at the individual phagosome level ( Figure 1F and Video S3 ) . Similar profiles were observed in MØs derived from human monocytes ( data not shown ) . We also verified that BAC/pHrodo beads imaged in media alone did not show such changes in fluorescence profile ( Video S4 ) . Further support for the relation between [Cl−] and pH during phagosomal maturation was demonstrated by the failure of phagosomal [Cl−] to increase when MØs were treated with bafilomycin A1 ( Figure S3A in Text S1 ) . Similarly , addition of bafilomycin A1 to the MØs after phagosomes containing the Cl− sensor beads had initially been allowed to mature resulted in increased BAC fluorescence , indicating a reversal of the Cl− accumulation upon dissipation of the pH gradient ( Figure S3B in Text S1 ) . Together , these results demonstrate that [Cl−] increases during phagosomal maturation , and supports the proposed functional relationship between acidification of the endosomes and [Cl−] increase [9]–[11] . Mtb shows a marked transcriptional response upon exposure to acidic pH , and we have previously shown that almost half of the Mtb genes upregulated during an early stage of MØ infection are induced in a pH-dependent manner [5] . Given our results indicating [Cl−] increase during phagosomal maturation and the link between [Cl−] and acidification , we compared the transcriptional profiles of Mtb grown in regular 7H9 media to those grown in 7H9 media supplemented with 250 mM NaCl for 4 hours . The number of genes ( 32 ) upregulated on exposure to high [Cl−] was noticeably fewer than the hundreds previously reported to be induced under acidic pH ( Table 1 ) [5] , [7] . Strikingly however , a significant number of genes that were upregulated in the presence of high [Cl−] ( 18/32 ) were genes that also showed increased expression during exposure to acidic pH ( Table 1 ) . The upregulated gene expression detected by microarrays was validated by semi-quantitative real time PCR ( qRT-PCR ) for several genes . These experiments were also carried out on samples exposed to acidic pH ( pH 5 . 7 ) , and showed data consistent with the microarray analysis ( Figure S4 in Text S1 ) . While our microarray platform allows for the global analyses of gene expression changes , it does have a flattened dynamic range [5] , [24] , and the qRT-PCR data indicate that the actual level of induction is considerably greater . These experiments indicate that Mtb responds transcriptionally to Cl− , and further reinforce the idea that pH and Cl− may function as interconnected environmental cues for Mtb during the course of infection . To perform analyses of Cl− and pH as environmental cues for live Mtb , we utilized the microarray and qRT-PCR results to select candidate genes for construction of a reporter Mtb strain that would be responsive to both changes in [Cl−] and pH . We focused on the rv2390c-rpfD operon , which appeared particular promising as both genes in the operon showed robust induction under conditions of high [Cl−] or acidic pH ( Figure S4 in Text S1 ) . The promoter region of rv2390c was cloned upstream of GFP in a replicating plasmid , and transformed into Mtb CDC1551 . This CDC1551 ( rv2390c'::GFP ) reporter strain was then grown in media +/− 250 mM NaCl , buffered to pH 7 . 0 to study [Cl−] effects at neutral pH , or in media buffered at pH 5 . 7 , without added NaCl . Using FACS analysis , we observed an increase in GFP fluorescence of CDC1551 ( rv2390c'::GFP ) in conditions of high [Cl−] or acidic pH over time , with peak inductions of 7–9 fold over control in each instance ( Figure 2A ) . To verify the Cl−-specificity of the response , we tested several other compounds for their ability to induce GFP fluorescence in CDC1551 ( rv2390c'::GFP ) , including KCl , arginine-HCl , Na2SO4 , and sucrose , in media buffered at pH 7 . 0 . Induction was observed with compounds containing Cl− , but not with Na2SO4 and sucrose , indicating that Cl− was the agent responsible for the increase in GFP signal , and suggesting that neither Na+ nor osmolarity were contributory factors ( Figure S5 in Text S1 ) . Induction of rv2390c'::GFP expression was also reversible , with GFP fluorescence returning to baseline levels within 5 days of removal of the high [Cl−] stimulus in log-phase bacteria ( Figure S6 in Text S1 ) . These data , along with the lack of induction observed with other stressors such as NO and hypoxia ( Figure S7 in Text S1 ) , argue for the usefulness of CDC1551 ( rv2390c'::GFP ) as a specific reporter Mtb strain for the intraphagosomal cues of pH and Cl− . To determine if Mtb's response to Cl− occurs in a concentration-dependent manner , we repeated the time-course induction assays with media containing different [Cl−] at pH 7 . 0 . GFP fluorescence of CDC1551 ( rv2390c'::GFP ) increased as [Cl−] rose , showing Mtb's ability to modulate its response to [Cl−] in a manner comparable to a rheostat ( Figure 2B ) . In agreement with a previous study reporting Mtb's dynamic response to diminishing pH [24] , we also observed increasing GFP signal with decreasing pH for CDC1551 ( rv2390c'::GFP ) ( Figure 2C ) . These results further demonstrate the usefulness of CDC1551 ( rv2390c'::GFP ) as a reporter Mtb strain for Cl− and pH , and indicate that Mtb's response to these two environmental cues is fine-tuned by its environment . To test whether Cl− and pH might act synergistically as intraphagosomal cues , we incubated CDC1551 ( rv2390c'::GFP ) in media buffered at pH 5 . 7 , with 250 mM NaCl . These conditions resulted in induction of GFP fluorescence to a level ( >50 fold ) much greater than merely the sum of the GFP signal obtained when the bacteria were grown in conditions with only one cue ( high [Cl−] or acidic pH ) ( Figure 3A ) . qRT-PCR tests on several genes in wild type Mtb ( WT ) exposed to the different conditions confirmed the synergistic activity ( Figure 3B ) . This synergy implied cross-talk between regulatory circuits . In particular , we examined the role of the two-component regulator phoPR , a system previously shown to be required for expression of the acid and phagosome-regulated locus aprABC [24] , and whose regulon significantly overlaps the list of genes regulated in a pH-dependent manner during MØ infection [5] , [8] . We found that unlike WT , a phoP::Tn mutant carrying the rv2390c'::GFP reporter failed to induce GFP fluorescence during growth at acidic pH , supporting the critical role of phoP in regulating Mtb's response to pH ( Figure 3C ) . Our experiments further indicated that phoP also played a role in regulating Mtb's response to Cl− , as induction of the GFP reporter signal during growth in high [Cl−] was reduced in the phoP::Tn mutant as compared to WT ( 1 . 5–2 fold vs . 7–9 fold ) ( Figure 3C ) . Intriguingly , GFP induction with the reporter phoP::Tn mutant in conditions of high [Cl−] at acidic pH ( 4 fold ) was still greater than that observed with high [Cl−] alone , despite the lack of induction with acidic pH as a single signal ( Figure 3C ) . qRT-PCR analyses on a ΔphoPR Mtb mutant , as well as a complemented ΔphoPR strain ( phoPR* ) , confirmed these data . There was decreased induction of target transcript in conditions of high [Cl−] alone or high [Cl−] at acidic pH in the ΔphoPR mutant as compared to WT ( 3 vs . 5 fold and 12 vs . >50 fold respectively ) , and no increase in transcript at acidic pH for the mutant ( Figure 3D ) . Genetic complementation restored transcript induction in the mutant to WT levels ( Figure 3D ) . These results implicate phoPR as a regulator that modulates Mtb's response to Cl− , while also indicating that it is merely one part of a regulatory circuit that impacts this response . Having established that Mtb's response to Cl− and pH is interconnected in vitro , we next pursued these studies in the context of MØ infection by Mtb . To make use of the rv2390c'::GFP reporter for these intracellular studies , we first moved the construct into a replicating plasmid containing mCherry driven by the constitutive promoter smyc [24] , [25] , to generate the strain CDC1551 ( rv2390c'::GFP , smyc'::mCherry ) ( Figure S8 in Text S1 ) . This allows visualization of all bacteria regardless of reporter induction levels , and an internal calibration of the GFP signal . Activation of MØs prior to infection with Mtb is known to increase the maturation stage and lower the pH of the bacteria-containing vacuoles [26] , [27] , which should increase induction of GFP expression as a function of both pH and [Cl−] . Resting or activated murine bone marrow-derived MØs were infected with the reporter Mtb strain , and samples examined by confocal microscopy . We observed increased GFP fluorescence as the infection progressed , with significantly more induction of GFP signal in the activated MØs ( Figures 4A and 4B ) . This difference in the microenvironment experienced by Mtb during infection of resting or activated MØ was even more starkly illustrated by pre-incubating the reporter Mtb in conditions of high [Cl−] prior to MØ infection . In this case , the inoculating bacteria had an increased level of rv2390c'-driven GFP expression at the start of infection , and exhibited an enhanced divergence in GFP signal between the resting and activated MØs ( Figures 4C and 4D ) . These experiments indicate that Mtb experiences different [Cl−] and pH during MØ infection , dependent on the activation status of the host MØ , and points to dynamic regulation of its gene expression in response to these environmental cues . The MØ experiments above demonstrate the feasibility of using the CDC1551 ( rv2390c'::GFP , smyc'::mCherry ) reporter strain to reveal important aspects of Mtb's microenvironment during infection . We sought to test the utility of this reporter system in a whole animal infection where the infection foci will likely present regional variation in immune responsiveness and heterogeneous levels of MØ activation . To probe if we could detect regional variation in immune-mediated modulation of infected MØs , we infected C57BL/6J WT or isogenic interferon-γ−/− ( IFNγ−/− ) mice with Erdman ( rv2390c'::GFP , smyc'::mCherry ) via intranasal inoculation . IFNγ−/− mice fail to properly activate their MØs on infection and are susceptible to Mtb , developing a disseminated infection that is fatal [28] , [29] . The Erdman strain was used for these experiments , as it establishes robust infection in mice . In vitro tests show that the Erdman reporter strain responds similarly to both Cl− and pH ( Figure S9 in Text S1 ) . Infected mice were sacrificed at 14 and 28 days post-challenge , and lung tissue examined by confocal microscopy . We observed significantly higher GFP fluorescence in the reporter strain in WT vs . IFNγ−/− mice at each time point examined ( Figures 5A and 5B ) . In the case of IFNγ−/− mice , we also noted a disseminated infection , in agreement with previous studies ( Figure 5A ) [28] , [29] . These results faithfully reproduce our MØ experiments since IFNγ−/− mice , which are unable to activate their MØs , exhibit reduced expression of the GFP reporter signal . To further examine the impact of host immune pressure on determining Mtb's microenvironment , we used host inducible nitric oxide synthase ( iNOS ) expression as an indicator of immune activation in WT mice at 28 days post-infection . This allowed us to compare Mtb resident in regions with vs . without an active immune response , within a single infected WT host . A first observation was that most Mtb were located in iNOS-positive regions in the mouse lung tissue ( Figures 5C and 5D ) . Significantly however , we found greater reporter GFP fluorescence in the bacteria residing in iNOS-positive regions vs . those located in iNOS-negative regions ( Figure 5D ) . This result reinforces the concept that host immune pressure can impact substantially on the cues that Mtb responds to in its microenvironment , and that reporter Mtb strains can be exploited to shed light on the signals the bacteria are exposed to during in vivo infection . In particular in the context of the rv2390c'::GFP reporter , it suggests that Mtb experiences a microenvironment with higher [Cl−] and more acidic pH during infection of a host with an activated immune system . While the complex nature of in vivo infection means that it remains possible that there are yet other , unidentified , factors that also contribute to the differential induction of GFP fluorescence observed , the apparent specificity of the rv2390c'::GFP reporter supports the notion of [Cl−] and pH being at least two of the major drivers of the phenotype observed . This is also consistent with the increase acidification of Mycobacterium-containing phagosomes in activated MØs reported previously [4] , [27] , [30] , and supports the contention that the bacteria are delivered live to a compartment that represents a more hostile environment . In order to further validate the utility of reporter strains for studying Mtb infection , we performed additional experiments to examine the possibility of generating a second , independent reporter Mtb strain that would respond to different environmental cues from the rv2390c'::GFP reporter strain . In particular , we pursued in vivo studies with a hspX promoter-driven reporter strain . hspX is a much-studied Mtb gene often used as a marker of expression of the dos regulon , known to respond to hypoxia and NO [19]–[22] . As expected , in vitro , GFP induction of an Erdman ( hspX'::GFP , smyc'::mCherry ) reporter strain varied with O2 tension and NO ( Figures 6A and 6B ) . Confocal microscopy analyses of lung tissue from mice infected with Erdman ( hspX'::GFP , smyc'::mCherry ) showed significantly greater induction of Mtb reporter GFP fluorescence in WT vs . IFNγ−/− mice at both 14 and 28 days post-infection ( Figures 6C and 6D ) . We also observed much greater induction of hspX'-driven GFP signal at 28 days vs . 14 days post-infection , in accord with the reported time-frame of iNOS synthesis during Mtb infection in WT mice ( Figures 6C and 6D ) [31] . Immunofluorescent staining of iNOS further showed significantly higher hspX'-driven GFP fluorescence in Mtb residing in iNOS-positive vs . negative regions in WT mice ( Figure 6E ) . Together with the Erdman ( rv2390c'::GFP , smyc'::mCherry ) results above , these experiments illustrate that both reporter Mtb strains reliably detect and respond to localized regions of immune activation in vivo , and support the usefulness of reporter Mtb strains for studies of Mtb-host interactions . Our finding that Mtb can utilize Cl− as an environmental cue , in synergy with pH , is a first illustration of a pathogen exploiting interlinked host signals during phagosome maturation . Importantly , Mtb responds to these cues not just in vitro but also during in vivo infection , where these signals are modulated by immune activity of the host . Most studies on Mtb and its response to environmental cues have centered on in vitro assays and homogeneous bacterial cultures . While these constitute an important foundation they provide little insight into how Mtb senses and responds to environmental cues in vivo , where the heterogeneity linked to location and immune activation is critical in determining the productiveness of the diverse subpopulations of Mtb present in an infected host [32] . In the current study we validated the two reporter strains for their ability to respond to stresses relevant to their survival in vivo . Using confocal microscopy and rigorous quantification of GFP fluorescence at the level of the individual bacterium , we were able to probe infected mouse tissue and demonstrate that: ( 1 ) GFP expression level was linked to immune activation by IFNγ , ( 2 ) bacteria in regions that stained positive for the activation marker iNOS exhibited higher levels of GFP expression , and ( 3 ) the heterogeneity amongst the bacterial population was as marked as predicted [32] , and can only be revealed by panels of reporter bacteria such as the ones developed in this current study . We feel that these strains represent a new generation of tools to probe the fitness of Mtb in vivo . These strains should enable us to functionally dissect the TB granuloma to identify privileged regions of bacteria growth , or hostile areas of immune containment . We also predict that these strains will be valuable in probing for drug action and tissue penetrance , through enhanced stress , as one tries to improve drug availability in vivo . Extending beyond Mtb , our results also have potential implications for other intracellular organisms that similarly experience compartments with a range of decreased pH , such as the bacteria Coxiella burnetti [33] and Brucella [34] , and the parasite Leishmania [35] . Might these microbes also respond to Cl− , and is the ability to use Cl− and pH as synergistic cues a more widespread phenomenon ? In bacterial studies , Cl− has largely been examined only within the context of salt tolerance and osmolarity . Few reports have studied Cl− itself in the context of bacterial-host interactions , although Radtke and colleagues proposed that increased [Cl−] aided Listeria monocytogenes phagosomal escape by increased activation of listeriolysin O [36] . Our study further raises the question of what roles other common ions might have on bacterial-host interactions . Although ions , such as iron , that serve as essential micronutrients and are actively sequestered by the host have long been recognized as important focal points for bacterial-host interactions [37] , the possible impact of more common ions , like Cl− , remain largely unstudied . In addition to Cl− , we speculate that other common ions , such as K+ , might also act as a signal for infecting bacteria . There are several known bacterial K+ transporters [38] , and these also impact on important aspects such as pH and membrane potential [39] . Indeed , K+ transporter mutants in several bacterial species , including Mtb , have been reported to be attenuated in colonization of their host [40] , [41] . We propose that further study of common ions and their possible role as environmental signals for microbes will yield many more as yet undiscovered aspects of the bacterial-host interface .
All animal procedures were conducted in strict compliance with the National Institutes of Health “Guide for the Care and Use of Laboratory Animals” . The animal protocol was reviewed and approved ( protocol number 2011-0086 ) by the Institutional Animal Care and Use Committee , Cornell University , under the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care , US Department of Agriculture , and the Public Health Service guidelines for the care and use of animals as attested by the National Institutes of Health . All efforts were made to minimize suffering . Bone marrow-derived MØs were isolated from C57BL/6J WT mice ( Jackson Laboratories ) , and maintained in DMEM ( Corning cellgro ) containing 10% FBS ( Thermo Scientific ) , 20% L-cell conditioned media , 2 mM L-glutamine , 1 mM sodium pyruvate and antibiotics ( penicillin/streptomycin ) ( Corning cellgro ) , at 37°C in a 7% CO2 atmosphere . Monocytes isolated from peripheral blood mononuclear cells ( Elutriation Core Facility , University of Nebraska Medical Center ) were grown in DMEM containing 10% human serum ( SeraCare Life Sciences ) , 2 mM L-glutamine , 1 mM sodium pyruvate and antibiotics , and allowed to fully differentiate into MØs before use in assays . Generation of Cl− and Cl−/pH sensor beads are described in the Supplementary Materials and Methods . For plate reader assays , 2×105 MØs/well were seeded in a 96-well black plate ( Corning Costar ) , and for confocal live-cell time-lapse microscopy assays , 4×105 MØs/well were seeded in a Lab-Tek II 8-chambered coverglass ( Nalge Nunc International ) . MØs were washed 3x with pre-warmed assay buffer ( PBS , pH 7 . 2 , 5% FBS , 5 mM dextrose , 1 mM calcium acetate , 1 . 35 mM K2SO4 , 0 . 5 mM MgSO4 ) , and sensor beads added at ∼2–5 beads/MØ in assay buffer . Acquisition of data on a plate reader or by confocal imaging was initiated within 2–3 minutes of bead addition . A Molecular Devices Gemini EM fluorescence plate reader was used for bottom read signal detection ( BAC – Ex . 365 nm/Em . 505 nm , AF594 – Ex . 590 nm/Em . 617 nm , pHrodo – Ex . 560 nm/Em . 585 nm ) , with 4 replicate wells/condition , and temperature control at 37°C . In experiments to establish a calibration curve , at the end of the assay ( 2 hrs ) described above , the MØs were washed 3x with pre-warmed Cl−-free buffer ( 1 . 54 mM KH2PO4 , 2 . 71 mM Na2HPO4 , 69 mM Na2SO4 , 5 mM dextrose , 1 mM calcium acetate , 1 . 35 mM K2SO4 , 0 . 5 mM MgSO4 ) , and then placed in buffer supplemented with specific [NaCl] , 200 nM bafilomycin A1 ( Sigma ) , 10 µM nigericin ( Calbiochem ) , and 10 µM monensin ( Enzo Life Sciences ) . After incubation to allow equilibration , the BAC and AF594 fluorescence signals were read on a plate reader as above . For live-cell time-lapse microscopy , cells were imaged with a Leica SP5 confocal , equipped with a stage enclosed temperature control system . A 364 nm laser line was used for excitation of BAC fluorescence , a 594 nm laser line for Alexa Fluor 594 fluorescence , and a 543 nm line for pHrodo . Emission detection was set at +/−15 nm of the peak emission λ in each case . 10 z-slices over a 12 µm range were acquired at each time point , using the Leica Application Suite Advanced Fluorescence program . Volocity software ( PerkinElmer ) was used for analysis and tracking of individual beads . The Mtb strain CDC1551 was the parental strain for all in vitro and MØ infection experiments . Strains used in mice infections were in the Erdman strain background . Routine culture of Mtb was as previously described [24] . The phoP::Tn mutant was from BEI ( #NR-14776 ) , and has been previously described [24] . Details of the construction of a CDC1551 ΔphoPR mutant and its complemented strain are described in the Supplementary Materials and Methods . Log-phase Mtb ( OD600∼0 . 6 ) was used to seed 10 ml cultures at OD600 = 0 . 3 in 7H9 media buffered at pH 7 . 0 , +/−250 mM NaCl , in standing vented T-25 flasks . RNA samples were collected after 4 hours of treatment , and five biological replicates were tested . RNA isolation , amplification , labeling and analyses by microarrays were carried out as previously described [5] . This microarray dataset is available in the ArrayExpress database under accession number E-MTAB-1374 , and on the TB Database website [42] . qRT-PCR experiments were conducted on cDNA generated from amplified RNA as previously described [24] . To generate CDC1551 ( rv2390c'::GFP ) , a 704 bp region immediately upstream of rv2390c was PCR amplified , placed in front of GFPmut2 [43] in a modified replicating plasmid pSE100 [24] , and transformed into CDC1551 . The rv2390c'::GFP , smyc'::mCherry reporter strain was constructed by cloning of rv2390c'::GFP into the replicating plasmid pCherry3 [25] , and transformation into CDC1551 or Erdman . To construct the Erdman ( hspX'::GFP , smyc'::mCherry ) reporter , a 558 bp region upstream of the hspX start codon was PCR amplified and cloned upstream of GFPmut2 in the pSE100 vector . The hspX'::GFP fusion was then subcloned into the pCherry3 plasmid and transformed into Erdman . Selection in all cases was carried out on 7H10 agar containing 50 µg/ml hygromycin . For broth assays , Mtb was grown in standing vented T-25 flasks , in 10 ml 7H9 medium buffered at specified pH , with addition of NaCl or other compounds as stated for each experiment . pH 7 . 0 medium was buffered with 100 mM MOPS , while pH 5 . 5–6 . 5 media were buffered with 100 mM MES . Appropriate antibiotics were added as necessary . NO assays were done in stirred , aerated , cultures and used the NO donor DETA NONOate ( Cayman Chemicals ) at 100 µM . Hypoxia experiments were conducted in 50 ml culture volumes in 125 ml duo-capped Erlenmeyer flasks ( BD Biosciences ) with stirring using a magnetic stir bar . Cultures were placed in a hypoxia chamber with adjustable O2 and CO2 controls ( BioSpherix ) , set on a magnetic stirrer within a 37°C incubator . CO2 was set at 7% , while O2 levels were adjusted as required . For all in vitro assays , samples were fixed with 4% paraformaldehyde and GFP fluorescence read on a BD FACS LSR II . FACS data were analyzed using FloJo ( Tree Star , Inc ) . Infection of murine bone marrow-derived MØs with Mtb were carried out as previously described [24] . Where needed , MØs were activated by treatment with 100 U/ml IFNγ and 10 ng/ml LPS . For infection with CDC1551 ( rv2390c'::GFP , smyc'::mCherry ) pre-induced with Cl− , the bacteria were grown in the presence of 250 mM NaCl for 6 days prior to MØ infection . Bacteria were at log-phase when MØs were infected . Samples were fixed , imaged and analyzed by confocal microscopy as described below . All animal experiments were carried out in accordance with NIH guidelines , and with the approval of the Institutional Animal Care and Use Committee of Cornell University . C57BL/6J WT mice and their isogenic IFNγ−/− derivatives ( Jackson Laboratories ) were infected with 103 CFU of Erdman ( rv2390c'::GFP , smyc'::mCherry ) or Erdman ( hspX'::GFP , smyc'::mCherry ) via an intranasal delivery method . This was accomplished by lightly anesthetizing the mice with isoflurane and administering the bacterial inoculum in a 25 µl volume onto both nares . At sacrifice , the lungs were removed and fixed in 4% paraformaldehyde overnight . For MØ infections , Mtb infected cells on glass coverslips were fixed overnight at indicated time points with 4% paraformaldehyde . Nuclei were visualized with DAPI ( Invitrogen ) . For mouse infections , whole lung lobes were fixed overnight with 4% paraformaldehyde , and stored in PBS prior to processing . Details of sample processing and antibodies used for confocal microscopy imaging are described in the Supplementary Materials and Methods . Samples were imaged with a Leica SP5 confocal microscope , and z-stacks reconstructed into 3D using Volocity software . For quantification of reporter Mtb signal , the fluorescence voxel volume of each bacterium was measured via the mCherry channel , with the corresponding sum of the GFP signal for that bacterium simultaneously measured . Settings for the GFP channel were maintained during imaging of samples within experimental sets to allow comparison of values . At least 100 bacteria were quantified for each condition . Statistical differences between data sets were determined by a non-parametric Mann-Whitney test . | Mycobacterium tuberculosis ( Mtb ) is the causative agent of tuberculosis , a disease that remains a major global health problem . To ensure its long-term survival in the host , Mtb must be able to sense and respond to changes in its immediate environment , such as the pH differences that occur in the phagosome in which it lives . Knowledge of the external signals that Mtb recognizes during infection is critical for understanding the impact of the microenvironment on Mtb pathogenesis and persistence , and how Mtb interacts with its host cell . We show here that [Cl−] correlates inversely with pH as the phagosome matures , and identify [Cl−] as a novel cue that Mtb responds to , in synergism with pH . By constructing a Mtb strain that fluorescently reports on changes in [Cl−] and pH , we find using a mouse model of infection that environmental alterations in Mtb's phagosomal home are mediated at the local level by activities of the host immune system . Our study demonstrates how a pathogen can exploit linked environmental cues during infection , and shows the value of reporter bacterial strains for Mtb-host whole animal studies . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"immunopathology",
"molecular",
"cell",
"biology",
"microbial",
"physiology",
"medical",
"microbiology",
"immunology",
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] | 2013 | Mycobacterium tuberculosis Responds to Chloride and pH as Synergistic Cues to the Immune Status of its Host Cell |
The avoidance of starvation is critical for the survival of most organisms , thus animals change behavior based on past nutritional conditions . Insulin signaling is important for nutritional state-dependent behavioral plasticity , yet the underlying regulatory mechanism at the cellular level remains unclear . Previous studies showed that insulin-like signaling is required for taste avoidance learning , in which the nematode Caenorhabditis elegans avoids salt concentrations encountered under starvation conditions . DAF-2c , a splice isoform of the DAF-2 insulin receptor , functions in the axon of the ASER sensory neuron , which senses changes in salt concentrations . In addition , mutants of a major downstream factor of DAF-2 , the forkhead transcription factor O ( FOXO ) homolog DAF-16 , show defects in taste avoidance learning . Interestingly , the defect of the daf-2 mutant is not suppressed by daf-16 mutations in the learning , unlike those in other phenomena , such as longevity and development . Here we show that multiple DAF-16 isoforms function in ASER . By epistasis analysis using a DAF-2c isoform-specific mutant and an activated form of DAF-16 , we found that DAF-16 acts in the nucleus in parallel with the DAF-2c-dependent pathway in the axon , indicating that insulin-like signaling acts both in the cell body and axon of a single neuron , ASER . Starvation conditioning induces nuclear translocation of DAF-16 in ASER and degradation of DAF-16 before starvation conditioning causes defects in taste avoidance learning . Forced nuclear localization of DAF-16 in ASER biased chemotaxis towards lower salt concentrtions and this effect required the Gq/PKC pathway and neuropeptide processing enzymes . These data imply that DAF-16/FOXO transmits starvation signals and modulates neuropeptide transmission in the learning .
A common strategy for animals to respond to starvation conditions is to change behaviors based on the nutritional state . Insulin/IGF signaling plays a key role in physiological and behavioral responses to nutritional stimuli . For instance , insulin signaling is involved in nutritional condition-dependent regulations of metabolism , development and longevity [1 , 2] . Insulin signaling acting in the nervous system controls feeding behavior and its dysfunction leads to neurological disorders [3 , 4] . Neural insulin signaling also affects valuation of nutritional stimuli in the human brain [5] . However , the functions of insulin signaling in the central nervous system , especially at the single-neuron level , have not been well clarified . Caenorhabditis elegans is a commonly used model to investigate behavioral plasticity because of its ease of genetic manipulation and analyses at the single cell level in vivo . Most components of insulin-like signaling in C . elegans are conserved across species . C . elegans has only one insulin receptor homolog , DAF-2 , despite having 40 insulin-like peptides [6 , 7] . Downstream of the DAF-2 receptor , a signaling pathway composed of the phosphatidylinositol 3-kinase homolog AGE-1 [8] , the 3-phosphoinositide-dependent kinase-1 homolog PDK1 and the Akt/PKB homologs AKT-1/2 [9 , 10] negatively regulates the forkhead transcription factor O ( FOXO ) homolog DAF-16 [11–14] . DAF-16 is expressed in various tissues and controls several phenomena , such as stress response , metabolism , dauer formation , and lifespan [15–17] . Recently , tissue-specific transcriptome analyses showed that the regulation of gene expression by DAF-16 differs between the nervous system and other tissues [18 , 19] . DAF-16 function in AWB chemosensory neurons contributes to pheromone-dependent behavior via transcriptional control of the glna-3 gene , which encodes a glutaminase homolog [20] . Moreover , DAF-2/DAF-16 signaling is required for behavioral plasticity . DAF-16 regulates starvation-dependent increase in pheromone repulsion in the ADL chemosensory neurons , as well as starvation-dependent thermotaxis plasticity in the thermosensory circuit [21–23] . DAF-16 enters the nucleus dependent on nutritional conditions and DAF-2 signaling [11] . However , it is unclear whether DAF-2/DAF-16 signaling mediates transmission of nutritional states required for the formation of memory in the regulation of the feeding status-dependent behavioral plasticity . C . elegans worms migrate toward sodium chloride concentrations experienced during feeding , but avoid such concentrations experienced during fasting , in a phenomenon called taste avoidance learning [24 , 25] . We reported that insulin-like signaling is required for taste avoidance learning and acts in the salt-sensing neuron , ASER [25 , 26] . Isoform-specific axonal transport of the DAF-2 isoform DAF-2c is required for taste avoidance learning . Starvation increases translocation of DAF-2c from the cell body to the axon , and this translocation is carried by kinesin-1-dependent axonal transport via the cargo adapter CASY-1 , a homolog of mammalian Calsyntenin . DAF-2c/PI3K signaling decreases diacylglycerol ( DAG ) dynamics in the axon [27] . As high or low DAG levels promote animals’ migration toward high or low-salt concentration , respectively , axonal insulin-like signaling has been proposed to control the direction of salt chemotaxis from attraction to avoidance , at least partly , through the regulation of DAG dynamics . We demonstrated that the loss of DAF-16 causes impaired taste avoidance learning , similar to that of insulin-signaling mutants [26 , 28] . Although phenotypes of insulin-signaling mutants were suppressed by loss-of-function ( lf ) mutation of daf-16 in behavioral plasticity in odor chemotaxis and thermotaxis [22 , 23] , defects of insulin-signaling mutants in taste avoidance learning were not suppressed by daf-16 ( lf ) mutations , implying that the role of DAF-16 may be different among different paradigms of behavioral plasticity . Here , we further investigated the function of DAF-16 and found that multiple DAF-16 isoforms function in the ASER sensory neuron independently of axonal insulin-like signaling in taste avoidance learning . Using the auxin-inducible degradation system , we showed that DAF-16 is required around the time of taste avoidance learning , rather than during development . DAF-16 localized to the nucleus of ASER under starvation conditions . A mutant form of DAF-16 , in which the putative Akt phosphorylation sites were mutated , strongly localized to the nucleus of ASER even in the presence of food . The forced nuclear localization of DAF-16 biased chemotaxis toward lower salt concentrations even after high-salt conditioning with food , and this effect was also observed in the mutant background of axonal insulin-like signaling . Thus , these findings suggest that different insulin-like signaling pathways work in the nucleus and the axon of ASER to control salt chemotaxis . Furthermore , we showed that the DAF-16-dependent salt avoidance requires the Gq/PKC signaling and neuropeptide-processing pathways in the nervous system .
Wild-type N2 animals avoid concentrations of sodium chloride ( hereafter referred to as salt ) encountered under starvation conditions , which is known as taste avoidance leaning [25] . After conditioning on agar plates that contain high or low concentrations of salt in the absence of food , adult animals migrate to areas of low or high salt concentrations , respectively , on a test plate with a salt gradient ( Fig 1A , 1B , 1E and 1F ) . We first examined the requirement of DAF-16/FOXO in taste avoidance learning using three different daf-16 ( lf ) mutants . daf-16 ( mgDf47 ) and daf-16 ( mgDf50 ) mutants showed weaker migration to low salt than wild type after starvation conditioning with high salt . The daf-16 ( m26 ) mutant exhibited a tendency of decreased low-salt migration after starvation conditioning , though the effect was not statistically significant ( Fig 1E ) . Both daf-16 ( mgDf47 ) and daf-16 ( mgDf50 ) mutants harbor large deletions in the forkhead domains of all DAF-16 isoforms ( WormBase ) , whereas the b isoform is spared in the daf-16 ( m26 ) mutant , suggesting that the weaker defect in daf-16 ( m26 ) mutant was likely attributed to the intact b isoform . After starvation conditioning with low salt , each of the daf-16 mutants showed significant defects in high-salt migration , as compared to wild-type animals . These results indicate that DAF-16 is required for avoidance of high or low salt concentrations after starvation conditioning . On the other hand , the daf-16 mutants showed no strong defect in associative learning between food and salt concentration: as the attraction to salt concentrations encountered during feeding was similar to that of the wild type ( Fig 1C , 1D , 1G and 1H ) , although the daf-16 mutation occasionally caused weak but significant reductions in high salt migration ( S3A Fig ) [24] . These results suggest that DAF-16 may also play a minor role in attractive behavior to high salt concentrations after feeding conditioning , but is particularly important for taste avoidance learning . They also suggest that a defect in taste avoidance learning does not appear to be due to a general defect in salt sensation or locomotion during chemotaxis . The mgDf50 allele was mainly used for the following experiments because it had the largest deletion among the daf-16 alleles ( Fig 2A ) . Multiple isoforms are generated from the daf-16-genomic region by alternative promoters and contain different N-terminal regions ( Fig 2A ) . The daf-16a isoform , which has a sequence most similar to that of the mammalian FOXO3 among other isoforms , is involved in development , longevity , and stress responses [29] . The daf-16b isoform , which lacks a part of the forkhead domain , has not been reported to have a strong effect on these biological phenomena . The function of the daf-16f isoform is reported to affect longevity . We generated daf-16-rescue strains , each of which expressed a single daf-16 isoform , as well as a fluorescent protein ( Venus ) , under the endogenous promoter . As reported previously [12 , 29] , the promoters of the a , b , and f isoforms drove expression in the whole body except for the pharynx , a few tissues , and almost all tissues except the gonad , respectively ( S1 Fig ) . We also confirmed Venus expression in head neurons including ASER , by these promoters ( S1H–S1J Fig ) . Next , we examined the learning ability of the daf-16-rescue strains . The defect of low-salt migration after starvation conditioning was canceled by expression of DAF-16a or DAF-16b , which was driven by each endogenous promoter in the daf-16 mutant ( Fig 2B ) , while no substantial effect was observed for DAF-16f ( Fig 2B , S2A Fig ) . Among the DAF-16 isoforms , only DAF-16a expression significantly rescued high-salt migration after starvation conditioning in the daf-16 mutant ( S2B Fig ) . Because the rescue effect of DAF-16a was strongest , we performed tissue-specific rescue experiments of the daf-16 mutant using daf-16a cDNA . Neuronal expression of DAF-16a fully rescued the learning defect of daf-16 mutants ( Fig 2C , S2C Fig ) . DAF-16a expression in ASER , but not in ASEL , was sufficient for full rescue of the low-salt migration defect ( Fig 2B , S2D Fig ) and partial rescue of the high-salt migration defect ( S2B Fig ) , suggesting that DAF-16 acts in ASER for low-salt migration and in multiple neurons including ASER for high-salt migration after starvation conditioning . ASER expression of the b and f isoforms also rescued the learning defect of the daf-16 mutant similar to that of the a isoform ( Fig 2B , S2B Fig ) . These results suggest that all daf-16 isoforms can function in ASER in taste avoidance learning . DAF-16a expression in multiple chemosensory neurons other than ASER or muscle cells was also sufficient for weak rescue of the learning defect ( Fig 2C , S2C Fig ) , suggesting that DAF-16a can regulate taste avoidance learning also in multiple cell types other than ASER . We next examined the isoform-specific daf-16 mutants , which were previously generated and were used for the study of longevity [30] . The daf-16 ( tm5030 ) , daf-16 ( tm5031 ) , and daf-16 ( tm6659 ) mutants , which harbor a deletion in the a , b , and h/f/d isoform-specific exon , respectively ( Fig 2A ) , had no strong defects in low-salt migration , while the daf-16 ( tm5030 ) mutant had a mild defect in high-salt migration after starvation conditioning ( Fig 2D and 2E ) . These results are consistent with the conclusion that taste avoidance learning is regulated by multiple DAF-16 isoforms with a strong contribution of DAF-16a . DAF-16 is reportedly required for the development of AIY interneurons [31] , whereas it functions in associative learning during the adult stage [18] . We investigated when DAF-16 functions in the regulation of taste avoidance learning , namely either during development or during the learning paradigm . We used the auxin-inducible degradation system , which is suitable for spatiotemporal analyses in several organisms including C . elegans [32 , 33] . In this system , two proteins are expressed in the same cells: one is a target protein tagged with the degron sequence that induces the ubiquitin-proteasomal degradation of the target protein and another is TIR1 , a plant-specific F-box protein required for the activation of an E3 ubiquitin ligase . To control DAF-16 expression in a spatiotemporal manner , both degron-tagged DAF-16a and TIR1 were expressed in the ASER neuron of the daf-16 mutant ( Fig 2F ) . The transgenic animals were treated with 1 mM auxin ( or 0 . 25% ethanol as a control ) for two hours before conditioning and during conditioning with high salt . The low-salt migration after starvation conditioning was recovered by expression of degron-tagged DAF-16 in the daf-16 mutant , and this recovery of low-salt migration was inhibited by auxin administration ( Fig 2G ) . On the other hand , only TIR1 expression and/or auxin administration had no significant effect on chemotaxis of the wild-type and daf-16 mutant animals . These results suggest that DAF-16 contributes to taste avoidance learning around the time of taste avoidance learning . We previously reported that insulin-like signaling acts in the axon of the ASER neuron in taste avoidance learning: During starvation conditioning , a splice isoform of the insulin receptor homolog , DAF-2c , is transported from the cell body to the axon of ASER , where it acts for taste avoidance learning . We looked into a possible interaction of DAF-16 with DAF-2c and CASY-1 , the calsyntenin homolog required for translocation of DAF-2c to the axon . We generated the daf-2c-isoform specific mutant daf-2c ( pe2722 ) , which harbors a frame-shift deletion in the c isoform-specific exon , and confirmed a substantial defect in taste avoidance learning , but not salt chemotaxis after feeding conditioning similar to a deletion mutant of daf-16 or casy-1 ( Fig 3A and 3B , S3A and S3B Fig ) . The daf-16 ( mgDf50 ) mutation caused additive effects in the daf-2c and casy-1 mutants . Furthermore , there was no substantial difference in the localization of DAF-2c::Venus in ASER between the wild-type and daf-16 mutant animals; DAF-2c::Venus was observed throughout the whole ASER neuron , including the axon , both in the wild-type and the daf-16 mutant ( S3C Fig ) . These data suggest that DAF-16 functions in parallel with the CASY-1/DAF-2c pathway . DAF-16 translocates into the nucleus during starvation where it controls the transcription of stress response genes [11] . daf-16 mutants showed defects in learned behavior after starvation conditioning but was almost normal in salt chemotaxis after feeding conditioning , implying a role of DAF-16 in the transmission of the starvation signals during conditioning . Next , we investigated whether nuclear localization of DAF-16 was increased in the ASER neuron during starvation conditioning . DAF-16a::GFP was localized to the cytosol of ASER in well-fed animals , whereas it was translocated into the nucleus after starvation for an hour ( Fig 3C ) . The DAF-16 nuclear translocation was not substantially different depending on salt concentrations during starvation ( Fig 3C , S4A–S4C Fig ) , suggesting that the strong translocation occurs mainly due to starvation . The insulin receptor , DAF-2 , a major upstream factor of DAF-16 , negatively regulates nuclear localization of DAF-16 in several cell types [11 , 13] . In the background of the daf-2 ( e1370 ) reduction-of-function mutant , nuclear translocation of DAF-16a::GFP was significantly increased in the ASER neuron even under well-fed condition , and this phenotype was rescued by expression of DAF-2a in ASER ( Fig 3D–3F ) . These results suggest that the DAF-2 insulin receptor acts upstream of DAF-16 cell-autonomously in ASER . On the other hand , the deletion mutation of daf-2c , daf-2c ( pe2722 ) , had no significant effect on the localization of DAF-16a::GFP in ASER under well-fed conditions , suggesting that DAF-2 isoforms other than the c isoform likely repress DAF-16 nuclear translocation in ASER in the presence of food . We previously reported that the PTEN phosphatase homolog DAF-18 , a negative regulator of insulin-like signaling , functions in ASER in taste avoidance learning . daf-18 mutants showed reduced high-salt migration irrespectively of the presence or absence of food during conditioning likely due to hyperactivation of the PI3K pathway [26] . The defect in high-salt migration of the daf-18 ( e1375rf ) mutant was suppressed by the daf-2c ( pe2722 ) mutation similar to the mutations of the PI3K homolog age-1 ( Fig 3H , S4D and S4E Fig; [26] ) . Conversely , the daf-18 ( e1375rf ) mutation suppressed the taste avoidance learning defect of daf-2c ( pe2722 ) ( Fig 3G and 3H ) , suggesting that DAF-18 antagonizes the DAF-2c/PI3K pathway in taste avoidance learning . We next investigated the effect of a daf-16 mutation on taste avoidance learning of daf-18 ( ok480rf ) and daf-18 ( mg198null ) mutants . If DAF-16 is negatively regulated by insulin-like signaling in taste avoidance learning , it is expected that the effect of the daf-16 mutation on the learning is reduced in the daf-18 mutants compared to the wild type , because in the daf-18 mutants the activity of DAF-16 will be already reduced in the daf-16 ( + ) background . As we expected , the daf-16 mutation had no significant effect on taste avoidance learning in the background of the daf-18 mutants ( Fig 3I and 3J ) . On the other hand , the daf-18 mutations significantly altered salt chemotaxis of the daf-16 mutant , consistent with the notion that DAF-18 modulates both the axonal DAF-2c signaling and the cell boy DAF-2/DAF-16 pathway in parallel . These data support the view that insulin-like signaling mediated by DAF-2 isoforms other than DAF-2c negatively regulates DAF-16 functions in taste avoidance learning . It has been reported that DAF-16 activity is negatively regulated by its phosphorylation via AKT-1 and AKT-2 kinases , and a mutant form of DAF-16 , in which serine/threonine residues in the putative AKT-1-phosphorylation sites were substituted by alanine , was strongly localized to the nucleus [13 , 29] . We expressed the mutant form of DAF-16a ( hereafter , DAF-16a ( AM ) ) in the ASER neuron , and confirmed that DAF-16a ( AM ) ::GFP was strongly localized to the nucleus in ASER even after well-fed conditions ( Fig 4A , S5A and S5B Fig ) . We next examined the effect of DAF-16a ( AM ) expression in ASER or ASEL on behavior . The wild-type or daf-16 mutant animals expressing DAF-16a ( AM ) in ASER showed significant decreases in high-salt migration after low-salt/starvation and/or high-salt/feeding conditioning ( Fig 4B , 4C and 4D ) . Meanwhile , the effect of DAF-16a ( AM ) on salt chemotaxis was not observed when expressed in ASEL ( Fig 4E ) . Combined with the finding that ASER expression of DAF-16 or DAF-16a ( AM ) was sufficient for rescue of the learning defect in the daf-16 mutant ( Fig 2B , Fig 4F ) , these results suggest that the action of DAF-16 in the nucleus of ASER drives low-salt migration after starvation conditioning with high salt . On the other hand , no substantial rescue was observed by ASER expression of DAF-16a ( AM ) in the high-salt migration defect of the daf-16 mutant ( Fig 4F ) , suggesting that an appropriate level of DAF-16 activity is required for high-salt migration after starvation conditioning . The chemotaxis defect toward high salt observed by the expression of DAF-16a ( AM ) was not due to locomotory defect at high salt ( Fig 4G ) . The DAF-16a ( AM ) expression increased low-salt migration also in the casy-1 and daf-2c mutants , similar to that in the wild type ( Fig 4H and 4I ) , suggesting that DAF-16 nuclear localization biases chemotaxis toward lower salt concentrations independently of axonal insulin-like signaling in ASER . We sought to uncover the molecular mechanisms underlying the DAF-16-dependent salt chemotaxis plasticity . We first examined the interneurons required for DAF-16-dependent low-salt migration . DAF-16a ( AM ) was expressed in animals in which the postsynaptic interneurons of ASER , either AIA , AIB , or AIY , were ablated by mouse caspase expression [34] , and examined the effect on salt chemotaxis of those animals . The effect of DAF-16a ( AM ) expression was decreased in AIA- or AIY-ablated animals , as there was no significant change in high-salt migration after feeding conditioning by DAF-16a ( AM ) expression ( Fig 5A ) . These results imply that ASER transmits DAF-16-dependent signals to AIA and AIY interneurons to drive migration to the low-salt area . We next examined the possible role of DAF-16 in neurotransmission . Diacylglycerol ( DAG ) signaling regulates neurotransmission through the nPKC-ε/η ortholog , PKC-1 ( S5C Fig ) [35 , 36] , and plays a key role in salt chemotaxis plasticity in ASER [37] . goa-1 , encoding a Goα subunit , and egl-30 , encoding a Gqα subunit , have been proposed to regulate salt chemotaxis mainly through DAG signaling in ASER ( S5C Fig ) . Both loss-of-function ( lf ) of goa-1 and gain-of-function ( gf ) of egl-30 increase high-salt migration , whereas a lf mutation of pkc-1 increases low-salt migration [25 , 26 , 37] . Increased low-salt migration by DAF-16a ( AM ) was suppressed by goa-1 ( n1134lf ) or egl-30 ( pe914gf ) mutations ( S5D Fig ) , suggesting that the DAG/PKC-1 pathway acts downstream of or in parallel with DAF-16a ( AM ) . The effect of DAF-16a ( AM ) was not observed in the background of a null mutant of pkc-1 , pkc-1 ( nj3lf ) ( Fig 5B and 5C ) . These data suggest that the DAG/PKC-1 pathway mediates DAF-16-dependent low-salt migration . The DAG/PKC-1-dependent pathway is involved in neurotransmission via neuropeptides [36] . In C . elegans , precursor proteins of neuropeptides are cleaved by proprotein convertases which are encoded by four genes , namely PC1/3 homologs , kpc-1 , bli-4 , and aex-5 , and the PC2 homolog egl-3 . The C-terminal extensions of lysine and/or arginine residues of these cleaved peptides are subsequently removed by carboxypeptidases E , such as EGL-21 . Mutants of these enzymes actually cause changes in the production of many neuropeptides [38 , 39] . As mutants of aex-5 and egl-3 showed significant defects in salt chemotaxis plasticity ( S6A–S6D Fig ) , we examined the effect of DAF-16a ( AM ) expression in the aex-5 and egl-3 mutants and found that ASER expression of DAF-16a ( AM ) significantly reduced high-salt migration of the aex-5 mutant , but not the egl-3 mutant ( Fig 5D ) , suggesting that decreased high-salt migration caused by DAF-16a ( AM ) expression requires the EGL-3 neuropeptide processing enzyme . We confirmed that egl-3 expression in the whole nervous system was sufficient for the rescue of the effect of DAF-16a ( AM ) expression and the salt chemotaxis defect in the egl-3 mutant ( Fig 5E , S6E and S6F Fig ) . We note that DAF-16a ( AM ) expression in ASER in some mutants caused salt chemotaxis defects in a manner different from that in the wild type: it significantly promoted migration to higher salt concentrations in egl-3 , egl-21 and goa-1 mutants and AIB-ablation animals ( S7 Fig; see also Discussion ) . Most neuropeptides processed by EGL-3 are thought to be further processed by EGL-21 [39] . Indeed , DAF-16a ( AM ) expression had no significant effect also in an egl-21 mutant background ( Fig 5D ) . Hence , we next examined the expression levels of egl-21 , egl-3 , and some other genes related to neurotransmission by qRT-PCR . No significant change was observed in expression of egl-3 , pkc-1 , unc-13 , and eat-4 , which encodes the vesicular glutamate transporter , by DAF-16a ( AM ) expression in ASER . On the other hand , the DAF-16a ( AM ) expression weakly , but significantly , reduced expression of egl-21 and dgk-1 , the latter of which encodes a diacylglycerol kinase ( S8B Fig ) . Moreover , overexpression of egl-21 in ASER imposed migration bias to higher salt concentrations after high-salt/starvation conditioning , whereas it had no significant effect on chemotaxis after low-salt/starvation conditioning ( Fig 5F and 5G ) , a phenotype opposite to that caused by DAF-16a ( AM ) expression in ASER . These results are consistent with the notion that DAF-16 promotes low-salt migration by changing production of neuropeptides via the EGL-21 neuropeptide processing enzyme in ASER .
Taste avoidance learning is a form of associative learning between starvation conditions and salt concentrations . We previously reported that the Ras/MAPK signaling pathway mediates transmission of food signaling to the ASER neuron and its downregulation increases axonal transport of DAF-2c , by which salt avoidance is promoted [25] . In this study , we show that DAF-16 acts in parallel to the DAF-2c pathway in taste avoidance learning . DAF-16 was translocated into the nucleus of ASER under fasting conditions in a DAF-2-dependent manner , and this regulation was mediated by DAF-2 ( exon 11 . 5- ) isoforms ( see below ) . Constitutively nuclear-translocated DAF-16 , DAF-16a ( AM ) , increased salt avoidance even after feeding conditioning . Furthermore , a cell type- and timing-dependent expression using the auxin-inducible degradation system revealed that DAF-16 acts in ASER around the time of taste avoidance learning . These observations suggest that DAF-2 ( exon 11 . 5- ) /DAF-16 signaling likely transmits starvation signals to the ASER nucleus in parallel to the axonal DAF-2c pathway in taste avoidance learning . This dual function of insulin-like signaling in the cell body and the axon may ensure dynamic changes of behavioral responses after starvation conditioning ( Fig 6 ) . daf-16 mutations caused strong defects in learned behaviors after conditioning under starvation ( Fig 1 , S3A Fig ) , but only minor effects on those after feeding conditions , suggesting that the defects of the daf-16 mutants are not simply due to abnormalities in salt sensitivity or locomotory activity . The effect of DAF-16a ( AM ) expression was significantly decreased in animals with genetically ablated AIA and AIY interneurons . These findings imply that DAF-16 may regulate salt avoidance by controlling neurotransmission between ASER and the innervated interneurons , such as AIA and AIY . It was reported that the morphology of AIY is regulated by DAF-16 , especially a DAF-16b isoform , during development [31] . Therefore , loss of the DAF-16b isoform might cause a change in neurotransmission from ASER to AIY , possibly leading to defective learning behavior . However , the b isoform-specific daf-16 mutant had no significant defect in the learning ( Fig 2D and 2E ) . In addition , not only DAF-16b but also DAF-16a and DAF-16f rescue the defect in the learning ( Fig 2B ) , while expression of DAF-16a and DAF-16f did not rescue abnormal morphology of AIY [31] . Therefore , it seems that the learning defects of the daf-16 mutants are not caused only by abnormal morphology of AIY . The AIY interneurons mediate an ASER-dependent curved locomotion toward low-salt concentrations [34] . The AIA and AIY interneurons are activated upon an increase in salt concentration and promote forward locomotion [40] . It will be interesting to investigate how DAF-16 controls the activities of interneurons upon ASER activation and the regulation of taste avoidance learning . Nuclear localization of DAF-16 was increased in ASER in daf-2 ( e1370 ) , a reduction-of-function mutation in the exon common to all isoforms , but not in daf-2 ( pe2722 ) , a frame-shift-deletion mutation in exon 11 . 5 which is contained only in the daf-2c isoform , suggesting that DAF-16 is negatively regulated by exon 11 . 5-skipped DAF-2 , DAF-2 ( exon 11 . 5- ) , which preferentially localizes to the cell body , as compared to DAF-2c ( exon 11 . 5+ ) ( Fig 3E ) [28] . The observation that DAF-16 nuclear localization is dependent on the putative Akt phosphorylation sites ( Fig 4A ) suggests that the cell body isoform , DAF-2 ( exon 11 . 5- ) , regulates DAF-16 localization via Akt in ASER , similar to that in other cell types [11 , 13 , 29] . Also , in mammalian neurons , the receptors for insulin-like peptides , such as insulin , IGF-I , and IGF-II , are localized to both the cell body and axon [41–43] . However , the relationship between multiple insulin-like signaling in the cell body and axon within a single neuron remains unclear . In C . elegans , impairment of both nuclear and axonal insulin-like signaling caused salt attraction after starvation conditioning as if the mutant worms were conditioned in the presence of food ( Fig 3A and 3B ) . Therefore , the cooperative function of insulin-like signaling acting in the distinct subcellular regions is essential for generation of the behavioral mode of salt avoidance caused by starvation conditioning , although the precise timing of action of these signaling pathways are currently unknown . In axonal DAF-2 signaling , the insulin-like peptide INS-1 , which preferentially localized to the axonal processes , regulates taste avoidance learning [25 , 26] . On the other hand , for the DAF-2 ( exon 11 . 5- ) /DAF-16 signaling pathway in the cell body , an insulin-like peptide that regulates taste avoidance learning remains unclear . Because decision of dauer entry and longevity are modulated by the presence or absence of food , the activity of DAF-2 ( exon 11 . 5- ) /DAF-16 signaling in ASER might be regulated by starvation responses commonly used for the regulation of development and longevity . In mammals , insulin released from the peripheral tissues enters the brain and acts on the insulin receptors expressed in the hypothalamic arcuate nucleus and regulates food intake behavior through control of FOXO1-dependent expression of neuropeptides , such as agouti-related protein ( AgRP ) and pro-opiomelanocortin ( POMC ) [44] . In this study , DAF-16 was found to regulate salt chemotaxis through EGL-3- and EGL-21-dependent neuropeptide processing in the nervous system . Taste avoidance learning of C . elegans is believed to increase the chance of obtaining food by avoiding areas that are likely devoid of food . Therefore , the insulin/FOXO pathway-dependent modulation of neuropeptide signaling might be an evolutionarily conserved mechanism to control feeding-related behavior . Although DAF-16 mainly localizes to the cytoplasm in the presence of food , the daf-16 ( mgDf50null ) mutant exhibited a mild defect in high-salt migration after feeding conditioning ( S3A Fig , S4 Fig ) . Thus , a weak activity of DAF-16 in the nucleus is likely required for high-salt attraction after feeding conditioning . On the other hand , starvation strongly promotes nuclear localization of DAF-16 independent of salt concentrations during fasting ( Fig 3C , S4 Fig ) . The daf-16 ( lf ) mutants showed strong defects in migration to both low- and high-salt concentrations after starvation conditioning ( Fig 1 ) . Thus , DAF-16 is required for avoidance of the salt concentrations associated with starvation towards both lower and higher concentrations . We speculate that DAF-16 controls transcription of genes required for the behavioral switch caused by starvation conditioning and those genes promote both low- and high-salt migration dependent on the cellular environments after high- and low-salt conditioning , respectively . The forced nuclear localization by DAF-16a ( AM ) expression promotes low-salt migration irrespectively of the presence or absence of food during conditioning ( Fig 4B and 4C ) . The forced nuclear-localized DAF-16 may affect transcription of only a subset of genes that regulate salt chemotaxis , such as egl-21 and dgk-1 , thereby causing unbalanced promotion of lower salt migration or repression of higher salt migration . Interestingly , in worms with defective neuropeptide processing , DAG metabolism , or AIB function , the forced DAF-16 nuclear localization in ASER significantly promoted migration to higher salt ( S7 Fig ) . These observations support the notion that DAF-16 can promote salt chemotaxis in different directions dependent on the cellular environments of the neural circuits . Because DAF-16a can also function in muscle cells and chemosensory neurons other than ASER in taste avoidance learning , in addition to the cell-autonomous functions in ASER , DAF-16a might regulate taste avoidance learning from neuronal and muscle cells to the taste neural circuit in a cell-non-autonomous manner . Detailed mechanisms underlying several modes of actions of DAF-16 in taste avoidance learning will need to be further investigated . A previous study demonstrated that a mutation in carboxypeptidase E was associated with the development of neurodegenerative diseases [45] , suggesting that processing of neuropeptides plays a key role in neural function . FoxO1 negatively regulates the expression of carboxypeptidase E in POMC-expressing neurons in mice , which leads to reduced food intake through processing of POMC [46] . Our qRT-PCR and behavioral analyses suggest that DAF-16/FOXO also negatively regulates the expression levels of EGL-21/carboxypeptidase E ( S8B Fig ) , thereby promoting avoidance of salt concentrations associated with starvation . EGL-21 overexpression in ASER caused the taste avoidance learning defect ( Fig 5F ) in the same direction as the egl-21 ( lf ) mutant ( S6A Fig ) , consistent with the notion that an adequate level of EGL-21 in ASER is required for taste avoidance learning . FOXO-dependent neuropeptide processing may underlie feeding-related behavior and behavioral plasticity across species . As the recovery of neuropeptide production by egl-3 expression in the whole nervous system was sufficient for rescue of the DAF-16-induced salt avoidance deficiency in the EGL-3/PC2 mutant , DAF-16 appears to regulate taste avoidance learning via neuropeptide signaling acting in unknown neuron ( s ) in the taste neural circuit ( Fig 6 ) . In addition to egl-21 , expression of a diacylglycerol kinase dgk-1 was significantly reduced in the DAF-16a ( AM ) -expressing animals ( S8F Fig ) . Mutations of dgk-1 , which are predicted to increase DAG levels , were reported to cause defects in associative learning between odor and starvation and suppress decreased odor chemotaxis in daf-18 mutants similar to those of the insulin-like pathway [47 , 48] . The axonal DAF-2 signaling regulates DAG dynamics in response to salt concentration changes [27] . In this study , we show that the DAF-16-dependent low-salt migration requires normal DAG/PKC-1 signaling ( Fig 5B and 5C , S5D Fig ) . These findings imply that DAG/PKC-1 signaling may play a key role downstream of both the soma and axonal DAF-2 pathways . Further comprehensive analysis will be required to understand fully the DAF-16-dependent transcriptional regulation acting during taste avoidance learning . Based on our study , we propose a speculative model for the regulatory functions of DAF-16 in taste avoidance learning ( Fig 6 ) . The insulin-FOXO axis is likely to be important for cognitive functions in mammals , including humans . Insulin receptors are expressed in the cerebral cortex and the hippocampus , which play critical roles in learning and memory [49] . FOXO1 , FOXO3 , and FOXO6 are expressed in the murine brain [50] . Cyclin-dependent kinase-5 ( Cdk5 ) increases amyloid beta levels through FOXO3 activity and induces the pathogenesis of Alzheimer’s disease [51] . FOXO6 expressed in the hippocampus is required for memory consolidation and synaptic function [52] . We believe that our findings will promote the discovery of novel functions of insulin/FOXO signaling in the complex nervous system .
The list of C . elegans strains is shown in S1 Table . The Bristol N2 strain was used as wild type . The C . elegans strains were cultured as described [53] . Animals were cultivated on Nematode Growth Media ( NGM ) plates under 15°C , 20°C , or 25°C . An Escherichia coli strain , NA22 , was used as a bacterial diet . Double mutants were generated by genetic crossing , and their genotypes were checked by PCR . For the daf-16 rescue experiments , the cDNA and a promoter region of each daf-16 isoform were amplified with KOD-Plus-Neo DNA polymerase ( Toyobo , Japan ) . A promoter region of each daf-16 isoform was amplified with KOD-Plus Neo DNA polymerase using wild-type ( N2 ) total cDNA and genomic DNA as a template , respectively . Primers are shown in S2 Table . The 6 . 1 kbp , 4 . 0 kbp , and 3 . 4 kbp 5’ upstream regions were used for the promoter regions of daf-16a , daf-16b , and daf-16f , respectively , as referred in Kwon et al . , ( 2010 ) . Most of the daf-16 expression plasmids were generated by the Gateway system ( Thermo Fisher Scientific ) . The PCR-amplified daf-16 cDNAs were inserted into the NheI-KpnI site of the pDEST vector containing an sl2::venus sequence ( pDEST-sl2::venus ) . The PCR-amplified daf-16 promoter sequences were inserted into the BamHI-NotI site of the pENTR vector ( pENTR-1A ) . The promoter region was inserted upstream of each daf-16 isoform cDNA by the LR reaction using the LR reaction kit ( Thermo Fisher Scientific , Japan ) . Plasmid DNA was transformed into E . coli competent cells ( DH5α or DB3 . 1 ) and extracted by the GENE-PREP-SYSTEM ( KURABO , Japan ) . Plasmids were purified by using the QIAquick PCR Purification Kit ( QIAGEN , Japan ) . The expression constructs for daf-16 were injected into animals at 5 ng μl-1 with a myo-3prom::venus transformation marker ( 10 ng μl-1 ) and an empty vector ( pPD49 . 26 ) . A daf-16a ( AM ) ::gfp-expressing plasmid was generated by PCR-based mutagenesis of the pGP30 plasmid including daf-16prom::daf-16a::gfp [13] . Both serine and threonine codons in the four putative Akt phosphorylation sites ( T54 , S238 , T240 , and S312 ) were mutated to an alanine codon ( GCT ) . Transgenic animals carrying daf-16a ( AM ) ::gfp transgenes were generated by introduction of the daf-16a ( AM ) ::gfp-expressing plasmid ( 5 ng μl-1 ) with a myo-3prom::venus transformation marker ( 10 ng μl-1 ) and an empty vector ( pPD49 . 26 ) . egl-21 cDNA was amplified from N2 total cDNA with KOD-One ( Toyobo ) DNA polymerase using primers , 5’-AGTAGCTAGCATGCTGCACGCGATGCG-3’ and 5’-GGACGATATCTTAACGACGACGGGCAATC-3’ . A daf-2 ( pe2722 ) mutant was generated by the CRISPR/Cas9 system . A Cas9 target site , 5'-GACGATGAAGAGCCCGGCGG-3' , was inserted into the pDD162 ( eft-3prom::Cas9 + Empty sgRNA ) vector as previously described [54] . The pDD162 vector containing the Cas9 target site was injected into animals at 50 ng μl-1 with a myo-3prom::venus transformation marker ( 10 ng μl-1 ) and an empty vector , pPD49 . 26 ( 40 ng μl-1 ) . The progenies with short deletions in exon 11 . 5 of the daf-2 gene were selected by PCR . The pe2722 mutation contains 41 bp deletion in exon 11 . 5 . To examine salt concentration learning , we performed salt chemotaxis assays after conditioning on agar plates with several different sodium chloride ( NaCl ) concentrations in the presence or absence of food . Salt chemotaxis assays were performed essentially as previously described [24 , 28] . Animals were grown to the adult stage on NGM plates seeded with E . coli NA22 at 20C . For conditioning , adult animals were collected with wash buffer ( 50 mM NaCl , 25 mM KPO4 , 1 mM MgSO4 1 mM CaCl2 , and 0 . 2% gelatin ) , washed twice and then transferred to agar plates containing 25 or 100 mM NaCl with or without NA22 . Animals were conditioned for six hours at 20C . The animals conditioned with food were collected with wash buffer , washed twice and placed at the center of a test plate . The animals conditioned without food were collected with wash buffer and transferred to a test plate . Animals were allowed to move freely for 45 minutes on the test plates . To prepare the test plate , two agar blocks ( 14 mm in diameter , including either 150 mM NaCl ( higher side ) or 0 mM ( lower side ) ) were placed at the positions 3 cm away from the center of a 9 cm agar plate 18–24 hours before behavioral tests . The agar blocks were removed and one microliter of 0 . 5 M sodium azide was spotted at those positions just before behavioral tests to immobilize animals around the spots . After behavioral tests , the test plates were transferred to 4°C refrigerator in order to stop the movements of all animals on the plates . The number of animals was counted in each region of test plates , and then the chemotaxis index was calculated by the equation as follows , where Nhigh-salt is the number of animals in the high-salt region , Nlow-salt is that in the low-salt region , Nstart point is that in the starting region and Nall is the number of all animals on test plates . Schematic of test plates was shown in our previous paper [28] . In many cases , we used 50–200 animals for each chemotaxis test . Chemotaxis index = ( Nhigh-salt - Nlow-salt ) / ( Nall - Nstart point ) In an auxin-inducible degradation assay , both degron::daf-16a , daf-16a cDNA N-terminally tagged with the degron sequence , and TIR1 cDNA were expressed in the ASER neuron . Sequences of degron and the TIR1 gene were shown [33] . Auxin ( Alfa Aesar ( #A10556 ) ) was dissolved in ethanol at 4 . 0 x 102 mM to prepare an auxin stock solution . Day 1 adult animals were transferred to NGM plates seeded with NA22 , which contain 1 mM auxin or 0 . 25% ethanol as a control . After incubation for two hours at 20°C , animals were collected with wash buffer , washed twice , and then transferred to a conditioning plate , which contains 100 mM NaCl and 1 mM auxin or ethanol ( control ) without food . Chemotaxis of the conditioned animals was tested as described above . Animals were grown to the adult stage at 20°C . Day 1 adult animals were mounted and anaesthetized on a 5% agar pad containing 10 mM sodium azide . In addition to bright field images , fluorescence images of Venus , mCherry and tagRFP were acquired by using a confocal microscopy ( Leica SP5 ) with 514 , 543 , and 633 nm excitation laser lights , respectively , and a 63 × objective . Animals at the L4 stage were transferred to a fresh NGM plate seeded with E . coli NA22 , which contains 50 mM of NaCl . After 24–30 hours , animals were mounted and anaesthetized on a 5% agar pad containing 10 mM sodium azide . In Fig 3D–3F , adult animals were mounted on the agar pad after further incubation for two hours at 25°C . Images were taken within 10 to 60 minutes after animals were mounted . In addition to bright field images , fluorescence images of GFP , mCherry , and tagRFP were acquired by using a confocal microscopy ( Leica SP5 ) with 488 , 543 , and 633 nm excitation laser lights , respectively , and a 63 × objective . Z-stack images were taken with 0 . 2 μm spacing . DAF-16::GFP fluorescence intensity ratios of the nucleus to the cytoplasm were measured by ImageJ Fiji ( version 1 . 0 ) . An image containing the largest area of the ASER cell body was selected from z-series images based on tagRFP expression . A region of the ASER nucleus was manually determined in the selected image based on mCherry expression . An average fluorescence intensity of DAF-16::GFP was calculated in each region of the nucleus and the cytoplasm ( that is , the peripheral region of the nucleus ) , and then the intensity ratio of the Nucleus/Cytoplasm was calculated . Animals were grown to the adult stage on NGM plates seeded with NA22 at 20°C and conditioned on a plate with 100 mM NaCl in the presence of food for 5–7 hours at 20°C . They were washed twice and transferred to a test plate containing 100 mM NaCl . Images of worms on the test plate were acquired for 15 minutes at a frame per second , and then locomotor speeds of the animals were calculated based on trajectories of the centroids of the animals using the worm tracking system as previously reported [24] . Total RNA was extracted from wild-type animals and two lines of transgenic animals expressing DAF-16a ( AM ) in ASER ( JN2874 and JN3212 strains ) . cDNA was synthesized by using PrimeScript RT reagent Kit ( Takara ) . The cDNA was used as templates for real-time PCR analysis using SYBR PreMix ExTaqII ( Takara ) . The list of primers is shown in S3 Table . The analyses were performed using Thermal Cycler Dice Real Time System ( Takara ) . The parameters for PCR were as follows: 95°C incubation for 30 seconds followed by 40 cycles of 95°C incubation for 5 seconds and 60°C incubation for 30 seconds . The expression level was normalized to that in the wild type and an eft-3 gene was used as an internal standard . One-way ANOVA with Tukey’s and Dunnett’s multiple comparison tests were used for statistical analyses . Two-way ANOVA with Bonferroni’s multiple comparison was used for statistical analyses for auxin-inducible degradation assays . Kruskal-Wallis and Mann Whitney tests were used for statistical analyses of real-time PCR assays and quantification of DAF-16::GFP localization . These analyses were performed by using GraphPad Prism 5 . 0 software ( GraphPad Software , La Jolla , CA ) . | Animals change behavior based on remembered experiences of hunger and appetite . Signaling by insulin and insulin-like peptides in the nervous system plays key roles in behavioral responses to hunger and satiety . In C . elegans , insulin-like signaling in the gustatory sensory neuron ASER regulates learned avoidance of salt concentrations experienced during fasting , which we call taste avoidance learning . DAF-2c , an isoform of the insulin receptor homolog , is localized to the axon of ASER and regulates taste avoidance learning . Here , we show that DAF-16 , the forkhead transcription factor O ( FOXO ) homolog , translocates into the nucleus of ASER during fasting and promotes taste avoidance learning . DAF-16 is negatively regulated by insulin-like signaling independently of axonal DAF-2c signaling . This dual function of insulin-like signaling in the cell body and the axon ensures dynamic changes in behavioral responses after experience of hunger . By genetic analyses using constitutively nuclear-translocated DAF-16 , we show that DAF-16 in ASER regulates taste avoidance learning via modulating neuropeptide signaling in the nervous system , which is reminiscent of the function of FOXO in the hypothalamus in the regulation of food-seeking behavior in mammals . | [
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] | 2019 | DAF-16/FOXO promotes taste avoidance learning independently of axonal insulin-like signaling |
Meiotic DNA double-strand breaks ( DSBs ) initiate crossover ( CO ) recombination , which is necessary for accurate chromosome segregation , but DSBs may also repair as non-crossovers ( NCOs ) . Multiple recombination pathways with specific intermediates are expected to lead to COs and NCOs . We revisited the mechanisms of meiotic DSB repair and the regulation of CO formation , by conducting a genome-wide analysis of strand-transfer intermediates associated with recombination events . We performed this analysis in a SK1 × S288C Saccharomyces cerevisiae hybrid lacking the mismatch repair ( MMR ) protein Msh2 , to allow efficient detection of heteroduplex DNAs ( hDNAs ) . First , we observed that the anti-recombinogenic activity of MMR is responsible for a 20% drop in CO number , suggesting that in MMR–proficient cells some DSBs are repaired using the sister chromatid as a template when polymorphisms are present . Second , we observed that a large fraction of NCOs were associated with trans–hDNA tracts constrained to a single chromatid . This unexpected finding is compatible with dissolution of double Holliday junctions ( dHJs ) during repair , and it suggests the existence of a novel control point for CO formation at the level of the dHJ intermediate , in addition to the previously described control point before the dHJ formation step . Finally , we observed that COs are associated with complex hDNA patterns , confirming that the canonical double-strand break repair model is not sufficient to explain the formation of most COs . We propose that multiple factors contribute to the complexity of recombination intermediates . These factors include repair of nicks and double-stranded gaps , template switches between non-sister and sister chromatids , and HJ branch migration . Finally , the good correlation between the strand transfer properties observed in the absence of and in the presence of Msh2 suggests that the intermediates detected in the absence of Msh2 reflect normal intermediates .
Meiotic crossovers ( COs ) are reciprocal exchanges of chromosome arms between homologous chromosomes ( homologs ) . They generate genetic diversity and establish physical links between homologs . In many organisms COs are crucial for accurate homolog segregation at meiotic division I , and the absence of COs leads to mis-segregation of homologs and aneuploid gametes ( for review [1] ) . Crossover control is therefore of extreme importance for normal meiosis . Crossovers result from the repair of programmed meiotic DNA double-strand breaks ( DSBs ) by the homologous recombination machinery . In most organisms DSBs outnumber COs , although to various degrees . A subset of DSBs that do not give COs is repaired without reciprocal exchange of chromosome arms and gives non-crossover products ( NCOs ) that can only be identified by gene conversions associated with the recombination process . DSB formation involves several proteins including the topoisomerase-like transesterase Spo11 protein that harbors the nucleolytic activity [2]–[4] . After DSB formation and Spo11 removal from the 5′ ends of the breaks [5] , 3′ single-stranded tails are generated and initiate recombination with homologous sequences [6] to ultimately produce COs and NCOs . Genetic and physical analyses performed in Saccharomyces cerevisiae suggest that the decision to form either a CO or a NCO is made before or during the transition between DSB formation and strand invasion of the homolog by one end of the DSB [7]-[10] . The molecular nature of this decision point remains to be elucidated . Two major pathways are involved in meiotic CO formation [11] , [12] . The ZMM pathway depends on the synaptonemal complex proteins Zip1 , Zip2 , Zip3 , the Mer3 helicase and the Msh4/Msh5 proteins , homologs of the bacterial mismatch repair protein MutS [9] , [13] . This pathway relies on the integrity of the synaptonemal complex [14] , a highly conserved structure that connects the homologs axes over their entire length ( for review [15] ) . The Mus81 pathway depends on the nuclease activity of Mus81 to resolve recombination intermediates [16] , [17] , independently of the synaptonemal complex integrity [12] , [18] . Residual COs in S . cerevisiae strains lacking both pathways suggest the existence of a third pathway that is likely repressed in a wild type context [11] . The balance between these pathways varies among organisms . CO formation results from both the ZMM and Mus81 pathways in S . cerevisiae , Arabidopsis thaliana and mammals , whereas it mainly results from the Mus81 pathway in Schizosaccharomyces pombe and from the ZMM pathway in Caenorhabditis elegans . The molecular mechanisms involved in CO control are relatively unknown despite several levels of CO regulation ( for review [19] ) . In organisms where the ZMM pathway is present , COs show interference , in that formation of a CO inhibits CO formation nearby . In addition , in genetic backgrounds where the number of DSBs is reduced , COs tend to be maintained at the expense of NCOs but the molecular mechanism of this crossover homeostasis remains unknown [20] . Both CO interference and CO homeostasis participate in the non-random distribution of COs along and among chromosomes . The original DSB repair model [21] proposed that both COs and NCOs result from distinct resolutions of a common recombination intermediate containing a double Holliday junction ( dHJ ) ( Figure 1 ) . However , subsequent physical and genetic analyses at a few recombination hot spots in S . cerevisiae showed that meiotic dHJs are almost exclusively resolved as COs , which depend on the integrity of the ZMM proteins [7] , [9] , [10] . This implies that NCOs result from an alternative recombination pathway , such as synthesis-dependent strand annealing ( SDSA ) , which does not produce dHJs [22]–[24] . The SDSA pathway is characterized by more evanescent strand invasion intermediates than the long-lived intermediates leading to dHJs [7] , [10] . In parallel , however , genetic studies led to the proposal that part of NCOs could also come from dissolution of dHJs [25] , a reaction known to be catalyzed in vitro by the combined action of a RecQ helicase and a type I topoisomerase [26] , [27] . Meiotic recombination is frequently associated with non-Mendelian segregation or 3:1 segregation of genetic markers . Less often , meiotic recombination is associated with post meiotic segregation ( PMS ) of genetic markers , which is identified by the formation of sectored colonies in fungi . Early studies based on these observations led to the original models of meiotic recombination . Holliday [28] as well as Meselson and Radding [29] suggested that heteroduplex DNA , which can lead to PMS , was the basic intermediate of recombination . Depending on the way mismatches are repaired , genetic markers within hDNA can be restored or converted yielding 4∶4 or 3∶1 segregation patterns , respectively . On another hand , Szostak et al . [21] favored the formation of double-strand gaps to explain the high frequency of 3∶1 segregation patterns . Extensive studies of PMS and the identification of mutants that increase PMS performed by Fogel and colleagues confirmed the formation of hDNA during meiotic recombination [30]–[32] . The association of hDNA with recombination intermediates was confirmed more recently by physical analysis [33] . Such mismatches are normally recognized and repaired by the mismatch repair machinery ( MMR ) ( for review [34] ) . The configuration of hDNA tracts is expected to vary depending on the recombination pathway and the specificity of the DNA transactions taking place ( Figure 1 ) ( for review [35] ) . Previous studies identified hDNAs at a few DSB hot spots either by using poorly repairable hairpin/loop extruding palindromes or by inactivating MMR . They revealed complex hDNA patterns and therefore proposed variations of the canonical recombination models [25] , [36] , [37] . In the presence of MMR , DNA polymorphism is a barrier to recombination and may lead to meiotic sterile progeny and therefore reproductive isolation of populations . In bacteria , this recombination barrier has been observed during transformation with polymorphic DNA [38] , [39] as well as during conjugation between diverged species [40]–[42] . In S . cerevisiae the efficiency of hDNA formation both in mitotic and meiotic cells decreases with increasing sequence divergence [43] , [44] . In eukaryotes , the current model proposes that homologs of the MutS bacterial MMR protein sense and reject mispaired hDNA intermediates formed during 3′ end invasion of the donor sequence . This hypothesis is reinforced by the enrichment in the MutS homolog Msh2 at the donor and recipient sequences near a DSB in the presence of sequence polymorphism ( for review [34] ) . Until recently most studies of meiotic COs , NCOs and hDNAs were based on a few loci corresponding to DSB hot spots and required the introduction of genetic markers . It is possible that the features and the balance between recombination pathways taking place at these loci do not reflect the average behavior of all loci . DNA arrays as well as deep sequencing allow the use of the natural polymorphic sites between diverged strains as markers to identify all recombination events between homologs generated during a single meiosis [45]–[48] . In order to better understand meiotic DSB repair mechanisms on a genome-wide level and therefore to explore CO formation control , we studied the DNA strand composition of the products of virtually all the interhomolog meiotic recombination events from two individual meioses of a SK1 x S288C hybrid lacking the Msh2 protein , using Affymetrix DNA tiling-arrays . This study provides for the first time a genome-wide view of hDNAs associated with COs and NCOs . This large data set allows a reassessment of current meiotic recombination models .
In order to identify meiotic COs and NCOs genome wide , we used an approach derived from the original work of Winzeler et al . [48] . It consists of crossing two polymorphic isolates that give rise to a fertile hybrid , in our case SK1 and S288C , inducing meiosis and genotyping each cell population coming from the four spores of a given meiosis to identify recombination events . For genotyping , each DNA from the four cell populations was hybridized onto one Affymetrix DNA tiling array ( GeneChip S . cerevisiae Tiling 1 . 0 R Array ) containing the genome of the S288C parental strain . The hybridization profiles were compared to those from the two parental strains to reveal their origins [49] . Such a strategy allows the identification of virtually all the single nucleotide polymorphisms between the parental strains [45] , [46] , [48] , [50] ( Figure 2 , Figures S1 and S2 ) . One major difference between our study and previous similar genome-wide studies is the hybrid used . We crossed SK1 and S288C that have about 0 . 7% sequence divergence [51] , [52] , which is more than between YJM789 and S288C ( 0 . 5% , [51] , [53] ) previously used [45] , [46] . Nevertheless , our hybrid goes through meiosis efficiently and spore viability is 70% in the presence of MMR and 63% in the absence of Msh2 . Importantly , the about 62000 sequence polymorphisms between SK1 and S288C that we used as markers are homogenously distributed along the genome , with no large region completely devoid of polymorphism [51] , [54] . This gives an average marker density of 1 per 194 nucleotides , with 97 . 5% of the inter-marker distances smaller than 1000 nucleotides and a median inter-marker distance of 77 bp . Only 11 regions without markers are longer than 10 kb and most of them correspond to Ty-containing loci . COs and NCOs were identified after analysis of the segregation patterns of all the natural polymorphic sites in the meiotic progeny of the SK1 x S288C S . cerevisiae hybrid . In the absence of recombination , all markers show a unique and continuous Mendelian segregation profile ( 2∶2 ) . COs , which characterize reciprocal exchanges between chromosome arms , lie at the junction between consecutive regions with two different Mendelian segregations of markers . Non-Mendelian segregation of markers ( 3∶1 ) at the exchange point reflects the presence of a gene conversion associated with the CO . When markers present a non-Mendelian segregation without chromosome arm exchange , they characterize a NCO . DSB repair by homologous recombination generates hDNA tracts . The patterns of hDNA tracts are expected to vary upon repair pathways . Analysis of hDNA patterns therefore provides a powerful tool to decipher recombination pathways [36] , [55] . Under normal circumstances , mismatches within hDNA are repaired by the MMR machinery . Inactivation of MMR is a common genetic tool to reveal hDNA intermediates . In our case , we chose to disrupt MSH2 to inactivate MMR and reveal hDNA intermediates , because Msh2 recognizes a large spectrum of mismatches but does not affect meiotic recombination per se in the absence of DNA polymorphism , unlike Mlh1 [56] . In the absence of MMR , haploid spores give rise to “mixed colonies” with information from both parents at each polymorphic hDNA . To reveal and trace hDNAs produced in a single meiosis , we therefore separated the mother cell from the daughter cell formed after the first mitotic division of each spore and genotyped the eight resulting cell populations from two meioses ( Figure 2 ) . This approach provides the genetic identity of each of the eight DNA strands from the four chromatids of the diploid hybrid after meiotic recombination ( Figure S1 and S2 ) [57] . In the absence of recombination , markers show a continuous Mendelian segregation profile ( 4∶4 ) . COs lie in between two consecutive regions with different Mendelian segregations . Gene conversions are characterized by either half- or full conversions showing respectively a 5∶3 or 6∶2 segregation profile of the markers . hDNAs correspond to half-conversions and are characterized by a 5∶3 segregation profile and can be associated with both COs and NCOs . We mapped COs in seven meioses from a S . cerevisiae hybrid obtained by crossing a wild type SK1 isolate and a wild type S288C isolate and in three meioses from a similar hybrid missing both alleles of MSH2 . We identified 73 and 92 COs per meiosis on average in the presence and absence of Msh2 , respectively ( Figure 3 ) . All chromosomes received at least one CO and the distribution of CO per chromosome is positively correlated to chromosome size ( Figure 3A and 3B ) , confirming previous observations in a YJM789 x S288C hybrid [45] , [46] . The correlation between CO number and chromosome size is stronger in the absence of Msh2 ( compare R2 in Figure 3A and 3B ) , suggesting that the presence of polymorphisms slightly affects CO distribution . The significant increase in COs ( p = 0 . 021 , Wilcoxon test ) in the absence of Msh2 is spread over the entire genome but the median distance between two COs is not significantly different from the 123 kb observed in a wild type hybrid ( p = 0 . 18 , Wilcoxon test , data not shown ) . The fact that spore viability is not improved in the absence of Msh2 despite an increase in properly distributed COs may result at least in part from the accumulation of recessive lethal mutations during the vegetative growth of the hybrid . Because Msh2 does not impact CO level in the absence of polymorphism [58] , our results show that the polymorphism between SK1 and S288C leads to a 20% drop in COs genome-wide through the action of Msh2 . This suggests that some DSBs are repaired using the sister instead of the non-sister chromatid . Such an hypothesis is supported by the recent finding that a meiotic DSB formed at a locus lacking direct homology on the homolog is efficiently repaired with the sister chromatid [59] . Alternatively , we cannot exclude that Msh2 shuttles potential CO intermediates into an interhomolog NCO path with limited strand transfer , the size of which would be below our detection threshold . Surprisingly , the 92 COs per meiosis on average in a SK1 x S288C hybrid lacking Msh2 are similar to the 90-95 COs observed in a YJM789 x S288C wild type hybrid [45] , [46] and to the about 86 COs determined genetically in homozygous S . cerevisiae isolates [60] , [45] . The results obtained with the two hybrids in the presence of Msh2 look contradictory since a recombination barrier imposed by the MMR seems to exist only in the SK1 x S288C hybrid but not in the YJM789 x S288C hybrid despite a significant level of sequence polymorphism . We envision several possible explanations . ( i ) The number of COs , as genetically determined in homozygous S . cerevisiae isolates , may be underestimated . In such a case , we would expect an increase in COs in the absence of functional MMR in a YJM789 x S288C , which has not been tested . ( ii ) MMR could be partially defective in the YJM789 x S288C , retaining its ability to repair mismatches necessary for gene conversions , but having lost its anti-recombinogenic activity . ( iii ) Only the sequence polymorphism between SK1 and S288C is above the threshold that triggers the anti-recombinogenic activity of the MMR . ( iv ) Finally , it is also possible that COs tend to be limited and maintained around 90 by the meiotic S . cerevisiae program . In this case , the MMR anti-recombinogenic action would be masked by CO limitation in the YJM789 x S288C background , but not in the SK1 x S288C hybrid , where sequence polymorphism becomes too high . Out of seven wild type meioses , we identified 27 NCOs per meiosis on average . In the absence of Msh2 , we identified 77 and 92 hDNA patterns associated with NCOs in two meioses with 88 and 93 COs , respectively ( Figure 3C ) . We did not analyze NCOs in the third msh2Δ meiosis that was used for CO analysis because of technical problems . As for COs , the number of NCOs is positively correlated to chromosome size but this correlation is weaker due to a higher variability of events per chromosome ( Figure 3A and 3B ) . Local lack of markers , short conversion tracts , and restoration , can all lead to an underestimation of strand transfer events [46] . If this fraction of events is similar for COs and NCOs , an assumption that may not be correct , it would be reflected by the fraction of COs where no strand transfers have been detected , i . e . 14% in the absence of MMR and 23% in the presence of MMR . Under this assumption , the actual average NCOs number per meiosis would be 98 in the absence of MMR and 35 in the presence of MMR . Overall , the number of NCOs detected in the absence of Msh2 was about 3 times higher than the number of NCOs observed in the presence of Msh2 ( Figure 3C ) . Interestingly , in the absence of Msh2 , CO and NCO numbers per meiosis approached parity ( 92 and 98 on average , respectively ) and their sum is compatible with the lowest estimates of 140-170 DSBs per meiosis [61] and 160 DSBs per meiosis [62] . This suggests that the low level of NCOs observed in a wild type context mainly results from a Msh2-related activity . MMR can mask NCOs by preventing recombination using a homologous chromatid and triggering repair using a sister chromatid as observed for COs ( see above ) . MMR can also mask NCOs by restoring parental information , an idea supported by the higher fraction of COs associated with strand transfer in the absence of Msh2 compared to a wild type context ( 86% versus 77% ) . Assuming there is no MMR bias toward either conversion or restoration at NCO sites , we would expect roughly as many conversions and restorations associated with NCOs . This would make the number of recombination events we observed in a wild type context compatible with an estimate of 140-170 DSBs per meiosis [61] , [62] without involving repair from the sister chromatid . However , in case there is a bias toward conversion at NCO sites as observed in [63] , then the number of recombination events we observed would be compatible with repair using the sister chromatid or repair with the homolog but without detectable conversions . Finally , given the partial defect in mismatch repair due to negative epistasis between the MLH1 and PMS1 genes from SK1 and S288C [64] it is possible that we missed some conversion events in the presence of Msh2 . Since the fraction of CO associated with a conversion event that we observed with the SK1 x S288C hybrid is comparable with the one from studies using the YJM789 x S288C hybrid , we anticipate that the number of missed NCOs due to the partial MMR defect is negligible . Formally , NCOs may arise from the SDSA pathway as well as the processing of dHJs . Simple SDSA , which consists in the invasion of a homologous sequence by a single end , generates a single 5∶3 hDNA tract [22] , [24] . SDSA has been shown to occur during meiosis [23] . Although not formally demonstrated , the two ends of a single DSB could engage SDSA independently and generate two 5∶3 tracts in trans configuration on the same chromatid ( double SDSA pathway ) . Such trans hDNA pattern could also arise from the dissolution of a dHJ by the combined action of a helicase and a topoisomerase I , like Sgs1 and Top3 as proposed by Gilbertson et al . [25] . The resolution of dHJs could also generate NCOs and leave two adjacent 5∶3 hDNA tracts onto the two recombining non-sister chromatids . Current models favor the idea that NCOs are mainly formed by the SDSA pathway [7] , [23] and that dHJs mainly give rise to COs . Based on the patterns of the persistent marks of strand transfer events , we identified 169 NCOs in two meioses in the absence of Msh2 ( Figure 4A ) . Out of these , 75 ( 44% ) present a strand transfer pattern compatible with the simple SDSA repair pathway ( Table 1 ) , 59 ( 35% ) present a pattern compatible with dHJ dissolution or double SDSA repair pathways ( Table 2 ) and the remaining 35 ( 21% ) present patterns impossible to attribute unambiguously to a particular origin ( Table 3 ) . These results confirm that the simple SDSA pathway is a major contributor to meiotic NCOs . In addition to this pathway , one unprecedented feature is the quantitative abundance of trans hDNA associated with NCOs , which are almost as frequent . Other studies from the S . cerevisiae ARG4 locus using poorly repairable hairpin extruding palindromes [25] and from a Drosophila msh6 mutant [65] also reported a significant level of trans hDNA associated with NCOs , suggesting a conserved mechanism . Interestingly , studies carried out at the HIS4 locus using either poorly repairable hairpins [37] or MMR deficient mutants [55] also revealed trans hDNAs but at a much lower frequency compared to us , and almost half of those events were associated with COs , which is not what we observed . Combined with ours , these results suggest that the frequency and the nature of the trans hDNAs may vary according to the locus . Among the 75 NCOs compatible with the simple SDSA repair pathway ( Table 1 and Figure 4A ) , 66 exhibit a continuous 5∶3 hDNA pattern on one strand only exactly as predicted by the canonical SDSA pathway . The other 9 show a discontinuous 5∶3 hDNA pattern , interrupted by 4∶4 ( 7 cases ) or 6∶2 ( 2 cases ) tracts . Both 4∶4 and 6∶2 tracts can result from Msh2-independent mismatch repair toward restoration and full conversion respectively as already described [36] , [55] , [66] , [67] . One possible mechanism for 4∶4 tract generation in the absence of Msh2 consists in two successive template switches during SDSA . The first switch would go from the non-sister chromatid to the sister or the parental chromatid , and the second switch from the sister or the parental chromatid back to the non-sister chromatid . Template switches have already been observed both in meiotic [68] and mitotic cells [69] at comparable frequencies ( about 10% ) . Out of the 59 NCOs compatible with dHJ dissolution or double SDSA repair pathway ( Table 2 and Figure 4A ) , 28 exhibit a continuous trans hDNA pattern on the same chromatid that we called 5∶3_5∶3* to indicate that the two consecutive 5∶3 tracts are different . Interestingly , 24 other NCOs exhibit a trans hDNA pattern with the two opposite hDNA tracts separated by a single 4∶4 ( 22 cases ) or 6∶2 tract ( 2 cases ) , showing a strong excess of 4∶4 tracts . Previous work from the Sekelsky laboratory already reported repair patches in between trans hDNAs but their analysis was restricted to one meiotic chromatid only [65] , [70] . Finally , 7 NCOs with trans hDNA present more complex profiles similar to the complex profiles observed for simple SDSA events . Formally , the trans hDNA pattern could result from double SDSA involving homolog invasion from the two ends of the break ( Figure 1 ) . The significant fraction of events containing a restoration patch separating the two hDNA tracts could be explained if one end first invades the sister chromatid and then undergoes a template switch to the homolog ( Figure 4B ) . However , the template switching frequency observed in mitotic and meiotic cells is much lower than the fraction of restoration tracts observed in this trans hDNA category ( about 10% vs 50% ) [68] , [69] . In addition , recent findings support a model in which only one end searches and invades one homologous non-sister chromatid while the other is kept with the sister [71] , [72] , which would disfavor the double SDSA pathway . Alternatively , the fact that roughly half of the trans hDNAs contain a restoration patch separating the two hDNA tracts could indicate the occurrence of a recombination intermediate containing an entry point for Msh2-independent mismatch repair specifically located close to the junction of the hDNA tracts . Such an entry point could be a nick abnormally left unrepaired after the combined action of a helicase and a topoisomerase I during the dissolution of a dHJ . Such a nick could be used as a primer for DNA synthesis leading to Msh2-independent mismatch repair ( Figure 1 and Figure 4B ) . Interestingly , nicks that would have resulted from double SDSA are not expected in between but on both sides of the hDNA tracts and their repair would have induced a different pattern . Radford et al . identified trans hDNAs in the D . melanogaster mei-9 mutant . Because MEI-9 is essential to CO formation and it encodes an ortholog of the S . cerevisiae Rad1 endonuclease , the authors proposed that unresolved dHJs may lead to NCOs by dissolution [70] . In conclusion , our observations suggest that at least part of trans hDNAs come from dissolution of dHJs . dHJs would therefore constitute a novel putative control point for CO versus NCO formation ( Figure 4D ) . The hypothesis that a significant fraction of dHJs give rise to NCOs appears inconsistent with the fact that NCOs level is not affected either by the absence of the transcription factor Ndt80 that induces the accumulation of unresolved joint molecules ( JMs ) or by the absence of the ZMM proteins that strongly impedes JMs formation [7] , [9] , [73] . These observations led to the model that dHJs do not give rise to NCOs but only to COs . We envision two possibilities to reconcile this hypothesis to ours . The first possibility is to suggest that only the fraction of JMs that is meant to become COs by dHJ resolution did not form or remained unresolved and accumulated in the absence of the ZMM proteins or Ndt80 , respectively . A minor fraction of JMs engaged to dHJ dissolution would still form and be processed properly as NCOs . The second possibility is that another type of recombination intermediates that has not been detected so far yields dHJs that are meant to become NCOs by dHJ dissolution ( Figure 4D ) . The hypothesis that a significant fraction of dHJs could give rise to NCOs is supported by the increase in COs in the absence of Sgs1 , known to catalyze dHJ dissolution in vitro in combination with the type I topoisomerase Top3 [45] . This increase in COs has been observed in mitosis [74] and in meiosis both at specific loci [75] and genome-wide [45] . Further support comes from the observation that Sgs1 deletion in the ZMM mutants rescues their CO defect . This led to a model in which the ZMM proteins would stabilize and protect early recombination intermediates from the action of Sgs1 [73] , [76] , [77] . Moreover it has been observed that TOP3alpha/Top3 and BLAP75/Rmi1 , which act together with RECQ4A/Sgs1 to unwind a dHJ in vitro [26] , are essential for proper meiotic progression in A . thaliana [78]-[80] . Out of the 35 remaining NCOs ( Table 3 and Figure S3 ) , 13 present relatively simple strand transfer patterns composed of a 6∶2 tract , associated with a 5∶3 tract in 5 cases . These events have been considered separately from the two previous classes because each of them could have arisen from either pathway , with 6∶2 tracts resulting from either double-strand gap repair or full conversion . 9 out of the 35 present hDNA tracts on two homologous chromatids , with 4 of them having two overlapping hDNA tracts reminiscent of Holliday junction branch migration within a homoduplex DNA that forms symmetric hDNAs ( aberrant 4∶4* tracts ) ( Table 3 ) . This confirms that the dHJ pathway poorly contributes to NCO formation as was previously observed [37] . The 13 remaining NCOs present more complex strand transfer patterns coming from multiple putative origins . Out of 181 COs from the two msh2Δ meioses for which hDNAs have been analyzed , 155 were associated with strand transfers ( Table 4 , Figure 4C , and Figure S4 ) . Surprisingly , all the strand transfer patterns were different from the pattern predicted by the canonical CO pathways , i . e . two continuous 5∶3 tracts distributed on the two non-sister chromatids around the DSB site ( Figure 1 ) . Only 36 COs out of 155 ( 23% ) carried hDNA on the two non-sister recombining chromatids , and only 19 ( 12% ) of them presented a strand transfer pattern compatible with the outcome of the canonical CO pathways ( Table 5 ) . Remarkably , 13 out of these 19 COs presented clearly asymmetric hDNA tract length , with a long hDNA tract on one chromatid and a short hDNA tract on the other chromatid ( Table 5 , and see example Figure S2C ) . This hDNA tract asymmetry is consistent with previous genetic studies that reported infrequent co-conversion of markers flanking a single DSB hot spot [81] . Such an asymmetry could reflect an asymmetric positioning of recombination intermediates around DSBs resulting from either a limited strand invasion combined to an extensive DNA synthesis , or an extensive strand invasion combined to a limited DNA synthesis , as proposed by Jessop et al . [81] . Alternatively , hDNA tract length asymmetry could result from migration of the D-loop [82] after the first strand invasion and extension . D-loop migration could partially or completely dismantle the first hDNA formed ( Figure 5A ) . Unexpectedly , we observed that the majority of strand transfer tracts of COs ( 119/155 ie 77% ) are present on one chromatid only ( Figure 4C , Figure S2A and S2B , Figure S4 , Table 4 , and Table 5 ) . In many cases these events occurred in regions with high marker density , ruling out detection artifacts due to local lack of markers to explain this asymmetry . The migrating D-loop model could also explain this asymmetric distribution of strand transfer tracts onto the recombining chromatids at CO sites ( Figure 5 ) . Formation of a stable single end invasion ( SEI ) intermediate prior to CO formation [10] , [33] , [81] could favor D-loop migration . More specifically , it is likely that the first invading end is extended prior to capture of the second end , although direct evidence is still lacking . This would leave the opportunity for the corresponding junction to migrate in either direction with respect to the invasion point and therefore affect the size of the corresponding hDNA up to its disappearance ( Figure 5 ) . In situations where D-loop migration is followed by dHJ formation and migration , 4∶4 tracts and aberrant 4∶4 tracts are expected to form . This scenario is supported by the frequent strand transfer patterns composed of a single 5∶3 or 6∶2 tract associated with a 4∶4 tract ( Table 4 and Figure S4 ) , which cannot simply result from an asymmetric positioning of recombination intermediates around the DSB . Interestingly , these observations are also consistent with what can be observed in wild type meioses . As shown in Table 6 , nine COs are separated from their associated 3∶1 gene conversion by a 2∶2 segregating tract . Savage and Hastings also observed such a pattern by performing a systematic analysis of the segregation patterns of multiple markers at the S . cerevisiae HIS1 locus [83] . Nevertheless , aberrant 4∶4 tracts are infrequent . One possible explanation for this puzzling observation is that the source of aberrant 4∶4 tracts , which is the region between the two HJs , is so small after dHJ migration that it is hardly detectable ( Figure 5A ) . This explanation is compatible with the inter-junction distance measured for dHJs visualized by electron microscopy that averages 260 bp [18] , [84] . One can also imagine that aberrant 4∶4 tracts within dHJs disappear by branch migration during a putative sequential resolution of the two HJs ( Figure 5A ) . Finally , we cannot formally exclude Msh2-independent mismatch repair as a source of asymmetry in strand transfer distribution at CO sites . Very interestingly , we did not observe asymmetry in strand transfer tract length at NCOs with trans hDNAs ( data not shown ) , for which we can assume rather confidently that the initiating DSBs are located at the boundary between the hDNA patches . These apparently contradictory observations raise an interesting question: are dHJs dissolved into NCOs different from dHJs resolved into COs ? Multiple observations could support such a difference . First , Pan et al . [62] recently proposed that at least part of DSB ends may be processed asymmetrically . Formation and maturation of the dHJ could depend on the nature of the invading end i . e . with long or short single-stranded tail . Alternatively , symmetric or asymmetric processing of the two ends of a given DSB could generate different types of dHJs . Second , it has been recently proposed that the two ends of a DSB were sequentially released to interact with the homolog [71] . Formation and maturation of the dHJ could depend on the release of the second end . Third , maturation of the dHJ could depend on the properties of the D-loop . D-loop migration could be necessary to form stable CO intermediates ( SEI ) . Finally , it is possible that formation and maturation of dHJs depend on the combined action of all these factors . Previous work pointed out the complexity of CO-associated strand transfer patterns with a limited number of markers [37] . Taking advantage of a greater marker density , we confirmed such a complexity and revealed patterns even more complex . Among 155 strand transfer patterns associated with COs , 112 comprised between 2 to 8 successive DNA tracts including 49 patterns comprising more than 3 successive DNA tracts ( Table 4 ) . This complexity results from the accumulation of 6∶2 and 4∶4 tracts in between 5∶3 tracts . As seen above , NCO-associated hDNA patterns also show marks of Msh2-independent mismatch repair but more than half of them are as expected without additional marks of complex events . This shows that CO intermediates present specificities that make them prone to complex events , notably through Msh2-independent mismatch repair [36] , [55] , [66] , [67] . As already mentioned , branch migration of HJs is a source of 4∶4 tracts associated with COs , as well as template switches between non-sister and sister chromatids . The repair of gaps is a source of 6∶2 tracts . In that respect , it is interesting to note that 6∶2 tracts are more frequently associated with COs ( 104; 67% ) compared to NCOs ( 31; 19% ) ( Table 5 ) . It suggests that gaps between DSB ends could preferentially be repaired by a CO pathway . Such gaps may arise from two very close Spo11-induced DSBs or from 3′ end removal most likely after invasion of a homologous sequence . As for dHJ dissolution , we propose that entry points for Msh2-independent mismatch repair could also be nicks in dHJ intermediates . Such nicks could lead to either 6∶2 or 4∶4 patches if they are used to prime DNA synthesis , as well as the more complex 5∶3_3∶5 pattern ( see below ) . These nicks could be intrinsic properties of dHJs that could exist under a non-ligated form [85] . They could also result from the action of structure-specific nucleases during , or independently , of the resolution process . Alternatively , such nicks could also result from Msh2-independent processing of mismatches ( Figure 5 ) . In conclusion , although compatible with dHJ-containing recombination intermediates [86] , the majority of CO-associated hDNA patterns are made more complex by frequent Msh2-independent mismatch repair [36] , [55] , [66] , [67] that could result from a combination of factors including repair of double stranded gaps , repair of nicks , template switches between non-sister and sister chromatids , and HJ branch migration . The most puzzling CO-associated hDNA pattern that we observed comprises opposite hDNA tracts i . e . 5∶3_3∶5 tracts in 26 cases ( 17% ) while it is almost never associated with NCOs ( 4 cases , 2% ) ( Table 5 ) . Such a pattern is usually interpreted as resulting from two recombination events at the same locus . We reasoned that under this scenario at least three chromatids should frequently be involved . However , this was never the case , suggesting that the 5∶3_3∶5 pattern results mainly from the repair of only one DSB . We propose that nick translation combined with HJ branch migration can transform a 5∶3 tract into the opposite 3∶5 configuration ( Figure 5Be ) . In the presence of MMR , hDNAs are repaired and can lead to full conversions . In the absence of MMR , only hDNAs associated with the final recombination products can be revealed but not the transient ones . Comparing the lengths of the strand transfers in the presence and absence of Msh2 can therefore be informative about the processing of hDNAs . The strand transfer tract size considered for a given CO or NCO in the absence of Msh2 corresponds to the sum of the length of all the individual 5∶3 , 6∶2 and 4∶4 patches associated with the event . We observed that the median sizes of strand transfers associated with both COs and NCOs are significantly smaller in the absence of Msh2 ( Figure 6A and 6B ) . In the case of COs , this size difference is mild ( 1 . 6 kb vs 1 . 8 kb , p = 0 . 029 Wilcoxon test ) , but it is much greater for NCOs ( 1 kb vs 1 . 8kb , p = 7 . 7×10−10 Wilcoxon test ) . Much of the increase in total number of NCOs in the absence of Msh2 comes from a greater number of short tract events ( Figure 6B ) . A possible explanation comes from the fact that transient hDNAs formed during recombination cannot be detected in the absence of MMR whereas they can formally be repaired and converted by MMR . As a consequence , conversion tracts are expected to be shorter in the absence of MMR ( Figure 7 ) . Interestingly , the SDSA-compatible NCOs are the major contributors to small size events in the absence of Msh2 with a median size about 2 fold smaller than the strand transfers associated with NCOs in the presence of MMR ( Figure 6B and 6C ) . This result is compatible with frequent conversions of both hDNAs formed by strand invasion and second end capture during SDSA when Msh2 is present while the hDNA formed by strand invasion is transient and not detectable in the absence of Msh2 unlike the hDNA resulting from second end capture [87] ( Figure 7 ) . Strand transfer tract length analysis did not allow us to determine which of the double SDSA or the dHJ dissolution pathway is the main precursor of NCOs with trans hDNAs . NCOs with trans hDNA patterns show a median length similar to the median length of CO-associated hDNA tracts . This observation is compatible with the dHJ as a common precursor . However , trans hDNA could also result from double SDSA events . We therefore considered independently the strand transfers from the two DNA strands for NCOs with trans hDNAs and found that their median sizes and size distributions are not significantly different from those of strand transfers compatible with single SDSA events ( data not shown ) . When present , the MMR machinery repairs mismatches formed during strand transfer by excision of one of the two DNA strands and subsequent gap fill in . Depending on which strand is repaired , the corresponding markers show either a continuous Mendelian ( 2∶2 ) or non-Mendelian ( 3∶1 ) segregation profile . We analyzed the segregation profiles of markers associated to COs or NCOs out of three meioses in the presence of MMR ( Table 6 and Table 7 ) . Among 65 NCOs , 53 ( 82% ) presented a uniform conversion tract of 3∶1 , as expected from the canonical SDSA pathway ( Table 7 , Figure 1 , and Figure 7A ) . In contrast , in the absence of Msh2 , only 44% of strand transfers associated with NCOs were compatible with the canonical SDSA pathway , and a high fraction ( 35% ) showed trans hDNAs that we proposed to result from double SDSA or dHJ dissolution ( Table 1 , Table 2 , and Figure 4A ) . We proposed above that the 3′ single strand ends formed at DSBs are frequently converted during strand invasion and second end capture . Under this assumption , the dHJ dissolution and double SDSA pathways are expected to form long and uniform 3∶1 segregation profiles in the presence of MMR ( Figure 7B ) . Any nick that would form during dHJ dissolution would take place within a homoduplex DNA and its repair would therefore be undetectable ( Figure 7B ) . Such repair is also expected to affect most hDNAs resulting from template switches between non-sister and sister chromatids and make them undetectable . Altogether , these explanations are consistent with the fact that the combined fraction of SDSA-like and trans hDNA patterns observed in the absence of Msh2 ( 79% ) is comparable with the fraction of 3∶1 pattern observed in the presence of Msh2 ( 82% ) . The remaining NCOs with complex strand transfers that affect two non-sister chromatids observed both in the presence and absence of Msh2 could result from pathways involving dHJ resolution ( Figure 1 ) . Interestingly , COs formed in the presence of Msh2 also showed mainly strand transfers with uniform 3∶1 marker segregation ( 88% ) ( Table 6 ) . We tested if the two CO formation models derived from the analysis of strand transfers in the absence of Msh2 ( Figure 5 ) were compatible with this observation . Figure 7C describes how the repair of 3′ single strand ends during strand invasion and second end capture could lead to the formation of a uniform 3∶1 segregation pattern in both CO formation pathways in the presence of MMR . As for NCOs , such an early repair of hDNA in the presence of MMR would prevent detection of nick repair and most template switches between non-sister and sister chromatids in CO intermediates . This scenario supports the higher frequency of complex events observed in the absence versus the presence of Msh2 , where 43% and 5% of the strand transfers present more than two DNA tracts , respectively ( Table 4 and Table 6 , and Figure 7C ) . Although we cannot formally exclude that the absence of Msh2 itself could constitute a source of complexity for recombination events , we do not favor this hypothesis . The fact that the COs and NCOs strand transfer patterns observed in the absence of Msh2 can explain the strand transfer patterns observed in the presence of Msh2 supports the idea that the intermediates observed in the absence of Msh2 reflect normal intermediates . In conclusion , the genome-wide analysis of recombination intermediates performed in the absence of Msh2 reinforces the plasticity of CO formation already anticipated from hot spots-specific studies . In particular , we showed that the majority of hDNAs associated with CO are asymmetrically distributed onto the recombining chromatids , leading us to propose variations of current recombination models . Interestingly , our study also revealed that a significant fraction of NCOs do not arise from simple SDSA , raising the idea that dHJs are also involved during NCOs formation . Further studies using specific mutants such as mutants of the RecQ helicase Sgs1 will be needed to clarify the nature of such alternative pathway ( s ) . Because meiotic recombination is well conserved through evolution , the new findings presented in this work will have a broad impact , and serve to further enlighten this complex topic .
All yeast strains used in this study are derivatives of S288C [88] and SK1 [89] . Strain genotypes are listed in Table S1 . MSH2 disruption was performed by PCR-mediated gene replacement [90] and the sequences of the oligonucleotides used are in Table S2 . SK1 x S288C crosses were made on YPD plates and diploids were subcloned onto selective plates before transferring onto 2% potassium acetate sporulation plates at 30°C . Tetrads were dissected after 3–5 days . For WT5 , 6 and 7 , sporulation was induced in liquid medium . The hybrids made involved the following crosses∶ NHY113 and SK1708 for wild type; BLY107 and BLY114 for msh2Δ . Wild type tetrads were dissected and only tetrads giving four viable spores were considered for genotyping by tiling array DNA hybridization ( Table S3 ) . Note that the whole colonies arising from spore germination have been genotyped , thereby neglecting potential hybridization problems due to heterozygosities resulting from post-meiotic segregation [57] . Three control hybridizations were performed for each parental strain ( Table S3 ) . To study hDNA in msh2Δ hybrids , two octads were obtained from two tetrads by separating the mother from the daughter cell after the first mitotic division of four viable spores as described in Figure 2 . The third meiosis from the msh2Δ hybrid was only considered for CO analysis , thus only four cell populations from the four spores were genotyped . Genomic DNA was purified from 300 ml of overnight saturated YPD culture using a Qiagen genomic-tip 500/G following the Qiagen genomic DNA handbook with the slight modification of extending zymolyase and protease K digestion to 1 hour . 12 µg of genomic DNA were fragmented by DNaseI treatment , biotin-end-labeled and hybridized to Affymetrix S . cerevisiae Tiling 1 . 0R Array as described in [50] . We genotyped spores using ssGenotyping [46] , [49] . The genotypes are provided in Table S4 . SK1 sequence was obtained from the Saccharomyces Genome Resequencing Project [51] . SK1-S288C genome alignment has been performed using LAGAN [91] . To prevent the consideration of artificial polymorphisms using ssGenotyping , alignments have been modified by replacing the Ns from the SK1 sequence by the corresponding S288C sequence when it was either A , C , G or T . Annotation of every single CO and NCO has been curated manually using tetrad inspector from the ssGenotyping package . Graphical views of recombination events are available upon request . Recombination events identified by only one marker ( Table S5 ) were discarded . Events identified by 4∶0 segregating markers were considered to be of mitotic origin and were not taken into account . Conversion tracts lengths are tract size estimates obtained using midpoints of flanking inter-marker intervals . Events separated by less than 5 kb were considered to have arisen from the same DSB and were therefore combined . This rule was not applied when a gene conversion occurred on a chromatid not involved in a CO but located less than 5 kb away from the CO . In this latter case , the gene conversion was considered separately from the CO . To analyze msh2Δ octads , we arbitrarily divided each octad in two tetrads that were analyzed independently with ssGenotyping ( see Figure 2 ) . Recombination events were reconstituted manually by combining the genotypes of the two arbitrary tetrads . Raw data are available from ArrayExpress ( http://www . ebi . ac . uk/arrayexpress ) under accession number E-MTAB-508 . | Sexual reproduction consists in fusing two complementary gametes carrying only one set of chromosomes ( haploids ) to form a cell with two sets of homologous chromosomes ( diploid ) . Gametes are generated through meiosis , a specialized cell division occurring in diploid organisms . For proper meiotic division to occur , homologous chromosomes need physical connections acquired through recombination that exchange chromosome arms ( crossovers ) and thereby contribute to genetic diversity . Recombination is induced by numerous chromosome breakages , but only a subset yields crossover recombinants , the remaining yielding non-crossover recombinants . Control of crossover formation is poorly understood . For this reason , precise knowledge of the meiotic recombination mechanisms is essential . Current models are based on studies performed at a few loci in model organisms . We revisited these models using an original approach that allowed us to study the DNA scars left at all chromosome breakage sites during single meioses in baker's yeast . We found that crossover formation is more dynamic than anticipated , which led us to propose variations of current crossover formation models . We also revealed that a significant fraction of non-crossovers do not arise from the canonical pathway , raising the possibility of a common pathway with crossover formation . | [
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] | 2011 | Genome-Wide Analysis of Heteroduplex DNA in Mismatch Repair–Deficient Yeast Cells Reveals Novel Properties of Meiotic Recombination Pathways |
Genomic maps of chromatin modifications have provided evidence for the partitioning of genomes into domains of distinct chromatin states , which assist coordinated gene regulation . The maintenance of chromatin domain integrity can require the setting of boundaries . The HS4 insulator element marks the 3′ boundary of a heterochromatin region located upstream of the chicken β-globin gene cluster . Here we show that HS4 recruits the E3 ligase RNF20/BRE1A to mediate H2B mono-ubiquitination ( H2Bub1 ) at this insulator . Knockdown experiments show that RNF20 is required for H2Bub1 and processive H3K4 methylation . Depletion of RNF20 results in a collapse of the active histone modification signature at the HS4 chromatin boundary , where H2Bub1 , H3K4 methylation , and hyperacetylation of H3 , H4 , and H2A . Z are rapidly lost . A remarkably similar set of events occurs at the HSA/HSB regulatory elements of the FOLR1 gene , which mark the 5′ boundary of the same heterochromatin region . We find that persistent H2Bub1 at the HSA/HSB and HS4 elements is required for chromatin boundary integrity . The loss of boundary function leads to the sequential spreading of H3K9me2 , H3K9me3 , and H4K20me3 over the entire 50 kb FOLR1 and β-globin region and silencing of FOLR1 expression . These findings show that the HSA/HSB and HS4 boundary elements direct a cascade of active histone modifications that defend the FOLR1 and β-globin gene loci from the pervasive encroachment of an adjacent heterochromatin domain . We propose that many gene loci employ H2Bub1-dependent boundaries to prevent heterochromatin spreading .
There is growing consensus that the non-random chromosomal arrangement of genes in higher eukaryotes enables the sharing of specific chromatin environments that facilitate co-regulation . Recent genomic profiling of histone modifications , chromatin factors and nuclear proximity in Drosophila and mammalian cells have revealed prevalent organization of genes into domains , or neighborhoods , of common chromatin state [1]–[5] . Genes taken out of their natural chromosomal environment become deregulated in a variety of human genetic diseases [6] . This so-called chromosomal position effect also underlies the variable expression of transgenes depending on their site of integration [7] . The maintenance of chromatin domain integrity can require the setting of boundaries . Boundaries not only allow the partitioning of gene regulation , but also may also maintain the concentration of factors required for heterochromatin structures and normal genome homeostasis [8] . Fixed chromatin boundaries can be established by DNA sequence elements called insulators , which function to protect genes from inappropriate signals emanating from their surrounding environment [9]–[12] . HS4 is a well characterized element that has served as a paradigm for the study of insulators in vertebrates . HS4 lies at a boundary between the chicken β-globin gene cluster and upstream region of condensed chromatin that is enriched in the epigenetic hallmarks of heterochromatin [13]–[15] . A 275 bp core of the HS4 element has two separable activities that functionally define insulators: it can block the action of an enhancer element on a linked promoter when positioned between the two and it can act as a barrier to chromosomal position effect silencing [16]–[18] . The enhancer blocking and barrier activities of HS4 involve different proteins and mechanisms and are separable in assay systems . The CTCF binding site footprint II ( FII ) is necessary and sufficient for enhancer blocking , but can be deleted from HS4 without affecting barrier activity [18]–[20] . HS4 requires a USF1/USF2 binding site ( FIV ) and three VEZF1 binding sites ( FI , FIII and FV ) for its barrier activity , which control histone modifications and DNA methylation , respectively [21]–[24] . HS4 manipulates histone modification signatures to counteract gene silencing [22] , [24] . HS4 has been found to be persistently enriched in high levels of H3 and H4 acetylation , H3-lysine 4 methylation , H4-arginine 3 methylation and acetylated histone variant H2A . Z regardless of neighboring gene expression [13]–[14] , [23] , [25] . We proposed that the active histone modifications at HS4 collectively act as a chain terminator to heterochromatin assembly by interfering with the propagation of repressive histone modifications [24] . Given that chromosomal silencing has been shown to be processive and stable , we reasoned that the HS4 element needs to act as a constitutive barrier if it is to effectively shield the locus . In this study , we address a hypothesis that HS4 might recruit histone modifications that act as master controllers of the active chromatin state to facilitate barrier stability . Intense study in recent years has begun to unravel the complex language of crosstalk between histone modifications during the establishment of different chromatin states [26] . Principal among the active histone modifications is the monoubiquitination of H2BK120 ( H2BK123 in S . cerevisiae ) , which is required for the tri-methylation of H3K4 [27]–[30] . H3K4me3 is a pivotal mark of the active chromatin state , by acting as a platform for the binding of multiple histone acetyltransferase , histone demethylase and nucleosome remodelling complexes [31]–[33] . We therefore investigate whether i ) H2B ubiquitination directs a cascade of active histone modifications at the HS4 insulator , ii ) this modification is required for its barrier activity , and iii ) the integrity of the 3′ chromatin boundary of the condensed chromatin located upstream of the β-globin locus . We also extend our analysis to look at the 5′ chromatin boundary of the same condensed chromatin and its role in shielding the FOLR1 gene locus .
We sought to address whether histone H2B ubiquitination plays a key role in establishing and maintaining the boundaries of a condensed heterochromatin-like domain that separates the FOLR1 and β-globin gene loci . Firstly , we mapped the presence of ubiquitinated nucleosomes across 50 kb encompassing the chicken β-globin locus ( Figure 1 ) . We established native chromatin immunoprecipitation ( N-ChIP ) assays using nucleosomes prepared by micrococcal nuclease ( MNase ) digestion of chromatin in low salt conditions to ensure the retention of potentially unstable variant nucleosomes found at this locus [34] . We prepared di- and tri-nucleosomes using a range of MNase concentrations which ensured that they were representative of open and condensed chromosomal regions ( Figure 1A , data not shown ) . The N-ChIP method strips away non-nucleosomal proteins ( Figure 1B ) , which allows the analysis of ubiquitinated histones using anti-ubiquitin antibodies . The enrichment of nucleosomes containing the 25 kDa monoubiquitinated form of H2B was confirmed by western blotting ( Figure 1C ) . N-ChIP analysis of histone ubiquitination was performed on primary red blood cells ( RBC ) from 10 day chick embryos , in which the β-globin locus is highly transcriptionally active , but the 5′ folate receptor ( FOLR1 ) locus is silent [15] , [35] . We observe a striking enrichment of ubiquitinated histones specifically at the core HS4 insulator element ( Figure 1D; p-value from student's t-test of enrichment = 2e−6 ) . Perhaps surprisingly , no enrichment of histone ubiquitination was observed at the promoters or enhancers of the highly active β-globin genes in RBCs . We also mapped histone ubiquitination in 10 day embryo whole brain tissue ( Figure 1E ) , where both the FOLR1 and β-globin genes are reported to be silent [15] . We also observe a specific enrichment of ubiquitinated histones specifically at the core HS4 insulator element in brain tissues ( Figure 1E , p-value = 6e−5 ) . We also observe significant levels of histone ubiquitination at the FOLR1 gene regulatory elements HSA and HSB in both 10 day embryo RBCs ( Figure 1D , p-value = 0 . 007 ) and whole brain ( Figure 1E , p-value = 2e−5 ) . These elements are situated between the FOLR1 gene and the condensed region and may harbor chromatin boundary activity . We sought to determine which of the well characterized activities of the 275 bp core HS4 element are required for the recruitment of histone ubiquitination . We performed N-ChIP analyses of histone ubiquitination at HS4 insulators present on single copy transgenes stably integrated into the early erythroid CFU-E stage cell line 6C2 [18] . We find that transgenic HS4 insulators are enriched in histone ubiquitination at a level equivalent to the endogenous HS4 element ( WT , Figure 1G ) . Histone ubiquitination is therefore likely to be recruited by one of the factors that mediate the insulator functions of the core HS4 element . We performed N-ChIP analysis of single copy transgenic HS4 elements that are mutated at the CTCF ( FII ) , VEZF1 ( FIII ) or USF1 ( FIV ) binding sites . These mutations have been extensively characterized and disrupt HS4's ability to mediate enhancer blocking , protection from DNA methylation or active histone modification , respectively [19] , [21] , [24] . We find that the USF1/USF2 binding site , footprint IV , is required for the recruitment of histone ubiquitination ( Figure 1G ) . This correlates with our previous finding that the USF site was also required for H3K4 methylation of the HS4 insulator [24] . The histone ubiquitination that is enriched at the HS4 element may occur on any of the core histones . We anticipated that histone H2B is subject to this modification as HS4 is constantly enriched in methylated H3K4 [13] , [24] , and H2BK120 mono-ubiquitination is required for proper H3K4 methylation [27]–[30] . Using crosslinking ChIP with recently developed antibodies , we confirmed that the core HS4 insulator was enriched in H2BK120ub1 in the early erythroid CFU-E stage cell line 6C2 ( Figure 2A ) . We were unable to detect any enrichment of H2AK119ub1 at HS4 or other β-globin sequences ( not shown ) . We sought to identify the E3 ligase responsible for H2Bub1 at the HS4 element so that the effects of depleting H2Bub1 could be studied . We used crosslinking ChIP analysis to show that RNF20 interacts with the core HS4 insulator element in erythroid cells ( Figure 2B ) . Chicken RNF20 ( BRE1A ) , is 90% identical to human RNF20/BRE1A , which is an E3 ligase responsible for efficient H2B ubiquitination ( Figure S1A ) [36]–[37] . The presence of RNF20 therefore suggests that this enzyme is required for the enrichment of H2BK120ub1 at the HS4 insulator . Next , we investigated whether H2Bub1 levels can be depleted following RNAi of RNF20 . It was important that we were able to knockdown RNF20 levels for prolonged periods as this would allow the study of progressive repression of the β-globin locus and insulated transgenes . This was achieved in 6C2 cells using a lentiviral vector system for doxycycline-regulated expression of miRNA-shRNA ( Materials and methods , Figure S1 ) . After four days of shRNA expression , RNF20 protein levels were reduced to ∼20% of wild type ( Figure 2C ) . We saw little change in the whole cell protein levels of the HS4-binding proteins CTCF , VEZF1 or USF1 . We studied the effect of this short term knockdown of RNF20 expression on a panel of histone modifications in total chromatin . We found that H2BK120ub1 levels in chromatin were reduced by ∼80% ( Figure 2D ) . RNF20 knockdown did not affect H2AK119ub1 levels . This confirmed that chicken RNF20 is a H2B-specific ubiquitin E3 ligase like its Bre1 orthologs . The reduction of H2B ubiquitination resulted in substantial reductions in H3K4me3 ( 70% ) and H3K79me2 ( 53% ) levels , and a minor reduction in H3K4me2 ( 20% ) ( Figure 2D ) . This demonstrates that chickens also employ the same trans-histone crosstalk pathways observed in yeast and mammals [26] . H3K9acK14ac was slightly reduced ( 7% ) , but the levels of other modifications associated with active or repressive chromatin remained largely unchanged ( Figure 2D ) . To determine whether RNF20 was responsible for all the histone ubiquitination observed at the HS4 and FOLR1 elements , we performed N-ChIP analysis across the FOLR1 and β-globin region before and after RNF20 knockdown in 6C2 cells . Consistent with our observations in primary 10 day embryo tissues , we find that the HS4 insulator and the FOLR1 HSA/HSB elements are substantially enriched in histone ubiquitination in 6C2 cells ( Figure 2E ) . In addition , there is elevated histone ubiquitination across the FOLR1 gene , which is highly active in 6C2 cells , consistent with co-transcriptional deposition ( Figure 2E ) . We observed a substantial depletion of histone ubiquitination at the HS4 insulator and FOLR1 HSA/HSB elements following four days of RNF20 knockdown ( Figure 2E; p-values from student's t-test of difference between WT and RNF20kd are 2e−5 and 0 . 002 , respectively ) . We observe similar profiles of RNF20-dependent H2Bub1 in 6C2 cells ( Figure S2 ) . The histone ubiquitination observed at HS4 and the FOLR1 regulatory elements is therefore RNF20-dependent H2B monoubiquitination . The HS4 insulator is marked by an assemblage of histone modifications and variants typically associated with transcriptionally permissive open chromatin; H3K9acK14ac , H4K5acK8acK12acK16ac , H3K4me2 , H3K4me3 , H4R3me2as , H2A . ZK4acK7acK11ac , and H2BK120ub1 [13]–[14] , [23] and this study ) . This active modification signature is a constant feature of HS4 in a variety of cell types irrespective of local gene expression . A very similar chromatin signature is observed at the FOLR1 HSA/HSB regulatory elements in 6C2 cells . We hypothesize that H2Bub1 may be the keystone for the deposition of the active histone signature at these elements . We therefore performed N-ChIP analysis of active and repressive modifications across the 50 kb β-globin gene neighborhood following short term knockdown of H2B ubiquitination . We found that H3K4me2 and H3K4me3 enrichments at the HS4 insulator element were reduced by 40% and 70% , respectively , following RNF20 knockdown ( 21 . 540 , Figure 3A and 3B ) . This is consistent with trans H2Bub1-H3K4me3 cross-talk occurring at HS4 nucleosomes . The depletion of H3K4me at HS4 is specific as the levels observed at the active FOLR1 gene promoter remain unchanged ( 5 . 613 , Figure 3A and 3B ) . Strikingly , the loss of H2Bub1 also considerably impacts the hyperacetylation of multiple histones at the HS4 insulator , with H3ac , H4ac and H2A . Zac reduced by 55% , 60% and 70% , respectively ( 21 . 540 , Figure 3C , 3D and 3F ) . The depletion of histone acetylation at the HS4 insulator is in contrast to the relatively unchanged levels of histone acetylation in bulk chromatin ( Figure 2D ) . We note that very similar depletions in active modifications are also observed at the FOLR1 gene regulatory elements HSA/HSB . These regulatory elements may harbor functional properties similar to those of the HS4 insulator element . H2A . Z incorporation was mostly unaffected , but there was a 50% reduction in H2A . Z levels specifically at the core of the HS4 insulator ( 21 . 540 , Figure 3E ) . We investigated whether the depletion of H2Bub1 and the resulting loss of the active histone signatures at the HSA/HSB and HS4 elements affected the containment of the intervening condensed region . We determined that H3K9me2 and H3K9me3 are restricted to the condensed region upstream of HS4 in wild type 6C2 cells ( 8 . 9 to 17 . 7 , Figure 3G and 3H , not shown ) . We find that after only four days of H2Bub1 depletion there is marked encroachment of H3K9me2 beyond the HS4 insulator . Significant H3K9me2 spreading into the β-globin locus is observed at all sites from the condensed region to the ρ-globin gene promoter ( Figure 3G ) . Significant H3K9me2 spreading is also observed in the other direction , encompassing the FOLR1 promoter and gene body . No encroachment of H3K9me3 is observed after short term depletion of H2Bub1 , but there is considerable consolidation of this mark at the edges of the condensed region ( Figure 3H ) . The heterochromatin associated mark H4K20me3 is also enriched in the condensed region and at the 3′ end of the FOLR1 gene , but did not spread upon four days of RNF20 knockdown ( Figure S3 ) . Finally , we found that the gene silencing mark H3K27me3 was present at comparably low levels across the condensed region and β-globin locus in 6C2 cells , which did not alter upon RNF20 knockdown ( data not shown ) . In summary , short term depletion of H2Bub1 is sufficient to disrupt H3K4me3 at the HSA/HSB and HS4 elements , which results in a rapid loss of multiple histone acetylation and chromatin boundary integrity . H3K9me2 appears as the first repressive mark to spread beyond the defective boundaries of the condensed heterochromatin region . The comprehensive loss of active histone modifications at the HS4 boundary following RNF20 knockdown may be due to reduced binding of the insulator proteins that recruit histone modifying enzymes . We showed above that the expression of the insulator proteins is unaffected following RNF20 knockdown ( Figure 2C ) . We therefore determined the binding of the insulator factors USF1 , CTCF and VEZF1 to HS4 using crosslinking ChIP analysis before and after the loss of active modifications following RNF20 knockdown . We find that the binding of each factor is unaffected following RNF20 knockdown ( Figure 4A–4C ) . We also discovered that the heterochromatin barrier factors VEZF1 and USF1 are also stably bound at the FOLR1 HSA and HSB elements , which contain binding motifs for both factors ( Figure 4B and 4C ) . The FOLR1 region is not bound by the enhancer blocking and chromatin looping factor CTCF ( Figure 4A ) . In summary , the disruption of insulator protein binding is not responsible for the comprehensive loss of active histone modifications at the HSA/HSB and HS4 elements . We also addressed whether the depletion of H2Bub1 prevented the stable recruitment of histone methyltransferase ( HMT ) complexes that target H3-lysine 4 . Existing models used to explain trans-tail crosstalk between H2Bub1 and H3K4me3 propose that the ubiquitination of H2B either regulates HMT residence by controlling nucleosome stability or creates a binding interface for HMT binding to chromatin ( see discussion ) . We performed crosslinking ChIP analysis for RBBP5 , a structural component of the SET1/COMPASS complex that interacts with USF1 [22] . We find that RBBP5 interacts with the HS4 insulator and the FOLR1 regulatory elements , all of which are sites of H3K4me3 . The binding of RBBP5 to the HS4 or HSA/HSB boundary elements is not significantly affected by RNF20 knockdown ( Figure 4D ) . The loss of H3K4me3 upon H2Bub1 depletion is therefore not due to the decreased residence of the core SET1 complex at HS4 . The ability of the HS4 element to shield genes from chromosomal position effect silencing in a wide variety of systems is well established [38] . This so-called barrier activity can be scored using a well established reporter transgene assay in erythroid cells [17]–[18] , [24] . We used this assay to monitor the expression of a human IL-2R fragment from stably integrated transgenes ( Figure 5A ) using flow cytometry over time in culture . Non-insulated transgenes typically succumb to chromosomal silencing by 40–60 days of culture , whereas transgenes insulated by HS4 elements are able to maintain original levels of expression for 80 days and beyond [18] . We took extensively characterized stable lines that each contain a single copy of the IL-2R transgene flanked by paired 275 bp core HS4 insulators . It was previously determined that transgene expression from these cells remains constant beyond 80 days of culture , the transgenic HS4 insulators are bound by CTCF , USF1 and VEZF1 [21] , and they are enriched in H3ac , H4ac , H3K4me [18] , [24] and H2Bub1 ( Figure 1G ) . We transduced early passage IL-2R transgenic cells with lentiviruses that express RNF20 shRNA . The lentiviral miRNA-shRNA system we employed allowed the stable knockdown of RNF20 for at least sixty days ( validated by Western blotting ) . We observed no change in 6C2 cell morphology and only a minimal reduction in cell doubling during this period ( not shown ) . We found that four days of RNF20 knockdown had no effect on transgene expression ( day 4 , Figure 5B , 5D ) . The depletion of H2Bub1 therefore has little direct effect on the transcription rate of the transgene . However , transgene expression became progressively silenced with continued depletion of H2Bub1 , with the IL-2R expression levels in independent transgenic lines falling by 50–60% after long term depletion ( Figure 5B , 5D ) . This level of silencing is less than that observed when flanking insulators are absent or mutated [18] , but is comparable to that observed in cells transfected with AUSF , a truncated form of USF1 that dominantly inhibits USF1 function [22] . Thus , constant H2B ubiquitination is required for HS4 to act as a stable barrier to chromosomal silencing . It has been postulated that the HSA/HSB regulatory region and the HS4 insulator might form chromatin boundaries that protect the FOLR1 and β-globin genes from the encroachment of the potentially repressive condensed chromatin that separates these loci [15] , [24] . We have therefore studied how long term depletion of H2Bub1 impacts on the containment of heterochromatin associated marks at these loci . We maintained the induction of RNF20 knockdown for forty days , which reduced proteins levels to 9% of wild type , compared to 19% seen after four days of knockdown ( Figure 6A , Figure 2C ) . The prolonged RNF20 knockdown resulted in the depletion of H2BK120ub1 in total chromatin to 13% of wild type levels ( Figure 6B ) . This in turn , resulted in considerable reductions in total H3K4me2 and H3K4me3 , reduced by 78% and 77% , respectively ( Figure 6B ) . Conversely , we observe 43% and 39% increases in the heterochromatin marks H3K9me3 and H4K20me3 in total chromatin ( Figure 6B ) . This is in clear contrast to the unchanged levels of heterochromatin marks after short term knockdown ( Figure 2D ) . Interestingly , the incorporation of the variant histone H2A . Z in total chromatin also increased by 24% after prolonged RNF20 depletion , perhaps to compensate for the gross shift from active to repressive chromatin across the genome ( Figure 6B ) . We performed N-ChIP analyses of histone modifications across the FOLR1 and β-globin loci to determine the effects of long term RNF20 knockdown on chromatin domain integrity . Firstly , we confirmed that H2BK120ub1 was depleted from the HS4 insulator ( Figure 6C ) . The levels of H3K4me2 and H3K4me3 at HS4 were greatly depleted ( by 80% and 65% , respectively ) as a result of the long term depletion of H2BK120ub1 ( Figure 6D , 6E ) . The loss of active modifications at HS4 for a prolonged period results in extensive encroachment of the heterochromatin associated marks H3K9me3 and H4K20me3 , which are normally restricted to the condensed region between the FOLR1 and β-globin loci . Strikingly , H3K9me3 spreads beyond HS4 to encompass the entire 33 kb β-globin locus ( Figure 6F ) . H3K9me3 spreading is likely to have occurred in the majority of cells in the population as the enrichment levels over the β-globin locus are comparable to those in the upstream condensed region . H3K9me3 spreading is also observed in the opposite direction , with significant increases in this mark over the FOLR1 promoter and gene body ( Figure 6F ) . Furthermore , H4K20me3 is also observed to spread from the upstream condensed region to cover the FOLR1 gene in one direction and as far as the ρ-globin promoter in the other ( Figure 6G ) . H3K9me2 , H3K9me3 and H4K20me3 are widely associated with gene silencing and heterochromatin formation . The encroachment of these marks over the FOLR1 and β-globin genes following RNF20 depletion may result in the silencing of their transcription . While the β-globin locus is becoming primed for expression at the CFU-E progenitor stage represented by 6C2 cells , the β-globin genes themselves are not expressed until terminal differentiation [15] , [35] . 6C2 cells cannot be induced to terminally differentiate , so we are unable to study the impact of heterochromatin spreading on the activation of β-globin gene transcription in this system . We therefore focused our attention on the expression of the FOLR1 gene , which is active in 6C2 cells [15] . RT-PCR analysis shows that FOLR1 expression is not affected by four days of RNF20 knockdown ( Figure 7 ) . This is despite the depletion of H2Bub1 , H3K4me2/3 , H3ac , H4ac , H2A . Zac at the HSA/HSB regulatory region ( Figure 3A–3D , 3F ) and the encroachment of H3K9me2 across the FOLR1 promoter and gene body ( Figure 3G ) . Closer inspection shows that H3K4me2/3 and H4ac of the FOLR1 promoter are unaffected following short term RNF20 depletion . FOLR1 gene transcription is therefore not directly dependent upon RNF20 or on maximal active histone modifications at the HSA/HSB elements . However , we find that FOLR1 gene transcription is progressively silenced with prolonged RNF20 knockdown , with 94% repression observed after sixty days of knockdown ( Figure 7 ) . The silencing of FOLR1 coincides with both the loss of H3K4me2/3 at its promoter ( Figure 6D and 6E ) and the accumulation of H3K9me3 and H4K20me3 over its promoter and gene body upon heterochromatin spreading ( Figure 6F and 6G ) . Taken together , these findings demonstrate that the elements HSA/HSB and HS4 form the boundaries of the condensed chromatin region between the FOLR1 and β-globin gene loci . They employ an H2Bub-dependent active chromatin signature that protects these genes from the encroachment of multiple heterochromatin associated marks . The spreading of H3K9me3 and H4K20me3 coincides with the silencing of the FOLR1 gene .
The first high resolution maps of histone modifications across gene loci during vertebrate development revealed that the well characterized chromatin boundary marked by the HS4 insulator is constitutively enriched with histone modifications associated with open chromatin [13]–[14] . Here we show that the HS4 insulator is also constitutively marked by H2BK120 mono-ubiquitination . We show that RNF20-dependent H2Bub1 is required not only for H3K4me2/3 at HS4 , but also for multiple acetylation of H3 , H4 and H2A . Z at this element ( Figure 8A ) . A very similar H2Bub1-dependent active histone signature is also found at the HSA/HSB elements upstream of the FOLR1 gene . To our knowledge , this is the first example of H2Bub1 directing such an extensive cascade of trans histone tail modifications at specific gene regulatory elements . HSA/HSB and HS4 mark the 5′ and 3′ flanks of the condensed chromatin region between the FOLR1 and β-globin loci , which is enriched in the epigenetic hallmarks of heterochromatin ( [13]–[15] , this study ) ( Figure 8B ) . The loss of the active histone modification signature at these elements following the depletion of H2Bub1 in erythroid cells results in the progressive spreading of multiple repressive histone marks across the entire FOLR1 and β-globin loci . These findings clearly demonstrate that the elements HSA/HSB and HS4 form the boundaries of the condensed chromatin region between the FOLR1 and β-globin gene loci . The ability of the HS4 insulator to shield transgenes from chromosomal silencing in a wide variety of systems is well established [38] , but this study provides firm evidence that HS4 functions as a chromatin boundary element in its endogenous context . Both the endogenous and transgenic HS4 elements require continued deposition of H2Bub1 to maintain chromatin boundary integrity and chromosomal position effect protection , respectively . It has been unclear for some years how the FOLR1 gene locus is defended from heterochromatin spreading . An earlier study demonstrated that a 3 . 7 kb region that encompasses the FOLR1 promoter and upstream regulatory elements is capable of directing strong copy number-dependent expression of randomly integrated transgenes in chicken erythroid cells [15] . This fragment contains the major promoter-proximal element HSA and an additional DHS , which we have named HSB ( Figure 1F ) . The elements may harbor locus control region ( LCR ) -like enhancer and/or chromatin boundary activities . Our observations are consistent with the latter . We find that the HSA and HSB elements are bound by the HS4 barrier proteins USF1 and VEZF1 , they recruit RNF20 and the SET1 complex and establish an H2B-dependent active histone modification signature . These molecular features mirror those at the HS4 element . A key different between the HSA/HSB and HS4 boundary elements is the absence of CTCF binding at the FOLR1 boundary . This indicates that CTCF is not required to act a barrier to the spreading of heterochromatin from the condensed region . This is consistent with our previous findings that the CTCF binding site of the HS4 insulator is dispensable for its ability to act as a barrier to chromosomal silencing in different assay contexts [18] , [20] . The modification of histones at chromatin boundaries is conserved across eukaryotes . It is well established that several histone acetyltransferases ( HATs ) are required for heterochromatin boundary integrity in budding yeast [10] , [39]–[40] . Indeed , artificial tethering of HAT chimeras is sufficient to create synthetic barriers to heterochromatin-mediated gene silencing [41] . It has also recently been found that the ILB barrier element at the Drosophila reaper locus also recruits histone acetylation [42] . Our observations that the depletion of multiple histone acetylation marks results in chromatin boundary failure at the chicken FOLR1 and β-globin loci adds further support for a conserved role for active histone modification in chromatin boundary formation . The finding that multiple active histone modifications at the HSA/HSB and HS4 elements are directly or indirectly dependent upon prior H2B ubiquitination is particularly striking . Given the conservation of the factors that mediate H2B ubiquitination and the trans-histone H2Bub1-H3K4me3 pathway , we anticipate that this modification will be employed at boundaries across eukaryotes . The finding that artificial tethering of Lge1 , a factor required for H2B ubiquitination and H3K4/K79 methylation , is sufficient to create a synthetic barrier to heterochromatin-mediated gene silencing in budding yeast supports this view [41] , [43] . A number of budding yeast boundary elements are also associated with regions of nucleosome depletion and elevated histone turnover [40] , [44]–[45] . This may be related to the incorporation of the histone variant H2A . Z , which supports heterochromatin boundary integrity [10] , [46] . However , we did not observe any extensive depletion in histone density at the chicken HSA/HSB or HS4 chromatin boundaries ( not shown ) . Furthermore , we found that the incorporation of H2A . Z at these boundaries remains intact following RNF20 knockdown and the loss of active modifications . Further studies are required to determine the role of H2A . Z at these elements , but it is clear that H2A . Z incorporation is not sufficient to prevent the spread of heterochromatin into the FOLR1 and β-globin loci . We have shown that the trans histone modification pathway from H2Bub1 to H3K4me3 reported in yeast and man is also conserved in chicken . How the mono-ubiquitination of H2B facilitates H3K4me3 has been subject to intense study over the last few years . Three models have arisen to explain this pathway . The ‘wedge’ model postulated that the bulky ubiquitin moiety would increase the access of H3K4 methyltransferases by non-specifically disrupting chromatin fiber packing in some way [47]–[48] . This simple mechanism appears improbable as substitution of ubiquitin with the bulkier SUMO moiety at the equivalent residue of H2B does not recapitulate H2Bub1-directed trans tail crosstalk in S . cerevisiae [49] . In contrast , a ‘stability’ model was recently put forward in response to findings in S . cerevisiae that H2Bub1 promotes nucleosome reassembly following RNA polymerase II transcription and enhances global nucleosome stability [49]–[51] . It is proposed that H2Bub1 may restrict the eviction of the H2A/H2B dimer from nucleosomes , thereby increasing the nucleosomal residence of the SET1/COMPASS methyltransferase complex which interacts with basic and acidic patches on H2A and H2B , respectively [52]–[53] . In this study , we found that the interaction of RBBP5 ( SWD1 ) , a core component of the SET1 complex remains bound at the HS4 insulator following the depletion of H2Bub1 . The loss of H3K4me2/3 cannot be explained by the decreased residence of SET1 complexes . Recent studies provide compelling evidence that H2Bub1 acts as ‘bridge’ to facilitate H3K4me3 . The core SET1 complex can interact with chromatin and mediate H3K4 mono-methylation in the absence of H2B ubiquitination [54] . However , it has been found that the accessory COMPASS subunit Cps35/Swd2 in yeast ( WDR82 in humans ) interacts with H2Bub1 and activates the processive H3K4 methyltransferase activity of the SET1 complex [55]–[56] . While the composition of SET1 complexes in chickens remains to be determined , our data are consistent with a mechanism of H2Bub1-directed activation of pre-loaded SET1 complexes to facilitate processive H3K4 methylation . The loss of H3K4me2/3 upon the depletion of H2Bub1 is likely to be the primary reason for the subsequent losses of multiple histone acetylation at the HS4 insulator . Methylated H3K4 is a pivotal recognition site required by multiple histone acetyltransferase complexes [57]–[60] . H3K4me3 also facilitates the recruitment of the NURF chromatin remodelling complex via its BPTF subunit [22] , [61] . The mono-ubiquitination of H2B is broadly recognized as a mark of transcriptional activity [30] . H2Bub1 is enriched in the bodies of expressed genes throughout yeast and mammalian genomes [62]–[63] , and the bulk of H2Bub1 requires many factors involved in the early steps of transcription elongation [30] . While the HS4 insulator has the epigenetic chromatin signature of a housekeeping promoter , it lacks either promoter or enhancer activity [64] . In addition , HS4 is not bound by RNA polymerase II ( Figure S4 ) and is not a source of transcripts [22] , [65] . It therefore appears most likely that HS4 recruits H2Bub1 through a process that is not linked to transcription . We found that the recruitment of H2Bub1 to HS4 is dependent upon the USF1/USF2 binding site . While we have been unable to detect RNF20 in stable complexes with USF1 or USF2 ( data not shown ) [22] , this is reminiscent of activator-dependent recruitment of Bre1/RNF20 to yeast and human promoters [66] . In addition to the recruitment of the E3 ubiquitin ligase RNF20 , the HSA/HSB and HS4 boundary elements also require sufficient activity levels of the E2 conjugase RAD6 to enable sufficient levels of H2Bub1 for chromatin boundary stability . There are two broad mechanisms that could result in the persistent H2B ubiquitination of the HS4 insulator . Firstly , the HSA/HSB and HS4 elements might not be subject to the rapid turnover of H2Bub1 associated with promoter clearance and transcription elongation [66]–[67] . Such a scenario would negate the need for the co-transcriptional stimulation of RAD6 conjugase activity [68] , as low efficiency H2B ubiquitination may be sufficient for high steady state levels of H2Bub1 at HS4 . Alternatively , HS4 may recruit factors that mediate RAD6 phosphorylation in the absence of RNA polymerase to stimulate efficient H2B ubiquitination of this element . Depletion of H2Bub1 disrupts the assembly of the active histone modification signatures at the HSA/HSB and HS4 boundary elements . This results in progressive spreading of heterochromatin-associated histone marks into the FOLR1 and β-globin loci either side of the condensed region . We find that the heterochromatin-associated marks H3K9me2 , H3K9me3 and H4K20me3 are propagated in a continuous manner from the upstream condensed region into the FOLR1 and β-globin loci . The heterochromatin domain expands from a ∼10 kb domain of the condensed region to cover the entire ∼50 kb FOLR1 and β-globin region given time ( Figure 8B ) . Intriguingly , each of the three repressive marks at the β-globin locus spreads in a different temporal manner , suggesting that different enzyme complexes are involved in propagating these marks . The first repressive mark to spread is H3K9me2 , which propagates over the entire FOLR1 locus and extends 14 kb into the β-globin locus after only four days of H2Bub1 depletion ( Figure 8B ) . Conversely , H3K9me3 and H4K20me3 do not extend beyond the upstream condensed region at this early stage , but H3K9me3 appears to consolidate at the borders of the condensed region . However , both H3K9me3 and H4K20me3 spread into the FOLR1 and β-globin loci upon longer periods of H2Bub1 depletion . H3K9me3 uniformly spreads to encompass the entire ∼50 kb FOLR1 and β-globin region , while H4K20me3 spreads into a ∼ 30 kb region covering the entire FOLR1 locus to the rho gene promoter ( Figure 8B ) . Several mechanisms have been proposed to explain the spreading of repressive chromatin [69] . Given that the de novo repressive marks in the FOLR1 and β-globin loci manifest as continuous domains with consistent modification levels throughout , we speculate that the marks are propagated via linear cis-spreading mechanisms . The simplest way to rationalize all our findings is that the spreading occurs using a classical stepwise assembly mechanism , where sequential iterations of repressor protein binding and methyltransferase recruitment propagate the repressive methyl mark onto neighboring nucleosomes . The ability of HP1 adaptor proteins to recognize H3K9me3 , interact with H3K9 and H4K20 methyltransferases and spread from sites of recruitment is a potential example of the self-reinforcing repressor interactions that may occur at the FOLR1 and β-globin loci [70]–[72] . Stepwise assembly mechanisms are consistent with the observed sequential pathway of repressive chromatin modification . It is possible that the propagation of H4K20me3 might require prior H3K9me3 , which requires prior H3K9me2 at this locus . While further investigations will be required to define the exact pathway of repressive mark assembly , it is clear that HS4 acts a chain terminator to heterochromatin spreading by using a panel of active histone modifications , which collaborate to block and inhibit repressive histone methylation . There may be a role for RNA-directed heterochromatin assembly at the FOLR1 and β-globin loci . It was recently shown that the maintenance of heterochromatin region's condensed conformation requires RNAi factors [65] . This suggests conservation with mechanisms employed in fission yeast where RNAi factors work together with heterochromatin proteins including the HP1 homolog Swi6 to mediate heterochromatin establishment [8] . However , RNAi factors are dispensable for the maintenance of heterochromatin [73] . It remains to be investigated whether RNA and RNAi factors play a role in heterochromatin spreading in higher eukaryotes . It is also conceivable that the continuous spreading of heterochromatin is dictated by the three dimensional organization of the FOLR1 and β-globin loci . If these gene loci are insulated from the upstream condensed region by positioning into different nuclear compartments , disruption of the active signature at the HSA/HSB and HS4 boundaries may result in the transfer of most or all the FOLR1 and β-globin loci into a repressive compartment . Such a scenario appears complex , however , as it would require the sequential transfer of these loci into different compartments rich in H3K9me2 , H3K9me3 and H4K20me3 methyltransferases . There is a paucity of information about the elements that form chromatin boundaries in vertebrates . Our finding that the HSA/HSB and HS4 boundary elements employ H2B ubiquitination to direct a cascade of active histone modifications suggests that genomic profiling of chromatin signatures will be a useful approach to identifying boundary elements . We note that there was a gross increase in total heterochromatin marks results when H2Bub1 is depleted for long periods . This observation suggests that many loci employ H2Bub1-dependent boundaries to heterochromatin spreading . It will be interesting to see whether other chromatin boundaries in vertebrate genomes also require such a large complement of active marks or employ a more restricted palette to deal with locus-specific threats .
Antibodies against H3K4me2 ( 07-030 ) , H3K4me3 ( 05-745R ) , H3K9me3 ( 07-523 ) , H3K27me3 ( 07-449 ) , H3 ( 07-690 ) , H3K9acK14ac ( 06-599 ) , H4K5acK8acK12acK16ac ( 06-598 ) , H2AK119ub ( 05-678 ) , H2A . Z ( 07-594 ) and CTCF ( 06-917 ) were obtained from Millipore . Antibodies against H3K9me2 ( ab1220 ) , H3K79me2 ( ab3594 ) , H3K79me3 ( ab2621 ) , H2A . ZK4acK7acK11ac ( ab18262 ) , PAF1 ( ab20662 ) , RPB1 ( ab5408 ) and TBP ( ab51841 ) were obtained from Abcam . Antibodies against H4K20me3 were a kind gift from Judd Rice [74] . Antibodies against ubiquitin ( sc-8017 ) , ( BML-PW8805 ) and USF1 ( H00007391-A01 ) were obtained from Santa Cruz , Enzo Life Sciences and Abnova , respectively . Antibodies against RNF20 ( A300-715A ) and RBBP5 ( A300-109A ) were obtained from Bethyl Laboratories . Anti-H2BK120ub1 antibodies were initially a kind gift from Moshe Oren [62] then purchased from Médimabs ( MM-0029 ) or Millipore ( 17-650 ) . Anti-VEZF1 antibodies were raised as described [21] . PE conjugated anti-CD25 ( IL-2R ) was obtained from Dako . Chicken 6C2 erythroleukaemia cells were grown in αMEM supplemented with 10% FCS , 2% chicken serum , 1 mM HEPES , 25 µM β-mercaptoethanol and 1% Penicillin/Streptomycin solution . Crosslinking chromatin immunoprecipitation was performed as described previously [Litt et al , 2001] . Briefly , 6C2 cells ( 2×107 cells/ml ) were crosslinked in fresh growth medium with 1% formaldehyde at room temperature for 20 minutes ( RNF20 , PAF1 and RBBP5 ) , 10 minutes ( CTCF , USF1 and VEZF1 ) or 2 minutes ( H2Bub ) . Reactions were quenched by adding glycine to a final concentration 0 . 125 M . The crosslinked cells were washed by PBS twice and then lysed ( 0 . 25% Triton X-100 , 10 mM EDTA , 0 . 5 mM EGTA and 10 mM Tris pH 8 ) . Cell nuclei collected by centrifugation were washed ( 0 . 2 M NaCl , 1 mM EDTA , 0 . 5 mM EGTA and 10 mM Tris pH 8 ) followed by chromatin solubilization ( 0 . 5% SDS , 10 mM EDTA and 50 mM Tris pH 8 ) . Chromatin was fragmented by sonication ( Misonix ) for a total time of 10 minutes in regular 10 second pulses . Insoluble material was removed by centrifugation at 15 , 000 g for 10 minutes at 4°C . Sizes of chromatin fragments were ∼500 bp on average . Soluble chromatin was diluted by X-ChIP buffer ( 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 167 mM NaCl , 0 . 01% SDS and 16 . 7 mM Tris pH8 ) to obtain chromatin from 1×107 cells per ml . 1 ml of chromatin was pre-cleared with 5 µg of non-immune IgG and 100 µl ( 50% slurry in X-ChIP ) of protein A/G agarose at 4°C for 3 hours . 10 µg of specific antibody was incubated with pre-cleared chromatin at 4°C with agitation overnight . Binding of protein A/G agarose was carried out at 4°C for 2 hours . The agarose was washed extensively with buffer 1 ( 1% Triton X-100 , 0 . 1% SDS , 2 mM EDTA , 150 mM NaCl and 20 mM Tris pH 8 ) , buffer 2 ( 1% Triton X-100 , 0 . 1% SDS , 2 mM EDTA , 500 mM NaCl and 20 mM Tris pH 8 ) , buffer 3 ( 0 . 25 M LiCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 1 mM EDTA , 10 mM Tris pH 8 ) and twice with TE buffer ( 10 mM Tris pH 8 , 1 mM EDTA ) . The bound chromatin was eluted into elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) , crosslinks reversed and protein digested . DNA was extracted by phenol/chloroform and ethanol precipitated in the presence of 10 µg of glycogen for quantitative PCR ( qPCR ) analysis . Circulating red blood cells and whole brain tissue ( without major vasculature ) were collected from 10 day fertilized chick embryos kindly provided by Aviagen , Ltd . Native nucleosomes were prepared in low salt conditions to ensure retention of all nucleosomes , as described [34] . In brief , cells were collected in the presence of inhibitors ( 25 µg/ml AEBSF , 0 . 5 µg/ml Leupeptin and 0 . 7 µg/ml Pepstatin , 10 mM N-ethylmaleimide and 10 mM sodium butyrate ) and nuclei were isolated by lysis buffer ( 10 mM NaCl , 3 mM MgCl2 , 0 . 4% NP-40 and 10 mM Tris pH 7 . 5 ) for MNase ( Sigma ) digestion in the presence of 1 mM CaCl2 . The MNase concentration ( X ) required to yield mostly di- and tri-nucleosomes was firstly determined . For ChIP experiments , three equal aliquots of nuclei were incubated with ½X , 1X and 2X MNase at 37°C for 17 minutes to obtain representative di- and tri-nucleosomes [14] . Digestion was stopped with 10 mM EDTA . Soluble chromatin was collected by centrifugation at 2 , 500 g for 5 minutes . The three supernatants were combined ( S1 ) . The remaining pellets were combined and resuspended in lysis buffer supplemented with 10 mM EDTA and left on ice for 15 minutes . Chromatin was released by passing through 20 then 25 gauge needles , and collected by centrifugation at 10 , 000 g for 10 minutes . The supernatant ( S2 ) was combined with S1 for sucrose gradient fractionation . ∼1 . 5 mg of S1–S2 chromatin was fractionated on 13 . 5 ml 5∼25% linear sucrose gradients ( Biocomp gradient master ) in a SW40Ti rotor at 31 , 000 rpm for 14 hours at 4°C . 1 ml fractions were collected and 10 µl aliquots were extracted for checking DNA fragment sizes . Fractions containing di- and tri-nucleosomes were pooled and fixed with 0 . 1% formaldehyde at room temperature for 10 minutes . The crosslinking reaction was stopped with 0 . 125 M glycine . Nucleosomes were exchanged into N-ChIP buffer ( 50 mM NaCl , 5 mM EDTA , 10 mM Tris pH 7 . 5 ) buffer using P-6DG Bio-Gel ( BioRad ) . 50 µg of nucleosomes were pre-cleared with 5 µg of non-immune IgG and 100 µl ( 50% slurry in N-ChIP buffer ) of protein A/G agarose at 4°C for 3 hours . 10 µg of specific antibody was incubated with pre-cleared chromatin at 4°C with agitation overnight . Binding of protein A/G agarose was carried out at 4°C for 2 hours . Immunoprecipitated chromatin was collected and washed 5 times with 1 ml N-ChIP wash buffer ( 150 mM NaCl , 0 . 2 mM EDTA , 0 . 1% Tween-20 and 20 mM Tris pH 7 . 4 ) . Chromatin was eluted with N-ChIP buffer supplemented with 1% SDS followed by 0 . 5% SDS . Eluates were digested with Proteinase K at 45°C for 2 hours and DNA extracted by phenol/chloroform and precipitated for qPCR analysis . Relative DNA enrichments were quantified in triplicate by TaqMan real-time PCR on a Roche 480 Lightcycler . The primers used in this study were described previously [14] , [21] . The comparative Ct method ( with correction for primer efficiencies ) was used to calculate fold enrichments and their standard deviations , as described previously [24] . Two sample equal variance Student's t-tests using a two-tailed distribution were applied to ChIP enrichment values to assess the significance of enrichments over controls , or changes following RNF20 knockdown . The calculated p-value ranges for enriched sites are indicated in the figure legends . The pSLIK micro RNA-based lentiviral expression system was used to mediate long term conditional knockdown of RNF20 in chicken cells [75] . Gene-specific shRNAs are embedded into the primary transcript of human miR30 , which is located in the 3′UTR of a doxycycline-regulated GFP transgene . The psm2 shRNA design tool was used to identify 20 potential shRNA targets ( http://hannonlab . cshl . edu ) . These were scored and four targets were cloned into pEN_hUmiRc2 , packaged into lentivirus particles and tested for performance as previously described [21] . GgRNF20-2628 , which targets CAGAGTAACTAGAGAGAAA , was the most potent miR-shRNA and was used throughout this study . Wild type or 8103 ( containing a single copy HS4 flanked IL-2R reporter transgene , [24] ) 6C2 cells were transduced with GgRNF20-2628 lentiviral particles and cloned following flow sorting or serial dilution . GFP-miRNA expression was induced with 2 µg/ml doxycycline . GFP expression was monitored by FACS analysis to confirm expression of the miRNA cassette and RNF20 protein levels were monitored by Western blotting during prolonged knockdown time courses . Nuclear extracts were prepared from cells harvested and washed with PBS and then lysed with hypotonic buffer ( 0 . 2% NP-40 , 0 . 1 mM EDTA and 20 mM HEPES pH 8 ) . Cell nuclei were collected by centrifugation at 2 , 500 g for 5 minutes . Nuclear proteins were extracted by incubation in high salt buffer ( 0 . 2% NP-40 , 0 . 4 M NaCl , 13 . 3% glycerol and 20 mM HEPES pH 8 ) at 4°C for 30 minutes . Insoluble debris was removed by centrifugation at 16 , 000 g for 10 minutes at 4°C . Soluble nuclear extract was quantified by Bradford assay ( Bio-Rad ) . 25 µg of nuclear extract or 7 µg of native S1–S2 nucleosome preparations were used for separation by SDS-PAGE . Proteins were then transferred to a PVDF membrane and imaged with HRP-conjugated secondary antibodies on a LAS-3000 imager ( Fujifilm ) . Band intensities corrected for background were quantified using AIDA software . 106 cells were harvested by centrifugation at 1 , 000 g for 5 minutes . Cells were washed twice in 1 ml of Hank's buffered saline solution ( Sigma ) supplemented with 0 . 1% BSA and 0 . 1% NaN3 ( HBSS+ ) . Cells were resuspended in 100 µl of HBSS+ and incubated with 10 µl of anti-CD25-PE ( Dako ) antibody in the dark at 4°C for 30 minutes . Excess antibody was removed by washing twice with 1 ml of HBSS+ . After the last wash , cells were resuspended in 500 µl of HBSS+ for FACS analysis . Flow cytometry was performed on a FACSCalibur flow cytometer ( BD Biosciences ) using CELLQuest software . RNF20 knockdown cells express GFP upon induction with doxycycline . Color compensation was used to correct for GFP fluorescence in the FL2 ( 585 nm ) filter; it was set as FL1 – 1% FL2 and FL2–20% FL1 . Data was acquired for 10 , 000 viable or GFP-expressing cells ( FL1 = 530 nm ) . Histograms were generated using FlowJo software ( Tree Star , Inc ) . Mean IL-2R fluorescence intensities of RNF20 knockdown cells were determined and normalized to those of parental reporter transgene cells with wild type RNF20 levels . Total RNA was isolated from 6C2 cells using TRI reagent ( Sigma ) and cDNA prepared using SuperScript III ( Invitrogen ) and random hexamers . The following primers were used SYBR green real time PCR assays: 5′ TGCTGCGCTCGTTGTTGA 5′ CATCGTCCCCGGCGA 5′ ATGCGTCATCTCATCAGCAG 5′ TTGGGAAGAAGGGTCATCAG 5′ GATTCTGCATGTGCCACTGT 5′ AAGACCTGGGTGAAGGGTCT | The transcription of genes in eukaryotes occurs within the context of chromatin , a complex of DNA , histone proteins , and regulatory factors . Whole-genome profiling of chromatin proteins and histones that are post-translationally modified has revealed that genomes are organized into domains of distinct chromatin states that coordinate gene regulation . The integrity of chromatin domains can require the setting of their boundaries . DNA sequences known as chromatin insulator or boundary elements can establish boundaries between transcriptionally permissive and repressive chromatin domains . We have studied two chromatin boundary elements that flank a condensed chromatin region located between the chicken FOLR1 and β-globin genes , respectively . These elements recruit enzymes that mediate the ubiquitination of histone H2B . Histone H2B ubiquitination directs a cascade of so-called “active” histone modification events that favor chromatin accessibility . We observe a striking collapse of the active histone modification signature at both chromatin boundaries following the depletion of ubiquitinated H2B . This loss of boundary function leads to the comprehensive spreading of repressive chromatin over the entire FOLR1 and β-globin gene region , resulting in gene silencing . We propose that chromatin boundaries at many gene loci employ H2B ubiquitination to restrict the encroachment of repressive chromatin . | [
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] | 2011 | Histone Crosstalk Directed by H2B Ubiquitination Is Required for Chromatin Boundary Integrity |
The chemotherapeutic arsenal against human African trypanosomiasis , sleeping sickness , is limited and can cause severe , often fatal , side effects . One of the classic and most widely used drugs is pentamidine , an aromatic diamidine compound introduced in the 1940s . Recently , a genome-wide loss-of-function screen and a subsequently generated trypanosome knockout strain revealed a specific aquaglyceroporin , TbAQP2 , to be required for high-affinity uptake of pentamidine . Yet , the underlying mechanism remained unclear . Here , we show that TbAQP2 is not a direct transporter for the di-basic , positively charged pentamidine . Even though one of the two common cation filters of aquaglyceroporins , i . e . the aromatic/arginine selectivity filter , is unconventional in TbAQP2 , positively charged compounds are still excluded from passing the channel . We found , instead , that the unique selectivity filter layout renders pentamidine a nanomolar inhibitor of TbAQP2 glycerol permeability . Full , non-covalent inhibition of an aqua ( glycero ) porin in the nanomolar range has not been achieved before . The remarkable affinity derives from an electrostatic interaction with Asp265 and shielding from water as shown by structure-function evaluation and point mutation of Asp265 . Exchange of the preceding Leu264 to arginine abolished pentamidine-binding and parasites expressing this mutant were pentamidine-resistant . Our results indicate that TbAQP2 is a high-affinity receptor for pentamidine . Taken together with localization of TbAQP2 in the flagellar pocket of bloodstream trypanosomes , we propose that pentamidine uptake is by endocytosis .
A specific aquaglyceroporin , TbAQP2 , is required for high-affinity uptake of pentamidine into human African trypanosomiasis parasites , Trypanosoma brucei [1–3] . Additionally , a trypanosome adenosine transporter mediates pentamidine transport , albeit at lower efficiency [2] . Aquaglyceroporins represent a subfamily of the aquaporin water channel proteins and conduct small , uncharged solutes , mainly glycerol and urea [4] . However , at a molecular weight of 340 Da and with two strongly basic , positively charged amidine moieties ( pKa 12 . 1 ) , pentamidine is not a physiological substrate analog and differs from previous examples of drug uptake involving aquaglyceroporins [5 , 6] . In the treatment of acute promyelocytic leukemia , the human aquaglyceroporin AQP9 [7] serves as an entry site for the drug arsenic trioxide , As2O3 [8] , which dissolves into weak arsenous acid , As ( OH ) 3 ( 126 Da ) , resembling the glycerol molecule ( 92 Da ) . The analogous antimonous acid , Sb ( OH ) 3 ( 173 Da ) , derived from the antimonial drug pentostam , is the first-line treatment of leishmaniasis and enters Leishmania major parasites via LmAQP1 [9] . The antineoplastic agent hydroxyurea ( N-hydroxylated urea , 76 Da ) is a permeant of PfAQP , i . e . the single aquaglyceroporin of the malaria parasite Plasmodium falciparum , and of TgAQP from the toxoplasmosis parasite Toxoplasma gondii [10] . Trypanosomes express three aquaglyceroporins , TbAQP1-3 , with permeability , among others , for glycerol , urea [11] , arsenous and antimonous acid [12] . The physiological roles of TbAQP2-3 are unclear because the phenotype of knockout parasites is inconspicuous [2] . The physicochemical properties of pentamidine seem to be generally incompatible with a direct passage via AQPs , because two conserved filter sites select against positive charge and too large size . The selectivity filter or aromatic/arginine ( ar/R ) constriction is typically composed of an arginine residue in an aromatic environment ( Fig 1A , right ) and excludes solutes with diameters larger than 3–4 Å as well as protons [13–15] . The central Asn-Pro-Ala ( NPA ) filter is formed by the NPA-capped ends of two α-helices that emanate a positive electrostatic field and repel cations , such as sodium , potassium , and ammonium , NH4+ [14 , 16] . TbAQP2 contains a conserved ‘NPA/NPA’ cation filter with the crucial asparagines [16] intact and minor amino acid exchanges in the second and third position ( NSA/NPS ) [2 , 11] . In contrast , the ar/R constriction is virtually absent . Here , arginine is replaced by a neutral Leu264 and the common aromatic positions carry aliphatic residues ( Ala88 , Ile110 , Val249 , Leu258; Fig 1A ) [2 , 11] . In this study , we asked whether pentamidine is a direct permeant of TbAQP2 and analyzed its interaction with the protein . We found that TbAQP2 is impermeable to even small cations , such as NH4+ or formamidine , and consistent with this , failed to increase the susceptibility of yeast cells to pentamidine . Strikingly , pentamidine bound avidly to the selectivity filter and fully inhibited TbAQP2 with an IC50 of 130 nM . This affinity is unprecedented in the on-going search for AQP inhibitors [17] and is derived from a unique interaction between one of the amidine moieties of pentamidine and Asp265 in conjunction with the displacement of water molecules from the channel interior . A single TbAQP2 point mutation , Leu264Arg , reversed pentamidine binding and rendered trypanosomes resistant . The localization of TbAQP2 in the flagellar pocket [2] , characterized by high rates of membrane turnover , leads us to propose that pentamidine uptake occurs by endocytosis with TbAQP2 acting not as a transporter but a high-affinity pentamidine receptor . Such a mechanism could be exploited as a novel route to shuttle small molecule drugs into trypanosomes in a similar fashion as discussed for macromolecular transferrin protein-conjugated compounds in connection with the trypanosomal transferrin receptor [18 , 19] .
To approach the question whether TbAQP2 is directly permeable for pentamidine , we set up a yeast expression system and initially analyzed permeability for small neutral and positively charged compounds . TbAQP3 carrying a conserved ar/R constriction ( Fig 1A ) served as a control . TbAQP2 and TbAQP3 were well expressed ( Fig 1B ) and functional as shown below . First , we carried out phenotypic growth assays based on a yeast strain that lacks all three endogenous ammonium transporters ( Δmep1-3 ) [13 , 20] . This strain does not grow on acidic media with ammonium as the sole nitrogen source ( Fig 1C , pH 5 . 5 , second lane ) . The growth phenotype is pH-dependent due to the chemical equilibrium of NH3/NH4+ ( pKa of 9 . 2 ) and growth is permitted towards more alkaline pH by increasing transmembrane diffusion of neutral NH3 . Permeability of transport proteins for charged NH4+ is indicated by growth at acidic pH , as seen with the tomato ammonium transporter 1;1 ( LeAMT1;1 ) as a positive control ( Fig 1C , top lane ) . In this assay , neither expression of TbAQP2 nor TbAQP3 enhanced the NH4+ uptake over that of cells without an ammonium transporter ( Fig 1C ) . A complementary outcome of the transport assay can be obtained by using yeast-cytotoxic methylammonium , H3C-NH3+ [13] . Here , yeast growth will cease when the neutral methylamine form , H3C-NH2 , diffuses across the cell membrane ( Fig 1D , pH 6 . 5 ) or when the protonated , charged form is transported ( Fig 1D , pH 5 . 5 , top lane ) . This assay is particularly sensitive due to exposure of the cells to the toxic compound for several days [13] . Again , TbAQP2 and TbAQP3 appeared impermeable for the positively charged compound form and survived the treatment ( Fig 1D ) . Pentamidine itself is cytotoxic to yeast when the cells are grown on a non-fermentable carbon source , such as glycerol [21] . Again , expression of TbAQP2 did not increase the susceptibility in comparison to non-expressing and TbAQP3 expressing cells ( Fig 1E ) . Next , we employed a biophysical light-scattering assay for TbAQP2 permeability [22] . We expressed TbAQP2 in a yeast strain that lacks the endogenous aquaglyceroporin ( Δfps1 ) , prepared protoplasts , and challenged them by a hypertonic , test solute-containing buffer . In an initial rapid phase , the protoplasts will release water following the outward osmotic gradient and shrink as indicated by an increase in light scattering . In the second slower phase , volume will be regained if the test solute can pass TbAQP2 and enter the protoplasts . Pentamidine is not testable in this setup because its low solubility prohibits the establishment of a sufficiently concentrated gradient . We used instead two pairs of test solutes , glycerol/3-aminopropane-1 , 2-diol and urea/formamidine , of which glycerol and urea are established permeants of TbAQP2 [11] , whereas the respective partners are bioisosteric nitrogen-derivatives , which are positively charged at neutral pH ( Fig 1F ) . 3-Aminopropane-1 , 2-diol has a pKa of 9 . 2; hence , at a buffer pH of 9 . 2 , which yeast will tolerate for the assay time , 50% of the compound will be deprotonated and neutral . Formamidine is too strong a base ( pKa 13 ) to be amenable to deprotonation in aqueous solution . In the assay , TbAQP2 showed permeability for glycerol and urea ( Fig 1F , red ) , whereas the positively charged compounds did not pass ( Fig 1F , dark blue ) as seen by traces equal to control protoplasts without TbAQP2 ( Fig 1F , black ) . The lack of permeability for the charged compounds is not due to molecule size , because formamidine is even smaller than urea . Indeed , 3-aminopropane-1 , 2-diol , when partially neutralized by a pH shift to 9 . 2 , passed TbAQP2 at two thirds the rate of glycerol ( Fig 1F , light blue ) . We conclude that organic cations fail to traverse the TbAQP2 channel . Formally , formamidine represents one of the amidine moieties of pentamidine . We reasoned that if formamidine ( 44 Da ) is excluded from passing TbAQP2 , then the much larger pentamidine would be excluded as well . The lack of an increase in susceptibility of TbAQP2 expressing yeast to pentamidine further argues against pentamidine transport via TbAQP2 . Consequently , we conducted another set of light scattering assays to determine how pentamidine interacts with TbAQP2 . We switched from hypertonic assay conditions to an isotonic glycerol replacement of 300 mM saccharose from the buffer to omit osmotic water permeability effects and to obtain monophasic solute-uptake traces , which allow for a more accurate quantification especially at low rates [23] . The red traces in Fig 2A show unhindered glycerol influx via TbAQP2 and TbAQP3 . Addition of up to 500 μM pentamidine 10 min prior to the assay left the glycerol permeability of TbAQP3 unaffected , whereas TbAQP2 was fully blocked already at 50 μM ( Fig 2A ) . The inhibition of TbAQP2 by pentamidine was dose-dependent and substantial even in the nanomolar concentration range . We translated the relative glycerol permeability rates into a dose-response curve yielding an IC50 of 130 nM ( Fig 2B ) . Pentamidine was effective immediately after addition to the protoplasts indicating that the binding site is readily accessible from the extracellular space . Since high-affinity inhibition of other AQPs is only achieved by covalent cysteine-modification by metalorganic compounds , e . g . mercurials or gold-containing auphen ( IC50 0 . 8 μM ) [17 , 24] , we set out to investigate the binding mode of pentamidine by determining structure-function relationships and by point mutation of TbAQP2 . We synthesized asymmetric variants of pentamidine by replacing one of the positively charged amidine moieties by nitrile , and by shortening the molecule via 4-pentoxy- , 4-propoxy- to 4-methoxy-benzamidine ( Fig 2B ) . The nitrile compound was as effective as pentamidine in blocking TbAQP2 glycerol permeability ( IC50 180 nM ) showing that one positive site is sufficient for full inhibition , most likely by electrostatical interaction with an acidic residue of TbAQP2 . Removal of one benzamidine group ( 4-pentoxy-benzamidine ) led to a shift in IC50 to 87 μM ( Fig 2B ) . Further shortening of the molecule to 4-propoxy-benzamidine resulted in an IC50 of 2 . 8 mM and a maximal inhibition of 60% . The methoxy compound was incapable of inhibiting TbAQP2 . Since electrostatic interactions exhibit highest affinity when efficiently shielded from water , our structure-function evaluation suggests that the amidine interaction site is located 7–8 Å , i . e . the length of the pentoxy chain , away from the solvent into the channel . This distance coincides with the position of the ar/R selectivity filter region . TbAQP2 lacks the conserved arginine in the ar/R filter possibly providing access of the acidic Asp265 to the channel surface ( Fig 1A ) . If the Asp265 sidechain carboxylate ( pKa 3 . 7 in water ) interacts with pentamidine , the affinity should be titratable by pH . Hence , we determined inhibition of TbAQP2 by pentamidine at pH 3 . 5 and 2 . 5 and observed 3 times ( 450 nM ) and 8 times ( 1 . 1 μM ) reduced affinity , respectively ( Fig 2C , left ) . To test whether the pH titration is specific to Asp265 , we changed the residue by point mutation to alanine , which equally led to a shift in IC50 to 4 . 5 μM ( Fig 2C , right ) . Further , this mutant was pH-independent with regard to pentamidine binding ( IC50 5 . 8 μM at pH 2 . 5; Fig 2C , right ) confirming the identity of the interaction site . Together , the structure-function evaluation and the mutation data indicate that the exceptional affinity of pentamidine to TbAQP2 is derived from an electrostatic interaction of one amidine moiety with Asp265 and shielding from the solvent by displacement of water molecules from the channel by the pentoxy chain . A selectivity filter arginine followed by an aspartate at the next sequence position is a signature motif of aquaglyceroporins [25] . At about 4 . 5 Å distance in the protein , both charged residues are positioned for an electrostatic interaction , see for instance TbAQP3 Arg256/Asp257 in Fig 1A [26] . Besides TbAQP2 , there are other examples of ar/R selectivity filters in which the arginine is replaced by an aliphatic residue , e . g . the T . gondii TgAQP or the AQP subfamily of plant tonoplast instrinsic proteins TIP2;1 [10] . However , in all such cases , replacement of the arginine goes together with an exchange of the neighboring aspartate for a neutral residue [27] . TbAQP2 is , thus , unique in having lost the arginine but having kept Asp265 . We concluded that a point mutation of TbAQP2 Leu264 to arginine should re-establish the internal salt bridge with Asp265 and prevent pentamidine from binding . Expression in yeast of TbAQP2 Leu264Arg yielded a functional water and glycerol channel with about half the permeability of the wild-type protein ( Fig 3A ) . Importantly , pentamidine failed to inhibit TbAQP2 Leu264Arg ( Fig 3A ) and this further supported the binding mode suggested above . We next investigated the findings from the heterologous yeast expression system in T . brucei parasites by expressing GFP-tagged TbAQP2 Leu264Arg ( GFPAQP2L264R ) or wild-type TbAQP2 ( GFPAQP2WT ) in a T . brucei aqp2-null background ( Fig 3B ) . Despite the mutation , TbAQP2 Leu264Arg localized to the flagellar pocket as reported previously for wild-type TbAQP2 [2] ( Fig 3C ) . While aqp2-null T . brucei expressing wild-type TbAQP2 were highly sensitive to pentamidine ( EC50 = 0 . 65 nM ) , T . brucei expressing mutant TbAQP2 remained resistant to pentamidine ( EC50 = 103 nM; Fig 3D ) . Thus , TbAQP2 Leu264Arg fails to bind pentamidine and fails to sensitize trypanosomes to pentamidine .
Maintenance of cation homeostasis is crucial for cell functionality and viability [14] . Accordingly , cation channels and transporters are highly regulated and/or selective , whereas non-cation conducting membrane proteins contain exclusion filters . The typical AQP channel layout exhibits two cation filters , which select against both types of cation conductance: a ) hydrogen-bonded proton-wires are disrupted in the ar/R constriction preventing the Grotthuss mechanism , b ) diffusion of non-proton cations is blocked by the electrostatic field generated in the NPA region [13–16] . Further , the amphipathic channel lining of the AQPs provide coordinating carbonyl oxygens only on one side , and therefore cannot sufficiently compensate for the dehydration penalty derived from the removal of the water shell from the ion [28] . Finally , the AQP protein structure is rigid and does not allow for conformational changes , because even small bends would obstruct the long ( 20 Å ) and narrow ( 3–4 Å ) water and solute channel [26] , forbidding transporter-type cation transduction following the alternate access mechanism [29] . As a consequence , naturally occurring AQPs with cation conductance along the water/glycerol channel have not been identified [28]; there is evidence , though , for cation permeability via the central pore of the human AQP1 tetramer [30] . TbAQP2 lacks an ar/R filter , which could potentially allow for proton leakage . The NPA region , however , is intact and perfectly repels cations , such as NH4+ and charged formamidine , as shown in this study . Thus , pentamidine containing two formamidine substructures cannot pass TbAQP2 . Instead , pentamidine acts as a high-affinity binder and inhibitor of TbAQP2 with one amidine moiety interacting with Asp265 by taking the place of the lacking arginine sidechain . The electrostatic interaction is increased by several orders of magnitude due to the exclusion of water from the channel ( see Fig 4A for a proposed binding mode ) . Inhibition of TbAQP2 water and solute permeability is not the mode of pentamidine action because parasites lacking the aquaglyceroporin thrive well [2] . The obtained high affinity of 130 nM despite a steep glycerol gradient of 300 mM further argues against pentamidine diffusion through TbAQP2 , because even high-affinity , alternate access-type transporters typically exhibit weaker , micromolar binding to enable release of the cargo after traversing the membrane [29] . AQP channel-like transporters , such as the malaria parasite’s lactate transporter , PfFNT , or the bacterial formate transporter , FocA , show Km values in the high millimolar range , i . e . 87 mM and 96 mM , respectively [31 , 32] . In fact , nanomolar binding constants are typically associated with high-affinity receptors . If TbAQP2 acts as a receptor rather than a transporter for pentamidine , how could drug uptake be achieved ? TbAQP2 is specifically localized to the flagellar pocket of bloodstream T . brucei parasites , which is characterized by high membrane turnover activity [33] . This activity is indispensable for the parasite and required , among others , for immune evasion and nutrient uptake . From our data we conclude that a likely scenario for TbAQP2-dependent , high-affinity uptake of pentamidine into trypanosomes is via continued , non-selective endocytosis of pentamidine tethered to TbAQP2 as a receptor ( Fig 4B ) . The hypothesis of pentamidine binding to TbAQP2 and subsequent uptake by endocytosis is strikingly supported by observations from the original biochemical characterization of pentamidine uptake [34] . Key findings were: a ) a single amidine moiety suffices to initiate high-affinity transport , which is in agreement with the binding mode of pentamidine to TbAQP2 ( Fig 4A ) , b ) transport is mainly unidirectional and highly concentrative , and c ) the process requires metabolic energy; the latter two points are fully compatible with endocytosis . Moreover , it is intriguing that in the procyclic , insect form of the parasites TbAQP2 is distributed over the entire surface of the plasma membrane [2] and these cells were found to transport slower and accumulate less pentamidine [2 , 35] further pointing at the importance of processes in the flagellar pocket for efficient pentamidine uptake . Intracellular release of pentamidine from TbAQP2 may be triggered downstream by acidification of the endo-lysosomal system that weakens the interaction with Asp265 ( Fig 4B ) . It is becoming increasingly clear that highly active processes in the flagellar pocket of African trypanosomes can be exploited for therapeutic purposes [33 , 36 , 37] . The classic pentamidine therapy appears to have been doing just that for decades . Future routes in drug design could foresee fusion scaffolds that contain a minimal pentamidine anchor , e . g . alkoxy-benzamidine , for TbAQP2 binding linked to an anti-trypanosomal cargo to be delivered into the parasite . There is a good chance for achieving sufficient specificity because of the unique TbAQP2 ar/R filter composition . This approach would be similar to the idea of generating drug fusions with the transferrin protein and transport via endocytosis of the transferrin receptor in the flagellar pocket [19] . A recent study targeted pentamidine-loaded chitosan nanoparticles via a single domain nanobody to the parasite surface , inducing endocytotic uptake [38] . Addressing TbAQP2 , however , would have the advantage that small molecules could be drafted , which , contrary to pentamidine , require only one positive moiety for Asp265 binding . Using small molecules increases the chance that the compounds will cross the blood brain barrier , which is a necessity to treat the severe cerebral stage of trypanosomiasis .
TbAQP2 , TbAQP2-L264R , TbAQP-D265A , and TbAQP3 were cloned via BamH I and HinD III into the pRS426Met25 vector [39] . The constructs carry an N-terminal hemagglutinin ( HA ) epitope-tag for detection by Western blot using a monoclonal mouse anti-HA antibody ( 1: 5000 , Roche ) and a horseradish peroxidase secondary anti-mouse antibody ( 1: 2000 , Jackson Immuno Research ) for ECL detection ( Amersham ) . For T . brucei expression , the pRPaGFPx construct [1] was modified to express GFP-tagged AQP2 under the control of a Tet-regulated RRNA promoter . Site directed mutagenesis was carried out using the QuikChange Multi Site-Directed Mutagenesis Kit ( Agilent technologies ) . Point mutations were confirmed by sequencing . Primer sequences are available upon request . T . brucei whole cell lysates were stored in the presence of a protease inhibitor cocktail ( Roche ) and were not boiled . GFP was detected using polyclonal rabbit α-GFP ( Europa; 1:2000 ) . The Saccharomyces cerevisiae strain 31019bΔmep1-3 ( MATa ura3 mep1Δ mep2Δ::LEU2 mep3Δ::KanMX ) was used , which lacks all endogenous ammonium transporters [40] . Cells expressing TbAQP2 , TbAQP3 , tomato AMT1;1 [41] , or no channel were grown overnight in liquid minimum YNB-glucose medium ( 0 . 17% YNB supplemented with 2% glucose , 0 . 5% ( NH4 ) 2SO4 , 2 × 10−3% histidine , 2 × 10−3% lysine , 0 . 01% leucine , Difco ) . Cultures were adjusted to an OD600 of 1 , and diluted in a series of 1:10 steps . For NH4+ uptake , 5 μl cell suspensions were spotted on agar plates of 20 mM MES-buffered ( pH 5 . 5 and pH 6 . 5 ) YNB-glucose medium containing 2 mM ( NH4 ) 2SO4 as the sole nitrogen source . Plates containing 0 . 1% proline were used for monitoring normal growth . For methylammonium uptake , cells were grown in YNB-glucose medium supplemented with 0 . 1% proline as a nitrogen source and 50 mM methylammonium ( Sigma ) . Susceptibility for pentamidine was assayed by growth on YPG agar media ( 1% yeast extract , 2% peptone , 2% glycerol ) without or with addition of pentamidine isethionate at 1 μg ml–1 ( 1 . 7 μM ) or 10 μg ml–1 ( 17 μM ) [21] . Growth was monitored for 2–5 days . The S . cerevisiae strain BY4742Δfps1 ( MATa , his3-1 , leu2Δ0 , lys2Δ0 , ura3Δ0 , yll043w::KanMX ) lacking the endogenous aquaglyceroporin Fps1 was obtained from Euroscarf , Frankfurt . Yeast cells expressing TbAQP2 , TbAQP2-L264R , TbAQP-D265A , TbAQP3 , or no channel were collected at 4000 × g and 4°C , washed , and incubated for 15 min in 3 ml phosphate buffer ( 50 mM , pH 7 . 2 ) plus 0 . 2% 2-mercaptoethanol . 6 ml of phosphate/2-mercaptoethanol buffer containing 1 . 8 M saccharose , 200 units of Zymolyase-20T ( MP Biomedicals , Illkirch , France ) , and 100 mg BSA Fraction V ( Roth , Karlsruhe , Germany ) were added and the suspension was incubated on an orbital shaker at 100 rpm for 60 min at 29°C . Protoplasts were collected at 2000 × g and 4°C , washed , and resuspended in 3 ml buffer ( 10 mM MOPS for pH 7 . 2; citric acid for pH 2 . 5 and 3 . 5 , or Tris for pH 9 . 2 ) plus 1 . 2 M saccharose , 50 mM NaCl , and 5 mM CaCl2 . For the assay , protoplast suspensions were diluted to an OD600 of 2 . All measurements were done using a stopped flow apparatus ( SFM-300 , BioLogic , Claix , France ) with a dead time around 10 ms , total flow rate of 14 ml/s , total volume of 202 μl , at 20°C . For hypertonic solute permeability assays , protoplasts were rapidly mixed with the same volume of buffer supplemented with 600 mM solute ( glycerol , urea , 3-amino-1 , 2-diol , or formamidine ) , generating a 300 mM hypertonic solute gradient . For isotonic glycerol permeability measurements , protoplasts were mixed with the same volume of buffer in which 0 . 6 M saccharose were replaced by glycerol , generating an isotonic 300 mM glycerol gradient . Pentamidine and derivatives were added 10 min prior to the assay . Protoplast volume changes were monitored by measuring the intensity of 90° light scattering at 546 nm . In each experiment , 6 to 9 single curves were averaged , exponentially ( rapid rates ) or linearly fitted ( slow rates ) and the resulting time constants were related to solute permeability . The solute permeability coefficient Psol was calculated using Psol = |dI/dt| · ( V0 · Cout ) / ( S0 · Cdiff ) [16] , with dI/dt being the slope of the intensity curve , V0 the initial mean protoplast volume ( 65 . 45 μm3 ) , S0 the initial mean protoplast surface area ( 78 . 54 μm2 ) , Cout is the total external solute concentration ( 1 . 5 M ) , and Cdiff is the chemical solute gradient ( 0 . 3 M ) . Pentamidine and 4-methoxy-benzamidine were from Sigma . 4-Pentoxy- and 4-propoxy-benzamidine were generated by Williamson ether synthesis from 4-hydroxy-benzonitrile and 1-bromopentane or 1-bromopropane , respectively , and subsequent conversion of the nitrile to amidine using CH3Al ( Cl ) NH2 [42] . The asymmetric pentamidine derivative carrying one amidine and one nitrile moiety was generated in a first step by Williamson ether synthesis of 4-hydroxy-benzonitrile and 1 , 5-dibromopentane to obtain the di-nitrile compound . The second step was done according to Pinner [43] in molecular sieve-dried ethanol using HCl gas as an acidic catalyst and stopped before full conversion of the nitriles to imino esters . The semi-converted product was isolated by chromatography and the single amidine group was obtained by reaction with gaseous ammonia in ethanol . Nitrile-pentamidine hydrochloride: 1H NMR ( 300 MHz , DMSO-d6 ) δ 9 . 33 ( 2H , s ) , 9 . 19 ( 2H , s ) , 7 . 88 ( 2H , d ) , 7 . 74 ( 2H , d ) , 7 . 11 ( 4H , m ) , 4 . 09 ( 4H , m ) , 1 . 8 ( 4H , m ) , 1 . 57 ( 2H , m ) . Bloodstream-form T . brucei , Lister 427 , MiTat 1 . 2 , clone 221a and derivatives were maintained as previously described [2]; 2T1 [1] and aqp2 null strains [2] were also described previously . Strains were transfected using a Nucleofector apparatus ( Lonza ) in conjunction with cytomix and transformants were selected with hygromycin ( 2 . 5 μg ml–1 ) . GFPAQP expression was induced by exposing cells to 1 μg ml–1 tetracycline for 48 h . EC50 assays were carried out using alamarBlue as described [44 , 45] . T . brucei immunofluorescence microscopy was carried out according to standard protocols . Briefly , cells were settled on slides and mounted in Vectashield ( Vector Laboratories ) containing the DNA counterstain , 4 , 6-diamidino-2-phenylindole ( DAPI ) . Images were captured using a Nikon Eclipse E600 epifluorescence microscope in conjunction with a Coolsnap FX ( Photometrics ) charge-coupled device ( CCD ) camera and processed in Metamorph 5 . 0 ( Photometrics ) . Trypanosoma brucei AQP2: CAG27021 ( GenBank ) Trypanosoma brucei AQP3: CAG27022 ( GenBank ) Lycopersicon esculentum AMT1;1: P58905 . 1 ( SwissProt ) Saccharomyces cerevisiae MEP1: CAA97132 ( GenBank ) Saccharomyces cerevisiae MEP2: CAA96025 ( GenBank ) Saccharomyces cerevisiae MEP3: DAA11552 ( GenBank ) Saccharomyces cerevisiae Fps1: CAA38096 ( GenBank ) Escherichia coli GlpF: BAE77383 ( GenBank ) | Pentamidine is usable only in the early , non-cerebral stage of human African trypanosomiasis . An aquaglyceroporin , TbAQP2 , is required for high-affinity pentamidine uptake . This study established the mechanism . Pentamidine does not pass TbAQP2 but blocks the unique selectivity filter via a single charged amidine and displacement of water from the channel , rendering it the first non-covalent , nanomolar aquaglyceroporin inhibitor . TbAQP2 does not act as a pentamidine transporter but a high-affinity receptor indicating drug uptake by endocytosis . The novel binding mode and proposed uptake mechanism open up possibilities for designing needed anti-trypanosomal small molecule drugs with regard to penetrating the blood-brain barrier for treatment of the severe , second disease stage . | [
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] | 2016 | Pentamidine Is Not a Permeant but a Nanomolar Inhibitor of the Trypanosoma brucei Aquaglyceroporin-2 |
Fertility traits in humans are heritable , however , little is known about the genes that influence reproductive outcomes or the genetic variants that contribute to differences in these traits between individuals , particularly women . To address this gap in knowledge , we performed an unbiased genome-wide expression quantitative trait locus ( eQTL ) mapping study to identify common regulatory ( expression ) single nucleotide polymorphisms ( eSNPs ) in mid-secretory endometrium . We identified 423 cis-eQTLs for 132 genes that were significant at a false discovery rate ( FDR ) of 1% . After pruning for strong LD ( r2 >0 . 95 ) , we tested for associations between eSNPs and fecundability ( the ability to get pregnant ) , measured as the length of the interval to pregnancy , in 117 women . Two eSNPs were associated with fecundability at a FDR of 5%; both were in the HLA region and were eQTLs for the TAP2 gene ( P = 1 . 3x10-4 ) and the HLA-F gene ( P = 4 . 0x10-4 ) , respectively . The effects of these SNPs on fecundability were replicated in an independent sample . The two eSNPs reside within or near regulatory elements in decidualized human endometrial stromal cells . Our study integrating eQTL mapping in a primary tissue with association studies of a related phenotype revealed novel genes and associated alleles with independent effects on fecundability , and identified a central role for two HLA region genes in human implantation success .
Natural variation in fertility traits is heritable in humans [1] , yet identifying genes contributing to these traits remains challenging . Although genome-wide association studies ( GWAS ) have identified variants associated with many other complex phenotypes , its application to fertility traits is challenging . In particular , widespread contraceptive use among fertile couples and significant clinical heterogeneity among infertile couples makes it difficult to sample large numbers of fertile subjects with unprotected intercourse or infertile subjects whose inability to conceive results from the same underlying biological processes . Although a few GWAS of male fertility [2] or infertility [3 , 4] traits have identified promising candidate genes , to date there have been no such studies in women . To address these limitations , we have focused our genetic studies of fertility on members of a founder population , the Hutterites [1 , 2 , 5–8] . This communal living group of European ancestry has limited contraceptive use , a uniform desire for large families , a prohibition of smoking , and fertility rates that are among the highest ever reported [9 , 10] . For example , only 2% of Hutterite couples are childless [9] compared to 10–15% of the general population [11] . Whereas miscarriages of clinically recognized pregnancies among Hutterite couples is 15 . 6% [8] , nearly identical to estimates of clinically recognized miscarriage rates in outbred populations [12] , recurrent miscarriages in childless couples are rare ( 0 of 525 interviewed Hutterite women [1] ) compared to 5% in the general population [13] . Moreover , their communal lifestyle ensures that sociocultural factors influencing fertility are relatively uniform among Hutterite couples [1 , 14] . We have proposed that their naturally high fertility rates , their reduced variance in environmental and lifestyle factors , and their limited gene pool due to the founder effect make the Hutterites an ideal population in which to dissect the genetic architecture of reproductive traits [1 , 2 , 15] . Here , we use an integrated strategy that first identifies a set of candidate regulatory single nucleotide polymorphisms ( SNPs ) in mid-secretory phase endometrium by expression quantitative trait locus ( eQTL ) mapping in non-Hutterite women with two or more previous miscarriages . We then tested for associations between those putatively functional expression ( e ) SNPs and fecundability in Hutterite women who are participants in a prospective study of pregnancy outcome [7 , 8] , and replicated the significant findings in an independent sample of women [16] . We report here the discovery of independent associations between SNPs that are eQTLs for the HLA-F and TAP2 genes in mid-secretory phase endometrium and fecundability ( the probability of achieving pregnancy ) , thereby implicating maternal HLA region genes for the first time in implantation processes .
We performed eQTL mapping in the mid-secretory phase endometrium , corresponding to the luteal phase of the ovarian cycle , from 53 women with two or more early pregnancy losses , using 378 , 362 common ( ≥10% ) SNPs that were within 200kb of one or more of the 10 , 191 genes detected as expressed in these tissues ( i . e . , cis-eQTLs ) ( see Methods ) . We observed 423 cis-eQTLs ( 416 unique SNPs ) for 132 genes at a false discovery rate ( FDR ) of 1% ( S1 Dataset ) . We next looked for gene ontology enrichments in the genes associated with eQTLs at a FDR of 1% using DAVID [17 , 18] and GREAT [19] . We found an enrichment of the GO Biological Process of “antigen processing and presentation” ( DAVID , FDR 2 . 5x10-5 ) , the GO Molecular Function of “MHC class 1 receptor activity” ( DAVID , FDR 5 . 30x10-4 ) , and GO Cellular Component “MHC class 1 protein complex” ( GREAT , FDR 2 . 02x10-6 ) . Many of the DAVID and GREAT enrichments overlapped , and both highlighted the importance of immune related genes among those with eQTLs in mid-secretory phase endometrium . To assess the clinical relevance of these eQTLs on female fertility traits , we first pruned the 416 SNPs for strong LD ( r2 ≥ 0 . 95 in the Hutterites ) and then carried forward 245 expression ( e ) SNPs for association studies in the prospective study participants . We previously genotyped 208 of the 327 Hutterite women in a prospective study of pregnancy outcomes [6 , 7 , 15 , 20] with the Affymetrix 500k or 6 . 0 genotyping chips . Using these genotypes as framework markers , we imputed all variants present in the whole genome sequences of 98 Hutterites to these women using PRIMAL , an imputation program that utilizes both pedigree- and LD-based imputation to provide on average of 87% coverage and >99% accuracy in the Hutterite pedigree [21] . From among the 245 ( LD-pruned ) SNPs that were eQTLs at a FDR 1% , genotypes for 189 ( associated with the expression of 108 genes ) were known for at least 85% of Hutterite women in the study of fecundability . We compared the length of the intervals from the resumption of menses after a prior pregnancy or miscarriage or following the discontinuation of birth control use ( referred to as time0 ) to a positive pregnancy test in women who were not nursing at time0 of each included interval . For first pregnancies , we considered the length of the interval from the first menses after marriage to a positive pregnancy test ( see Methods ) . If pregnancy occurred prior to the resumption of menses ( or before the first period after marriage in first pregnancies ) , we considered the interval to be 14 days . Data were available for 191 intervals in 117 women ( see Methods ) ; 178 of the observed intervals resulted in a pregnancy at the time of the last follow-up . We used life-table analysis to compare intervals between genotype classes , adjusting for two significant covariates: maternal age and number of prior births ( classified as 0–1 , 2–3 , or ≥4 ) ( see Methods ) . Among the 189 eSNPs that we examined , genotypes for 21 were associated with the length of the interval to pregnancy at a P < 0 . 05 ( Table 1 ) . Two of these eSNPs were significant at a FDR of 5% and after Bonferroni correction for 189 tests . The most significant association was with rs2071473 , an eSNP associated with expression of the TAP2 gene in the HLA class II region; the C allele at this SNP was associated with longer intervals to pregnancy and higher expression of TAP2 gene in mid-secretory phase endometrium ( Fig 1 ) . The median interval lengths to pregnancy were 2 . 0 ( lower , upper quartile 1 . 2 , 4 . 7 ) , 3 . 1 ( 1 . 9 , 6 . 2 ) , and 4 . 0 ( 2 . 0 , 7 . 6 ) months among women with the TT , CT , and CC genotypes , respectively , at this eSNP ( P = 1 . 3x10-4 ) . The second association was with rs2523393 , an eSNP associated with expression of the HLA-F gene in the HLA class I region; the G allele at this SNP was associated with longer intervals to pregnancy and lower expression of HLA-F in mid-secretory phase endometrium ( Fig 2 ) . The median interval lengths to pregnancy were 2 . 3 ( 1 . 8 , 4 . 5 ) , 2 . 6 ( 1 . 4 , 4 . 8 ) , and 4 . 9 ( 2 . 0 , 11 . 7 ) months among women with the AA , AG , and GG genotypes , respectively , at this eSNP ( P = 4 . 0x10-4 ) . For both eSNPs , intervals were longer and genotype differences more pronounced among women at lower parity ( S1 and S2 Figs ) . Among the 21 eSNPs with P <0 . 05 , nine ( 43% ) were associated with expression of HLA region genes: one with TAP1 , three with HLA-F , three with HLA-G , and two with MICA , consistent with the gene ontology analysis identifying enrichments for genes with antigen processing and presentation functions among those with eQTLs in mid-secretory phase endometrium . The results for all 189 eSNPs and their associated genes are shown in S2 Dataset . Previous studies of HLA and fertility in the Hutterites have shown that HLA matching between partners for alleles at the class II locus HLA-DRB1 is associated with reduced fecundability , presumably due to the higher risk for class II compatible embryos among these couples [7] . To rule out that maternal-fetal compatibility at the TAP2 or HLA-F locus accounts for the observed effects in this study we repeated the fecundability analysis , first stratifying couples based on husband’s genotype ( rather than wife’s genotype ) and then stratifying couples based on the wife’s genotype ( as above ) but now including the husband’s genotype as a covariate . We reasoned that if longer intervals are due to maternal-fetal compatibility and not maternal genotypes per se , then results of analyses stratifying by husband’s genotype should yield results similar to analyses stratified by wife’s genotype , and analyses including both husband’s and wife’s genotypes should be more significant than analyses considering either one individually . Neither eSNP was significant in the analysis considering the husband’s genotype as a main effect on length of intervals to pregnancy ( HLA-F rs2523393 P = 0 . 94; TAP2 rs2071473 P = 0 . 56 ) . When husband’s genotype was included as a covariate in the model , the P-values were reduced from 4 . 0x10-4 to 0 . 0014 for rs2523393 and from 1 . 3x10-4 to 0 . 0015 for rs2071473 , but the effect size associated with the risk alleles remained largely unchanged ( β coefficients changed from 0 . 39 to 0 . 34 for rs2523393 and -0 . 44 to -0 . 36 rs2071473 when husbands’ genotypes were included as a covariate ) . These data indicate that maternal genotype at these two eSNPs is driving the association with time to pregnancy; there is no evidence for paternal or fetal genotype effects at these eSNPs contributing to interval lengths . Although these two eSNPs reside at opposite ends of the HLA region and are separated by ~3Mb , there are moderate levels of LD between them in the Hutterites ( r2 = 0 . 19 ) . To determine the statistical independence of the associations with fecundability , we repeated the time to pregnancy analysis but included the genotype at the other eSNP as a covariate . In the analysis of rs2071473 ( TAP2 ) that included genotype at rs2523393 ( HLA-F ) as a covariate , the effect size and P-value changed from β = 0 . 39 ( P = 1 . 3x10-4 ) to β = 0 . 23 ( P = 0 . 0064 ) ; in the analysis of the rs2523393 ( HLA-F ) that included genotype at rs2071473 ( TAP2 ) as a covariate , the effect size and P-value changed from β = -0 . 44 ( P = 4 . 0x10-4 ) to -0 . 34 ( P = 0 . 047 ) . Thus , while the magnitude of each association is reduced in the analyses conditioning on the alternate eSNP , both retain independent effects on fecundability . The observed reduction in β values and significance is likely due to the LD between the SNPs . To further examine this , we stratified the women into three groups based on being homozygous at both , one , or neither of the high risk ( longer interval ) alleles at each eSNP ( CC at rs2071473 [TAP2] and GG at rs2523393 [HLA-F] ) ( Fig 3 ) . If the effects at these two loci were independent , then women who are homozygous for the high risk allele at both eSNPs should have longer intervals than women who are homozygous at only one or neither high risk allele . Indeed , intervals to pregnancy were longest among women homozygous for both rs2071473-CC and rs2523393-GG ( median interval 5 . 2 months [1 . 9 , 11 . 0 months] ) , intermediate among homozygous for only one of the high risk alleles ( median interval 4 . 0 months [2 . 1 , 9 . 3 months] , and shortest among women who were not homozygous for either high risk allele ( median interval 2 . 4 months [1 . 4 , 4 . 8 months] ) ( P = 2 . 9x10-4 ) . Moreover , women homozygous for the risk alleles at both the TAP2 and HLA-F eSNPs had significantly longer intervals compared to women who were homozygous for a risk allele at only one of the two eSNPs ( P = 1 . 6x10-5 ) . Taken together these analyses indicate that the TAP2 and HLA-F associations are independent and have additive effects on fecundability . Using the same approach as that used in the Hutterites , we first examined the genotype effects of each SNP on fecundability and then the joint effects of the combined genotypes at each locus . At rs2071473 , the TAP2 eSNP , the median interval lengths to pregnancy were 5 . 0 ( 4 . 0 , 7 . 0 ) , 6 . 0 ( 4 . 0 , 8 . 2 ) , and 6 . 0 ( 4 . 0 , 9 . 0 ) months among women with the TT , CT , and CC genotypes , respectively ( P = 0 . 083; Fig 4A ) . At rs2523393 , the HLA-F eSNP , the median interval lengths were 5 . 0 ( 4 . 0 , 8 . 5 ) , 6 . 0 ( 4 . 0 , 8 . 0 ) , and 6 . 0 ( 5 . 0 , 10 . 0 ) months among women with the AA , AG , and GG genotypes , respectively ( P = 0 . 155; Fig 4B ) . Although these results did reach nominal significance in the RFTS cohort , the 95% confidence intervals of the ORs in the Hutterites and RFTS cohort overlap ( S1 Table ) . In the combined analysis , intervals to pregnancy were longest among women homozygous for both rs2071473-CC and rs2523393-GG ( median interval 8 . 0 months [5 . 5 , 12 . 5 months] ) , intermediate among homozygous for only one of the high risk alleles ( median interval 6 . 0 months [4 . 0 , 9 . 0 months] , and shortest among women who were not homozygous for either high risk allele ( median interval 5 . 0 months [4 . 0 , 7 . 0 months] ) ( P = 0 . 033; Fig 4C ) , as we observed in the Hutterites . Because there are many SNPs in strong LD with our lead eSNPs in the Hutterites , it cannot be inferred from association studies which of these linked SNPs are the true causal variants . To address this question , we used in silico analyses to determine which of the fecundability-associated eQTLs are in or near regulatory elements in decidualized human endometrial stromal cells [22 , 23] , ENCODE-annotated functional sites in the endometrial cell lines ECC-1 and Ishikawa [24] , as well as the complete ENCODE regulatory element dataset . To interrogate more completely the variation in these regions , we used whole genome sequence data in the Hutterites to survey all variation in the 500kb windows flanking each of the two eSNPs that were associated with fecundability . After filtering our variants with minor allele frequencies <0 . 10 and call rates <85% , 4 , 442 variants remained in the TAP2 region and 2 , 675 variants remained in the HLA-F region . We then filtered these variants based on their LD with each lead eSNP and retained the 70 variants with LD r2 ≥ 0 . 7 with rs2071473 and the 62 variants with LD r2 ≥ 0 . 7 with rs2523393 . The variants that had LD r2 ≥ 0 . 7 with the lead eSNP in each region defined an approximately 6kb window around the lead SNP . We repeated the association studies with all variants within each 6kb window and fecundability . Because many of these variants were not included in the eQTL study in the 53 women with recurrent early pregnancy loss , we also imputed the missing genotypes using whole genome sequences from 100 European American individuals [25] ( see Methods ) and performed eQTL mapping in the mid-secretory phase endometrial RNA using these variants , as described above . The lead eSNP at the TAP2 locus , rs2071473 , is located within an intron of the HLA-DOB gene and is near ( ~600bp ) an NF2R2 transcription factor ( TF ) binding site in hESC; EP300 , FOXM1 , ATF2 , and RUNX3 binding sites in a B-lymphoblastoid cell line ( GM12878 ) ; and a DNase-I hypersensitivity site in 40 ENCODE cell lines ( Fig 5A ) . This SNP is also within ~800bp of a FAIRE peak in hESC . Another SNP , rs2856995 , that is 732bp upstream of and in perfect LD ( r2 = 1 ) with rs2071473 resides within the NF2R2 binding site and 92bp upstream of the FAIRE site in hESC . Multiple other variants are located adjacent to FAIRE sites and among the 36 of these variants with eQTLs , 34 were eQTLs only for TAP2 ( FDR <15% ) , and two were eQTLs for TAP1 ( FDR = 14% ) ( S3 Dataset ) . The lead eSNP at the HLA-F locus , rs2523393 , is located within an intron of HLA-F-AS1 , an antisense transcript that was not expressed in mid-secretory endometrium . The eSNP is about 10kb downstream of HLA-F , and in perfect LD ( r2 = 1 ) with a cluster of SNPs ~360bp away that reside within multiple ENCODE-annotated functional sites in endometrial cell lines [26] ( Fig 5B ) . One such SNP , rs2523389 , is in a DNaseI hypersensitivity site in 120 ENCODE cell lines including the endometrial derived cell lines ECC-1 and Ishikawa treated with 10nM estradiol . This variant is also in a CTCF binding site that is present in 97 ENCODE cell lines , including the endometrial cell line ECC-1 , and in a c-Myc binding site in a leukemia cell line . Another variant , rs2523391 , is in a STAT3 binding site in a mammary gland cell line and 6bp from a CEBPB TF ChIP-seq binding site in HeLa and HepG2 cell lines , in addition to the same functional sites as rs2523389 . Although the c-Myc , STAT3 , and CEBPB binding sites were not present in the hESC or ENCODE endometrial cell lines , these transcription factors are essential for decidualization of endometrial stromal cells and the successful establishment of pregnancy [27–29] . Among the variants in LD with rs2523393 at r2 ≥ 0 . 7 and with eQTL results , all were most strongly associated with expression of HLA-F ( S4 Dataset ) . One eSNP in the HLA-F promoter ( rs1362126; r2 = 0 . 78 to rs2523393 ) was also an eQTL for HLA-G ( FDR <1% ) , although to a lesser degree than for HLA-F ( HLA-F eQTL P = 2 . 18x10-6 , HLA-G eQTL P = 2 . 06x10-3 ) . Overall , these data indicate that our lead eSNPs and/or a small number of variants in perfect LD with those eSNPs are plausible causal candidates for the observed associations with fecundability in each region .
The mechanisms that allow the fetal allograft to avoid maternal immunologic rejection and survive over relatively long gestational periods in placental mammals are still incompletely known , although our understanding of these processes have advanced considerably since Medawar proposed this paradox over 60 years ago [30] . In particular , it has become clear that major histocompatibility complex ( MHC ) antigens , which play a central role in the rejection of non-self tissues , also contribute to maternal tolerance of the fetus , which is maintained in normal pregnancies . For example , our group previously demonstrated that matching of HLA antigens ( the human MHC loci ) between Hutterite couples is associated with longer intervals from marriage to each birth compared to couples not matching for HLA [31] , and that longer intervals resulted from both higher miscarriage rates among couples matching for class I HLA-B antigens [8] and longer intervals to pregnancy among couples matching for class II HLA-DR antigens [7] . More recently , we reported associations between maternal HLA-G genotypes and miscarriage [6] . We and others have also shown associations between maternal or fetal HLA-G genotypes with recurrent pregnancy loss and preeclampsia [32–42] . Finally , recent studies have elegantly demonstrated that two HLA that are expressed by fetal extravillous cytotrophoblast ( EV-CTB ) cells at the maternal-fetal interface , HLA-G and HLA-C , are ligands for inhibitory receptors ILT2/IL4 and KIR2DL3 , respectively , on immune cells [43–45] . Collectively these data indicate that multiple HLA molecules play important independent roles at the maternal-fetal interface in human pregnancy and that their effects can influence pregnancy outcome throughout gestation . In this study , we hypothesized that perturbations of genes expressed in mid-secretory phase endometrial cells could affect implantation and be visible as delayed time to pregnancy in otherwise fertile couples . Although our study was unbiased with respect to genome location because we interrogated variation that was first identified through a genome-wide eQTL study , the results of both the eQTL study and the subsequent study of fecundability highlighted the importance of HLA region genes in achieving pregnancy . Among the eQTLs taken forward to studies of fecundability in the Hutterites , nine ( 43% ) of the eSNPs with association P-values <0 . 05 were eQTLs for HLA region genes compared to 15% of all 189 eSNPs tested . Two of the associations with fecundability were significant at a FDR of 5% , remained significant after correction for multiple testing , and were replicated in an independent sample of fertile women: one SNP was with an eQTL for TAP2 and one for HLA-F . To our knowledge neither of these two HLA region genes has previously been directly implicated in pregnancy processes . We further demonstrated that eQTLs for these two HLA loci in mid-secretory phase endometrium are independently associated with fecundability in fertile women and that neither paternal nor fetal genotype at these loci contributed to these effects . These findings may be particularly relevant to women with primary infertility of unknown etiology , with recurrent implantation failure following in vitro fertilization ( IVF ) , or possibly even with recurrent early pregnancy loss . Both genes are intriguing candidates for fecundability genes . The TAP2 gene in the class II HLA region encodes the antigen peptide transporter 2 protein . TAP2 forms a heterodimer with TAP1 ( encoded by the TAP1 gene , located 7kb away ) in order to transport peptides from the cytoplasm to the endoplasmic reticulum , where they are loaded into assembling class I HLA molecules prior to their transport to the cell surface . The association of TAP complex with HLA class I molecules , including HLA-F [46] , is critical for their expression on the cell surface [47] . HLA-F , which is located ~3Mb telomeric to TAP2 , encodes a class I HLA protein that is considered “non-classical” because it has limited coding polymorphisms and restricted tissue distribution [48] , and functions that are still poorly characterized but likely distinct from the classical class I HLA ( HLA-A , HLA-B , HLA-C ) . In fact , recent studies have shown that HLA-F physically interacts with the KIR3DL2 and KIR2DS4 receptors on natural killer ( NK ) cells [49] , an abundant and critical cell in the maternal uterus that proliferates during the secretory phase and then throughout pregnancy [50] . Later in pregnancy , HLA-F is expressed in EV-CTB [51–54] , although its function in placental cells is not well characterized [52] . Our combined results suggest that perturbations in expression of either gene in endometrial cells in the mid-secretory phase influences implantation success , with overexpression of TAP2 and underexpression of HLA-F resulting in delayed time to pregnancy . We found multiple variants in perfect LD with both eSNPs that reside in transcription factor binding sites and other regulatory elements in endometrial cell lines . The TAP2-associated variants are located within a NR2F2 ( COUP-TFII ) binding site . Multiple studies have shown that female mice deficient in NR2F2 have implantation failure , with impairments of both embryo attachment and uterine decidualization [55–57] , and NR2F2 knock downs a human endometrial stromal cell line significantly reduces TAP2 expression [22] . These combined data suggest a potential mechanism for the association we observed with expression of TAP2 and fecundability in women . At the HLA-F locus , variants associated with gene expression level and fecundability are in DNaseI hypersensitivity sites , a marker of open chromatin and transcriptional activity , in multiple human endometrial cell lines [26] , suggesting that one or more of these variants may indeed be causally associated with both gene expression and fecundability . Although genome-wide association studies can be powerful approaches for identifying susceptibility loci for common diseases and complex phenotypes , they require very large sample sizes that may be infeasible to acquire for many important phenotypes . We used an alternative approach for mapping fecundability genes by first reducing the search space to SNPs that were associated with gene expression in a relevant tissue and then taking this smaller set of regulatory SNPs forward to an association study in carefully phenotyped subjects . This approach revealed two novel associations with fecundability and immediate intuition regarding the genes underlying each association , the relationship between gene expression and fecundability , and potential mechanisms for these associations . Future studies will be required to characterize the role of these molecules in the implantation process and to evaluate their potential as drug targets for treatment of conditions related to suboptimal implantation .
Fifty-eight women underwent endometrial biopsies as part of their clinical evaluation for recurrent pregnancy loss at the University of Chicago , after obtaining informed consent . These women were between the ages of 26 and 43 years and had at least two previous pregnancy losses before10 weeks gestation . Fifty-two ( 90% ) were of European ancestry , two ( 3% ) were of Asian ancestry , and four ( 7% ) were of African ancestry . Medical records and individual diagnoses were not available to us for this study , and all women were included in the expression studies . Because recurrent miscarriage can result from many potential causes , and nearly half remain unexplained after evaluation , we reasoned that gene expression in the endometrium from these women would maximize variation in gene expression and increase our power to detect eQTLs . We were unable to obtain samples from women without a history of pregnancy loss for this study . Endometrial biopsies were performed during the mid-secretory phase ( 9–11 days after endogenous luteinizing hormone [LH] surge , detected by each woman testing her daily urine ) and immediately frozen on dry ice; samples were stored at -80°C until RNA was extracted , as previously described [58] . Histological examination of the biopsies confirmed endometrial tissue from the fundus of the uterus; endometrial glands and epithelium were present . RNA was extracted from the endometrial biopsies using a phenol-chloroform phase separation with TRIzol per the manufacturer’s directions ( Life Technologies Corp . , Carlsbad , CA , USA ) and RNeasy RNA extraction kit ( Qiagen , Venlo , Netherlands; per manufacturer’s directions ) . RNA quality was assessed using the Agilent 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA ) . The average RNA integrity number ( RIN ) score [59] was 8 . 06 ( range 6 . 7–9 . 3 ) . We were unable to obtain RIN scores for two samples due to extremely low concentration . However , these two samples passed all gene expression QC and were therefore included in the eQTL mapping studies . Gene expression was measured in RNA from 58 individuals . We included triplicate samples for three women and duplicate samples for 29 . Gene expression was interrogated using the Human HT12v4 Expression BeadChip ( Illumina , Inc . , San Diego , CA , USA ) , which contains 47 , 231 probes that target 11 , 121 unique RefSeq genes . cDNA synthesis , hybridization , scanning and image processing and returned probe intensity measurements were performed at the University of Chicago Functional Genomics Core . Intensity estimates were log-transformed and quantile normalized using the ‘lumi’ package in R [60] ( see S3 Fig ) . To remove probes for targets that were likely not expressed , all probes that did not have a detection P-value <0 . 05 in at least 70% of the samples were removed ( leaving 17 , 208 probes ) . We further removed probes that did not map uniquely to the HG19 genome using Burrows-Wheeler Aligner ( BWA ) , and probes that contained CEU HapMap SNPs with the QC+ designation ( 4 , 825 probes total excluded ) . After quality control ( QC ) , 12 , 383 probes remained and were included in our eQTL studies . Probe averages were taken for replicate samples . Of the 11 , 121 unique genes targeted on the array , 2 , 192 ( 20% ) had multiple probes [61] . For those genes with multiple probes , we chose the most 3’ probe in the gene to estimate expression . Samples from two women were excluded prior to QC because there were too few probes detected after hybridization ( 13 , 189 and 15 , 863 probes , respectively , compared to a median count of 21 , 341 out of 47 , 231 probes on the array ) ; and three women were excluded prior to analysis because there was no DNA available for one and low genotyping call rates in two ( see below ) . The remaining 53 women ( 49 European ancestry , 2 Asian ancestry , 2 African American ancestry ) had both high quality expression and genotype data . In those 53 samples , 10 , 191 genes were detected as expressed . Processing batch and array were significantly associated with the variance in gene expression based on principle component ( PC ) analysis and their effects were regressed out using a linear model ( see S4 Fig ) . Other covariates that were considered ( age , BMI , race , and season of biopsy collection ) were not significant in these samples ( see S2 Table ) . An overview of the sample inclusion pipeline is shown in S5A Fig . DNA from women in the gene expression studies were genotyped with the Affymetrix Axiom Genome-Wide CEU 1 Array at the UCSF Genomics Core Facility . We performed QC checks using PLINK [62] , and removed 4 , 922 SNPs with <95% genotype call rates , 503 with Hardy-Weinberg P-values ≤0 . 001 , 336 non-autosomal SNPs , and 252 , 872 with minor allele frequencies <0 . 10 . There were 370 , 008 SNPs remaining . After excluding two women with low call rates , the remaining 53 subjects had SNP call rates >97% . Linear regression was used to test for associations between the expression levels of 10 , 191 genes and genotypes at the 378 , 362 SNPs with a minor allele frequency greater than 10% , using the R package Matrix eQTL [63] . Genotypes were recoded as 0 , 1 , or 2 to reflect an additive model . To maximize the power in our sample , we tested for associations only with SNPs within ~200kb of the transcription start site of a gene , a distance that would include nearly all cis regulatory SNPs [64–66] . The Matrix eQTL package assigns both a p-value and a false discovery rate ( FDR ) to each SNP-gene association . The FDR was calculated using the Benjamini and Hochberg [67] procedure ( see [63] for detailed methods ) . eQTL mapping was additionally performed including only the 49 women of European ancestry . As expected , p-values were generally less significant in the smaller sample , however the HLA-F and TAP2 eQTLs remained significant at a FDR <1% ( S5 Dataset ) . We used the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) v6 . 7 [17 , 68] to interrogate pathway and gene enrichment for eQTLs at a FDR 1% compared to all gene-SNP combinations in our analysis ( background ) . An enrichment score was calculated using Fisher's Exact test ( modified as EASE score ) on gene count for eQTLs at a FDR of 1% compared to all genes tested . We used the high classification stringency for our analysis . We also used the Genomic Regions Enrichment of Annotations Tool ( GREAT ) to analyze the significance of SNPs which are eQTLs at a FDR of 1% [19] . GREAT first associates genomic regions with nearby genes and then applies the functional annotations for those genes to the regions . We used the basal plus extension definition of a gene regulatory domain , in which a gene’s defined regulatory domain expands until it reaches the nearest gene’s basal domain or maximally 5kb upstream and 1kb downstream . Using this definition , SNPs located between two genes may include both gene regulatory domains . GREAT uses a hypergeometric test over these defined genomic domains to assess enrichment between foreground ( FDR 1% eQTLs ) and background ( all SNPs for which there is a gene-SNP pair tested in the eQTL analysis ) . The data included in our analyses of fecundability were derived from a prospective study of pregnancy outcome in South Dakota Hutterites that was initiated in 1986 , as previously described [6–8 , 69] . The women in this study are provided with calendar diaries and EPT pregnancy test kits ( Warner-Lambert Co . ) . They record in the diary dates of menses , changes in nursing patterns , illnesses or travel for the husband and wife , and dates of miscarriages or deliveries . In addition , they are instructed to test for pregnancy if they do not start menses exactly one month after the first day of their previous period , and to record the results of all pregnancy tests in the diaries . They are also asked to start testing for pregnancy on a monthly basis starting 6 months after delivery until menses resumes . Results of all pregnancy tests and outcomes of each pregnancy are recorded in the diaries , which are collected yearly either in person or through the mail . The results reported here include data collected through 2013 . Among the 325 Hutterite women who have participated in the prospective study , 156 women had at least one interval during which they were not nursing at time0 . Of these 156 women , genotype data were available for 117 . The following studies were performed in these 117 women , who provided information on 191 intervals . An overview of the sample inclusion pipeline is shown in S5B Fig . The distribution of the interval lengths until a positive pregnancy test was compared between genotype groups using non-parametric life-table analyses [70] . The product-moment method was used to compute the time-to-pregnancy curves . These curves were compared with the Mantel-Haenszel log-rank . Women were stratified based on their genotype . Potential confounding effects of maternal age at the beginning of each interval , number of prior miscarriages , number of births ( parity ) , maternal birth year , maternal inbreeding , and the kinship coefficient of the couple were assess by Cox regression analysis , as previously described [7] . Only maternal age and number of prior births ( parity ) were significant covariates and included in subsequent analyses . Because the Cox model assumes that the hazard ratios for continuous variables are log linear , we classified women ( at each observation ) by the number of prior births in three categories: 0–1 , 2–3 , or ≥4 prior births . RFTS is a pregnancy cohort that enrolled study participants from the community between 2001 and 2012 . Participants were recruited from Galveston , Texas; Memphis , Nashville , Knoxville , and Chattanooga , Tennessee; and the Research Triangle region ( Raleigh , Durham , and Chapel Hill ) in North Carolina . Detailed descriptions of direct marketing and recruitment strategies have been previously described [16] . Participants completed a baseline interview at enrollment and a computer assisted telephone interview at the end of the first trimester . All pregnancies were confirmed by pregnancy tests performed either by their provider ( with confirmation ) , the study staff , or by the participant with BFP Early Pregnancy Test Strips ( Fairhaven Health ) provided by the study staff . The baseline and first trimester interviews provided information on reproductive history and potential confounders . Information about fecundability was collected on the baseline interview . Women were asked to report the number of cycles or months that it took them to conceive ( if pregnant ) or how long they have been trying to become pregnant ( if not pregnant ) . Time to pregnancy was censored after 11 months; women in the study did not use any contraception during this time . We considered only one interval ( pregnancy ) per woman . DNA samples were obtained either in person or by mail during follow-up using Oragene saliva DNA kits ( DNA Genotek Inc . , Ontario , Canada ) . These analyses included 314 RFTS participants who were 18 years or older , non-Hispanic white , and had self-reported time-to-pregnancy . In these women , we genotyped the two SNPs associated with fecundability in the Hutterites ( rs2071473 and rs2523392 ) using TaqMan assays . We then excluded from subsequent analyses 22 women who did not know or declined to answer questions about their contraceptive practices , eight women who could not provide information on their cycle length , 22 women whose cycle lengths were <21 days or >35 days , one woman with cycle lengths >35 days and did not respond to questions about her contraceptive practices , and one woman with missing genotype data . These exclusions resulted in 260 women that were included in subsequent analyses . An overview of the sample inclusion pipeline is shown in S5C Fig . The genotypic effects on time to pregnancy were estimated using a discrete time hazard model , a discrete time analog to the Cox proportional hazards model . We considered as covariates maternal age , education , marital status , income , smoking , alcohol use , caffeine consumption , body mass index , number of previous pregnancies , number of previous elective pregnancy terminations , and whether they were pregnant at the start of the study using a forward selection method . Number of previous elective terminations ( range 0–2; median = 0 ) was inversely associated with interval lengths ( P = 0 . 028 ) and being pregnant at the start of the study was associated with shorter interval lengths ( P = 0 . 0016 ) . These two covariates were included in the analysis of genotype effects . Although age and number of previous pregnancies ( parity groups: 0–1 , 2–3 , ≥4 ) were not significant predictors of interval lengths in the RFTS study ( maternal age , P = 0 . 91; parity groups compared to 0–1 , P = 0 . 26 and 0 . 74 , respectively ) , we included them in the model to be consistent with the analysis in the Hutterites . Variants that were present in the Hutterites in each of the two associated regions but were not genotyped in the women with recurrent pregnancy loss who were included in the eQTL study were imputed in those women for a second stage eQTL mapping . We used whole genome sequences from 100 European American individuals as the reference genotypes for imputation [25] . Before imputation , variants with minor allele frequencies <1% or genotype call rates <95% were removed and variants on the reverse stand were flipped to the forward strand using PLINK [62] . To decrease computation time , we pre-phased the haplotypes in the reference genomes using Mach [71] . We then imputed genotypes in the women in the eQTL study using Minimac [72] . To avoid using SNPs with low imputation quality , we removed SNPs with an estimated R2 less than 0 . 5 before performing the eQTL mapping . We were able to impute or directly genotype 117 of the 7 , 062 variants in the two associated regions . The eQTL mapping study in women with recurrent pregnancy loss was approved by the University of Chicago IRB ( protocol number 14599B ) ; all women gave written consent . The prospective study in the Hutterites was approved by the University of Chicago IRB ( protocol number 5444 ) ; all participants gave written consent . The RFTS study was approved by Vanderbilt Human Research Protection Program ( VHRPP ) and Vanderbilt IRB ( protocol numbers 070037 and 100396 ) ; all participants gave both verbal and written consent . | Little is known about the genetics of female fertility . In this study , we addressed this gap in knowledge by first searching for genetic variants that regulate gene expression in uterine endometrial cells , and then testing those functional variants for associations with the length of time to pregnancy in fertile women . Two functional genetic variants were associated with time to pregnancy in women after correcting for multiple testing . Those variants were each associated with the expression of genes in the HLA region , HLA-F and TAP2 , which are have not previously been implicated female fertility . The association between HLA-F and TAP2 genotypes on the length of time to pregnancy was replicated in an independent cohort of women . Because HLA-F and TAP2 are involved in immune processes , these results suggest their role in specific immune regulation in the endometrium during implantation . Future studies will characterize these molecules in the implantation process and their potential as drug targets for treatment of conditions related to implantation failure . | [
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] | 2016 | Expression Quantitative Trait Locus Mapping Studies in Mid-secretory Phase Endometrial Cells Identifies HLA-F and TAP2 as Fecundability-Associated Genes |
Fragile X syndrome ( FXS ) is the most frequent inherited form of mental retardation . The cause for this X-linked disorder is the silencing of the fragile X mental retardation 1 ( fmr1 ) gene and the absence of the fragile X mental retardation protein ( Fmrp ) . The RNA-binding protein Fmrp represses protein translation , particularly in synapses . In Drosophila , Fmrp interacts with the adenosine deaminase acting on RNA ( Adar ) enzymes . Adar enzymes convert adenosine to inosine ( A-to-I ) and modify the sequence of RNA transcripts . Utilizing the fmr1 zebrafish mutant ( fmr1-/- ) , we studied Fmrp-dependent neuronal circuit formation , behavior , and Adar-mediated RNA editing . By combining behavior analyses and live imaging of single axons and synapses , we showed hyperlocomotor activity , as well as increased axonal branching and synaptic density , in fmr1-/- larvae . We identified thousands of clustered RNA editing sites in the zebrafish transcriptome and showed that Fmrp biochemically interacts with the Adar2a protein . The expression levels of the adar genes and Adar2 protein increased in fmr1-/- zebrafish . Microfluidic-based multiplex PCR coupled with deep sequencing showed a mild increase in A-to-I RNA editing levels in evolutionarily conserved neuronal and synaptic Adar-targets in fmr1-/- larvae . These findings suggest that loss of Fmrp results in increased Adar-mediated RNA editing activity on target-specific RNAs , which , in turn , might alter neuronal circuit formation and behavior in FXS .
Fragile X syndrome ( FXS ) is the most common single-gene inherited neurodevelopmental disorder causing mental retardation . This disorder is characterized by an array of behavioral and cognitive disabilities , including autism , anxiety , epileptic seizures , hyperactivity , attention deficits , and mild craniofacial abnormalities [1 , 2] . The cause for FXS is a genetic loss of fragile X mental retardation protein ( Fmrp ) due to transcriptional silencing of the fragile X mental retardation 1 ( fmr1 ) gene . In the 5'-untranslated region ( 5’-UTR ) of fmr1 , an expansion of more than 200 CGG trinucleotide repeats results in abnormal DNA hypermethylation and diminished mRNA expression [3] . Fmrp is predominately a cytoplasmic protein and consists of two ribonucleoprotein K homology ( KH ) domains and a GAR/RGG ( glycine-arginine-rich ) box . It is an RNA-binding protein that is essential for the function of the central nervous system [2–4] ( CNS ) . In neurons , it regulates neurite transport of a subset of mRNAs and inhibits protein translation by blocking both initiation and elongation . In synapses , upon metabotropic glutamate receptor ( mGluR ) stimulation , the translation of Fmrp-targeted mRNAs is inhibited , allowing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptor trafficking and synaptic transmission [3] . Recent findings showed that Fmrp also affects other aspects of post-transcriptional gene regulation , including the stability of certain transcripts , activity-dependent mRNA transport , and RNA interference pathways [5] . Thus , Fmrp regulates the translation and trafficking of synaptic proteins , and subsequently affects synaptic plasticity and brain function . The structure and sequence of the fmr1 gene is conserved from invertebrates to mammals , allowing the development of various animal models to study the mechanism of the syndrome [6–10] . Fmr1 knockout ( KO ) and conditional KO mice mimics the typical characteristics of FXS patients , including molecular , electrophysiological , neurological , and behavioral defects [7 , 8 , 11] . The imaging of KO mice brains revealed structural abnormalities of dendritic spines and changes in synaptic protein distribution that affect synaptic formation and plasticity [4 , 12] . In Drosophila , several fmr1 mutants demonstrated an array of behavioral and developmental defects . As in the case of mammals , the morphology and connectivity of the synapses were altered [13] . In addition to the mouse and fly models , a zebrafish fmr1 mutant ( fmr1-/- ) model for FXS was established [6] . The zebrafish is a transparent vertebrate that is suitable for genetic manipulations and live imaging of a simple and evolutionarily conserved CNS [14 , 15] . Consistent with the expression pattern of fmr1 orthologs in mammals , fmr1 expression in zebrafish is enriched in the brain [16] . Although an apparent phenotype was not observed in fmr1-/- larvae [6] , adult fmr1-/- zebrafish demonstrated hyperlocomotor activity , impaired anxiety , and altered learning behavior [17 , 18] . Furthermore , reduced long-term potentiation and enhanced long-term depression was found in the telencephalon of adult fmr1-/- zebrafish , indicating deficient synaptic plasticity [18] . Thus , accumulating evidence points to a specific role of Fmrp in regulating synaptic proteins that mediate the structure and activity of neuronal circuits; however , the molecular mechanisms of this process remain unclear . An intriguing mechanism that may be involved in FXS is adenosine-to-inosine ( A-to-I ) RNA editing . In drosophila , dFMR1 interacts and modulates the activity of adenosine deaminase acting on RNA ( Adar ) enzyme [13] . Adar acts on double-stranded pre-mRNA structures and deaminates A into I , which is recognized by the cell’s splicing and translational machineries as an equivalent to guanosine ( ‘G’ ) , thus the A-to-G recoding process alters the mRNA coding sequences and enhances protein diversity [19] . In addition , RNA editing can affect alternative splicing [20] as well as RNA expression and stability [21] , and Adar is a negative regulator of circular RNA ( circRNA ) formation [22] . Notably , deficient Adar can cause severe neurological defects or lethality in Drosophila , zebrafish , and mice [23–25] . Furthermore , key Adar target-sites are found in synaptic genes [26] , and functional studies have shown that mild alterations in RNA editing levels in AMPA receptor subunits ( GluRs ) affect channel activity and synaptic plasticity [26 , 27] . However , although Adar-mediated RNA editing is linked to neurological alterations , the significance of this process in neurological disorders , such as FXS , and the global Fmrp-Adar mediated effect on the transcriptome , particularly neurological genes , are unclear . Here , the live imaging of neurites and synapses , as well as the video tracking of behavior , revealed abnormalities in neuronal circuit formation and locomotor activity in fmr1-/- larvae . Furthermore , thousands of clustered RNA editing sites were identified in the zebrafish transcriptome , and an interaction between Fmrp and Adar was determined . These findings , in addition to high-throughput microfluidic RNA editing quantification , suggest that RNA editing plays a role in the mechanisms that mediate neuronal circuit formation and behavior in FXS .
The RNA-binding protein Fmrp regulates the expression levels of a specific set of target proteins in humans [28] . Since an apparent morphological phenotype was not previously detected in fmr1-/- zebrafish larvae [6] ( Fig 1A ) , and in order to verify that the function of Fmrp in zebrafish is conserved with mammals , we sought to test the expression levels of three Fmrp target genes , mtor , sash1 , and talin1 , which showed elevated protein expression levels in FXS human brains [28] . mTor plays a key role in controlling protein homeostasis , cell survival , and synaptic density [29 , 30] . Sash1 is a member of the SLY family of signal adaptor proteins , and is essential in intracellular signal transduction [31] . Talin1 acts as an integrin-binding cytoplasmic adaptor that is a central organizer of focal adhesions [32] . To characterize the spatial expression pattern of mtor , sash1 , and talin1 in zebrafish larvae , whole-mount in situ hybridization ( ISH ) was used . Similar to fmr1 , all three genes were widely expressed in the brain ( Fig 1B–1D ) . In order to quantify the expression levels of these genes , quantitative reverse transcription polymerase chain reaction ( qRT-PCR ) was performed in 6 days post fertilization ( dpf ) fmr1-/- and wild-type ( WT ) larvae . The mRNA levels of mtor and sash1 increased by approximately 2 . 5- and 2-fold , respectively , in fmr1-/- compared with WT larvae [mtor ( WT = 1 . 03428 , fmr1-/- = 2 . 73493 ) , p<0 . 05; sash1 ( WT = 1 . 0617 , fmr1-/- = 2 . 1338 ) , p<0 . 05 , Fig 1E] . These results show that loss of Fmrp results in elevated mRNA levels of target genes . Since mtor showed the highest increase in mRNA expression levels , we monitored its protein levels , specifically in the brain of fmr1-/- and WT zebrafish . Western blot analysis revealed an increase in mTor ( 250 kDa ) protein levels in fmr1-/- compared with WT brains , while the protein levels of actin ( 43 kDa ) were similar in both genotypes . Thus , similar to the case of mammals , the loss of Fmrp increases the expression levels of the mTor protein . Taking into account previous characterizations of physiological and behavioral deficiencies in fmr1-/- adult zebrafish [17 , 18] , these results further establish the fmr1-/- larvae as a valid model for the study of the genetic and neurological mechanisms of FXS . In the absence of Fmrp , FXS patients demonstrate hyperactivity , reduced anxiety-related behavior , and memory deficits [2] . Similarly , hyperlocomotor activity was observed in fmr1 KO mice and fmr1-/- adult zebrafish [18] . To monitor locomotor activity in fmr1-/- larvae , high-throughput behavioral systems were used . Larvae were kept under light/dark conditions ( LD , light 14 h: dark 10 h ) , and the rhythmic activity of 6 dpf fmr1-/- ( n = 34 ) and WT ( n = 30 ) larvae was monitored during the day and night . While both genotypes exhibited rhythmic locomotor activity that peaked during the day ( Fig 2A ) , fmr1-/- larvae exhibited a 36% and 37% increase in locomotor activity during both day ( WT = 6 . 961 cm/min , fmr1-/- = 9 . 483 cm/min , p<0 . 0001 ) and night ( WT = 6 . 499 cm/min , fmr1-/- = 8 . 888 cm/min , p<0 . 0001 ) , respectively ( Fig 2B ) . These results indicate overall hyperlocomotor activity in fmr1-/- larvae , establishing that fmr1-/- larval behavior complies with the typically observed FXS phenotype . The response of fmr1-/- larvae to light and dark transition states was tested by exposing 6 dpf larvae to three cycles of alternating 30 min periods of light and dark during the day . The larvae responded to light and dark transitions with robust changes in locomotor activity ( Fig 2C ) . Notably , during the light period , fmr1-/- larvae increased their locomotor activity by 28% compared with WT larvae ( WT , n = 177 , 10 . 410 cm/min; fmr1-/- , n = 179 , 13 . 358 cm/min; p<0 . 005 , Fig 2D ) , confirming that loss of Fmrp results in hyperlocomotor activity . Furthermore , behavioral analysis during the light-to-dark and dark-to-light transitions ( comparing activity 5 min before and 5 min after the transition state ) showed that while the response to dark stimuli was unaffected , the fmr1-/- larval response to light stimuli was adverse to that of the WT larvae . Notably , this tendency was repeated in all three light/dark cycles . Altogether , these results show hyperlocomotor activity and altered behavioral response to light stimuli in fmr1-/- larvae . These findings suggest that the loss of Fmrp affects locomotor activity , possibly due to the misregulation of synaptic proteins by Fmrp . In the absence of an adequately functioning Fmrp , human FXS patients demonstrate severe cognitive and behavioral deficiencies . Taking into account these symptoms and the hyperlocomotor activity found in fmr1-/- larvae , we sought to resolve whether axon morphology and structural synaptic density of cholinergic motor neurons are affected in fmr1-/- live larvae . The mnx1X3 enhancer [33] was used to fluorescently label motor neurons . The constructs mnx1X3:GAL4 and uas:memYFP were co-injected into one-cell-stage fmr1-/- and WT embryos . At 2 dpf , mnx1X3:GAL4/uas:mem:YFP positive embryos were sorted out and single motor neurons were imaged ( Fig 3A ) . Image analysis revealed that the total length of the axon arbors and the number of branches ( Fig 3B and 3C ) increased by 59% and 120% , respectively , in fmr1-/- compared with WT larvae ( WT; n = 17 , 208 . 050 μm , 6 . 823 branches; fmr1-/-; n = 27 , 331 . 034 μm , 15 branches; p<0 . 05; Fig 3D and 3E ) . These results suggest that Fmrp acts to stabilize hyper-axon arborization in motor neurons . Since synaptogenesis guides the growth and branching of axonal arbors [34] , the increased axon branching in fmr1-/- larvae could attest to a deficiency in the number and distribution of synapses . Therefore , the mnx1X3:GAL4 , uas:SYP-EGFP , and uas:tRFP constructs were co-injected into fmr1-/- and WT embryos . At 2 dpf , synapse density was quantified by assessing the number of puncta along the axonal arbor of single motor neurons in both genotypes ( Fig 3F and 3G ) . We found an increase of 53% ( WT , n = 11 , 0 . 490 puncta/micron; fmr1-/- , n = 17 , 0 . 752 puncta/micron; p<0 . 05; Fig 3H ) in synaptic density in mnx1X3:GAL4/uas:tRFP/uas:SYP-EGFP/fmr1-/- compared with mnx1X3:GAL4/uas:tRFP/uas:SYP-EGFP/WT embryos . These results show that loss of Fmrp increases total synaptic density in the axons of spinal motor neurons . Given that Fmrp is an inhibitor of synaptic protein translation [35] , these findings suggest that Fmrp-dependent inhibition of synaptic proteins regulates axonal arborization and structural synaptic changes in cholinergic motor neurons . The hyperlocomotor activity and disrupted behavioral response to dark-to-light transition states in fmr1-/- larvae , coupled with the broad expression of the fmr1 gene , suggest that deficiencies in neuronal circuit formation are not restricted to motor neurons and may also be present in sensory neurons . In relatively early stages of zebrafish development , Rohon-Beard ( RB ) sensory neurons are the primary sensory spinal neurons [36] . They are located in the dorsal spinal cord and project axons toward broad areas in the periphery [37] . In order to test the role of Fmrp in RB axons , we imaged single RB neurons using the huc pan-neural promoter [38 , 39] in live embryos . The constructs huc:GAL4 and uas:memYFP were transiently expressed in fmr1-/- and WT embryos and , at 2 dpf , positive embryos were sorted out and imaged ( Fig 3I and 3J ) . We found that total arbor length and the number of branches increased by 73% and 92% , respectively , in fmr1-/- compared with WT embryos ( WT , n = 9 , 979 . 365 puncta/micron , 26 . 333 branches; fmr1-/- , n = 10 , 1694 . 455 puncta/micron , 50 . 7 branches; p<0 . 05; Fig 3K and 3L ) . To quantify the number of synapses in live embryos , the huc:GAL4 , uas:SYP-EGFP , and uas:tRFP constructs were co-injected into fmr1-/- and WT one-cell-stage embryos . At 2 dpf , synapse density was quantified in the axonal arbor of single RB neurons in both genotypes ( WT , n = 10; fmr1-/- , n = 13; Fig 3M–3O ) . Imaging of synapses in these neurons revealed that synaptic density did not vary between fmr1-/- and WT larvae . These results show that Fmrp regulates axon branching in RB neurons that mediate sensory response . Since Fmrp is widely expressed in the brain and spinal cord , and mGluR activation regulates Fmrp function , we tested whether loss of Fmrp affects structural synaptic density in glutamatergic neurons . We monitored synapse density in the glutamatergic hypocretin/orexin ( Hcrt ) neurons . These hypothalamic neurons innervate downstream glutamatergic nuclei , such as the locus coeruleus , and regulate feeding , reward , sleep , and wake [40] . The hcrt promotor was used to fluorescently label Hcrt axons [14] . The construct hcrt:SYP-EGFP was injected into one-cell-stage fmr1-/- and WT embryos . At 2 dpf , hcrt:SYP-EGFP positive embryos were sorted out and single axons , projecting dorsocaudally toward the spinal cord , were imaged ( Fig 3P–3R ) . Image analysis of an fmr1-/- embryo revealed a 30% increase in synaptic density compared with a WT embryo ( WT , n = 8 , 0 . 1875 puncta/micron; fmr1-/- , n = 9 , 0 . 2444 puncta/micron; p<0 . 05; Fig 3S ) . Altogether , these results show that loss of Fmrp increases synapse density along the axons of glutamatergic and cholinergic neurons in the brain and spinal cord . The mechanism by which the RNA binding protein Fmrp regulates axonal and synaptic structural changes in zebrafish is unclear . Recent findings on Drosophila showed that Fmrp modulates Adar enzyme activity [13] , which , in turn , serves as a modulator of neuronal excitability and function as well as gene expression [26 , 41] . Since Adar-mediated A-to-I editing was shown to affect the function of synaptic proteins [26 , 42] , we hypothesized that Adar expression and activity will be altered in fmr1-/- larvae . Initially , we sought to genomically characterize the zebrafish Adar family members . The Adar enzymes are highly conserved in metazoans , although the number of genes and isoforms varies between species [43] . Mammalian genomes encode three Adars: Adar and Adarb1 ( Adar1 and Adar2 , respectively ) which are both catalytically active , and Adarb2 ( Adar3 ) , which is considered to be catalytically inactive [44] . An alternative promotor at the amino terminus of human Adar1 leads to the formation of two defined isoforms , commonly known as Adar1-p150 and Adar1-p110 . While Adar1-p150 is localized in the nucleus and the cytoplasm , the shorter Adar1-p110 isoform is constitutively active and localized mainly in the nucleus [45] . Genomic analysis revealed that the zebrafish genome encodes four adar genes [43 , 46] . A phylogenetic reconstruction of the zebrafish and human Adar protein sequences revealed that zebrafish Adar proteins converge into three distinct clusters and that human Adar2 has two zebrafish orthologs ( Fig 4A ) . Notably , all zebrafish Adar enzymes share common domain architecture consisting of a variable number of amino-terminal dsRBDs and a carboxy-terminal catalytic deaminase domain ( Fig 4B ) . To determine the spatial expression of adar genes , we performed whole-mount ISH in WT zebrafish . At 2 dpf , all adars were primarily expressed in the brain and spinal cord , while at 6 dpf , adar transcripts were strongly expressed in the brain ( Fig 4C–4R ) . Although evidence of an interaction between Fmrp and Adar has been shown in Drosophila [13] , it is not known if these proteins associate in vertebrates . To explore the biochemical interaction between Fmrp and Adar2a proteins , co-immunoprecipitation ( Co-IP ) was performed . HEK293T cells were transfected with constructs that expressed the zebrafish Adar2a and Fmrp proteins tagged with EGFP and MYC , respectively ( Fig 4S ) . Extracts from HEK293T cells expressing EGFP-Adar2a and MYC-Fmrp ( Fig 4T ) were used for actin , MYC , and EGFP pulldowns , and eluates were subjected to SDS-PAGE electrophoresis ( Fig 4S ) . We found that Fmrp was purified with Adar2a and vice versa . In contrast , actin did not pull down nor purify with either Fmrp or Adar2a ( Fig 4S ) . These results indicate that zebrafish Fmrp and Adar2a proteins interact . Intriguingly , loss of Fmrp in FXS may affect the expression levels of Adar mRNA and protein . Previous work showed that FMRP can bind to many mRNA sequences via two major RNA recognition elements ( RREs , conserved sequences: ACUK and WGGA ) [28 , 35 , 47–49] . Specifically , 75 RRE sequences were mapped to adar1 [47 in coding sequences ( CDS ) and 28 in 3’-UTR] and 4 sequences were mapped to adar2 ( 2 in CDS and 2 in 3’-UTR ) . Using computational sequence homology , we searched for RREs in the CDS of zebrafish adar1 and adar2 genes . We found 42 sequences that exhibit 100% homology between the two species . Of them , 40 sequences were mapped to the CDS of adar1 , and 2 sequences were mapped to the CDS of adar2 ( Fig 4U ) . Thus , we determined whether Fmrp binds to adar1 mRNA using RNA immunoprecipitation assays . HEK293T cells were transfected with the zebrafish MYC-Fmrp , and anti-MYC as well as anti-Actin antibodies were used to pull down specific protein-mRNA complexes . Following total RNA extraction from the cells , cDNA was amplified and adar1 was identified in cells precipitated with an anti-MYC antibody but not in cells precipitated with an anti-Actin antibody . These results show that Fmrp protein can bind adar1 mRNA ( Fig 4V ) . In order to quantify the effect of Fmrp on adar mRNA expression levels , a qRT-PCR was performed in 6 dpf fmr1-/- and WT larvae . All four adar genes showed increased expression levels in fmr1-/- larvae compared with WT larvae ( Fig 4W ) . The mRNA levels of adar1 , adar2a , adar2b , and adar3 increased by 3 . 7 , 2 . 2 , 1 . 5 , and 1 . 2 fold , respectively ( adar1 , WT = 0 . 834 , fmr1-/- = 3 . 102 , p<0 . 005; adar2a , WT = 1 . 03667 , fmr1-/- = 2 . 2900 , p<0 . 05; adar2b , WT = 0 . 9752 , fmr1-/- = 1 . 48804 , p<0 . 05; and adar3 , WT = 1 . 0098 , fmr1-/- = 1 . 24590 , p<0 . 05 , Fig 4W ) . Furthermore , Western blot analysis showed a 30% increase in Adar2 protein expression levels in fmr1-/- brains compared with WT brains ( Fig 4X ) . These results show that Fmrp interacts with adar1 mRNA and Adar2a protein , and that loss of Fmrp leads to increased expression of the adar genes and the Adar2 protein . The location of known RNA editing sites in zebrafish is limited to a handful of sites [24 , 50 , 51] . To study whether RNA editing is altered in FXS , we initially assessed the global extent of RNA editing in zebrafish by analyzing transcriptome data . We probed RNA-seq datasets consisting of ~1 . 8Gb , strand-specific , 76-bp paired-end reads from a developmental study that contained 17 samples covering 8 different developmental stages [52] ( GSE32898 ) . RNA-seq data were analyzed by a bioinformatic pipeline aimed at detecting dense clusters of HE RNA sites [53] . This approach is most suitable for detecting editing in cases lacking genomic information derived from the same sample . A total of approximately 350 , 000 DNA-RNA mismatches were identified ( S1 and S2 Tables ) . We calculated the prevalence of all possible 12 DNA nucleotide substitutions and found massive A-to-G mismatch enrichment ( 93% , Fig 5A , S1 Table ) in intergenic regions , introns , UTRs , and CDS ( Fig 5B ) . This large enrichment strongly suggests that these substitutions are the result of Adar activity . Next , we examined whether the nucleotide sequence context of the detected RNA editing sites complies with the sequence motif typical of Adar targets . Mammalian Adar1 and Adar2 enzymes have an apparent preference for RNA uracil ( ‘U’ ) at position -1 located 5’ to the editing target site , while guanine ( ‘G’ ) is the least constructive option [54] . Analyzing the sequence of adjacent zebrafish , HE sites revealed that , as in the case of mammals , ‘G’ is the underrepresented nucleotide at position -1 ( Fig 5A ) . Since this position was shown to be imperative for RNA editing , our finding supports that the A-to-G mismatches are RNA editing sites . Further analysis of the genomic locations of the editing sites in humans and zebrafish revealed that the majority of the clustered sites ( 76% ) are located in repeat sequences , mainly rich intergenic regions . Of these , we found that over 25% of all editing sites are located in the DNA hAT repeat family , which occupies about 8% of the zebrafish genome ( Fig 5C ) . Using the mfold tool [55] , we illustrated that both members of the hAT repeat family , ANGEL and TDR19 , fold into stable dsRNA structures that are typical Adar substrates ( Fig 5D ) . Altogether , these findings profile a genome-wide RNA editing map in zebrafish that includes ample RNA editing sites in hundreds of Adar target genes . The elevated levels of adar mRNA expression and Adar2 protein observed in fmr1-/- zebrafish ( Fig 4 ) , along with the identification of a multitude of clustered editing sites in the zebrafish transcriptome ( Fig 5 ) , provided the basis to examine whether the loss of Fmrp affects RNA editing levels in target genes . Until recently , in order to identify and quantify a single RNA editing site , traditional saturated PCR amplification of a single locus was used . The introduction of RNA-seq techniques led to the massive identification of new RNA editing sites; however , quantification of minute alterations in the levels of RNA editing is fairly limited due to typical low depth coverage of specific loci and the large dynamic range of RNA expression [56 , 57] . In this study , we focused on evolutionarily conserved RNA editing targets between mammals and zebrafish . We utilized a novel microfluidic-based multiplex PCR ( mmPCR ) approach to simultaneously amplify 48 target regions that contain the preselected RNA editing target sites across a 48-sample panel ( Fig 6A , Materials and Methods ) [58] . PCR products were index-tagged and subjected to deep sequencing , enabling single-molecule resolution of RNA editing levels in 6 dpf fmr1-/- and WT larvae ( n = 10 batches of larvae per genotype ) . Before analyzing the A-to-G ratio per site , we ran a correlation test ( S1 Fig ) . We found that the correlation between the number of reads taken from fmr1-/- and WT larvae is consistently high and does not affect editing levels ( p<0 . 0001 , two-tailed Pearson r = 0 . 978; S1 Fig ) . The zebrafish genome is highly polymorphic and does not consistently match the database ( genome assembly Zv9 , GCA_000002035 . 2 ) ; thus , for sequence comparison , we also sequenced and quantified genomic DNA from the same animals using the same targeted re-sequencing approach . This enabled a direct comparison of DNA and RNA sequences from the same source . In order to avoid quantification of genomic , single-nucleotide polymorphisms ( SNPs ) and sequencing errors , we set specific thresholds using the following criteria: i ) sample incidence rate—target regions that were successfully captured in equal or more than 75% of the same genotype samples; ii ) target site coverage—target regions with coverage depth of at least 400 reads; and iii ) A/G ratio—an editing site with a ratio of [A/ ( A+G ) ] of at least 2% . Only target sites that met these criteria were further analyzed . Out of the initial 70 predicted RNA editing sites included in our target set , 28 target sites met all criteria . An additional 35 novel and previously untargeted editing sites , located in close proximity to the targeted sites in the same genomic region , were also detected during data analysis . Of these , 24 sites were RNA editing sites that met the selection criteria ( ‘off-target’ sites , S3 Table ) . Finally , after applying all criteria , a cohort of 52 novel RNA editing sites was quantified and characterized in both fmr1-/- and WT larvae ( S2 Table , Fig 6B ) . To validate the results , several RNA editing sites were sequenced using the customary Sanger method , and chromatogram analysis confirmed the presence of RNA editing sites ( Materials and Methods , S2 Fig ) . Final analysis of RNA editing results showed fmr1-dependent differential RNA editing levels in ten sites ( Fig 6C ) . These ten evolutionarily conserved RNA editing targets were located in seven genes . Four of these genes were synaptic genes: L-type calcium channel ( cacna1da ) , ionotropic glutamate receptor kainate 2 ( grik2 ) , ionotropic glutamate receptor AMPA receptor subunit 4b ( gria4b ) , and ionotropic glutamate receptor AMPA receptor subunit 3a ( gria3a ) . Notably , gria3a showed differential RNA editing in four sites ( Fig 6C , S4 Table ) . These results suggest that altered RNA editing , specifically in synaptic genes , might modulate synaptic structure and function in fmr1-/- larvae . The changes in editing observed in whole fmr1-/- larvae were relatively mild . However , these changes may be larger in specific tissues . Therefore , we amplified and sequenced the genomic DNA ( gDNA ) and cDNA of three representative RNA editing sites , specifically from fmr1-/- and WT adult brains . Sequencing revealed that the difference in RNA editing levels between genotypes in gria3b and grik2 genes , which showed no change when sampling whole larvae , increased to 14% and 8% , respectively . In addition , we quantified the levels of RNA editing in acetylcholinesterase ( ache ) , which is a key enzyme in cholinergic synaptic transmission , and found an 18% increase in RNA editing levels in fmr1-/- compared with WT brains ( Fig 6D ) . These results show Fmrp-dependent tissue-specific changes of RNA editing levels in brains and suggest that these relatively large changes may be present in other specific tissues . Next , we employed a second analysis aimed to transform the quantification of site-specific editing levels into the complete formation of mRNA variants , which more accurately reflect the effect of RNA editing on transcriptome diversity . Analyzing all the editing sites located adjacently on a single molecule enabled us to determine the abundance of each mRNA sequence within the gene mRNA repertoire . This analysis showed that gria2a and gria3a contain multiple RNA editing sites on the same amplicon . When analyzed separately , insignificant , differential editing levels were found between fmr1-/- and WT larvae ( S4 Table ) . However , when we analyzed the occurrence of RNA editing in conjunction with mRNA transcript formation , we identified small but significant changes in the relative abundance of the various mRNA transcripts generated by sequence recoding in fmr1-/- and WT larvae ( S5 Table , Fig 6E and 6F ) . Analysis of the editing pattern of gria2a , which contains two adjacent editing sites ( chr1:20124223 and chr1:20124224 ) showed a 2 . 6% increase in the genomically encoded , unedited form ( LR ) in WT compared with fmr1-/- larvae ( S5 Table , Fig 6E: unedited form in WT = 43 . 2%∓1% , fmr1-/- = 40 . 6%∓0 . 9% , p<0 . 05 ) . In gria3a , which also contains two adjacent editing sites ( chr5:25066153 and chr5:25066156 ) the results exhibited a 1 . 6% decrease in the relative abundance of the fully edited version of AV formed by double editing in both T442A and I423V sites in WT compared with fmr1-/- larvae ( S5 Table , Fig 6F; double edited form in WT = 9 . 8%∓0 . 4% , fmr1-/- = 11 . 3%%∓0 . 6% , p<0 . 05 ) . This analysis shows that in clustered sites , even small changes in the editing levels can be manifested into different mRNA transcripts that can reshape synaptic proteins and thus contribute to functional diversity . Altogether , these results suggest that RNA editing sites , particularly in synaptic genes , are diversely regulated by Fmrp-mediated Adar activity . These molecular modifications may affect synaptic structure , axon processing , and the function of specific neuronal circuits that regulate behavior .
The epigenetic , neurological , and behavioral findings in the zebrafish FXS model suggest that Fmrp-mediated RNA editing plays a role in the molecular mechanisms that regulate structural plasticity of neuronal circuits that regulate behavior . In support of this model , Fmrp and Adar2a biochemically interacted , and loss of Fmrp increased the mRNA expression levels of the adar enzymes as well as Adar2 protein levels . Furthermore , we found that the zebrafish genome contains thousands of RNA editing sites , and that RNA editing levels are altered in conserved synaptic and neuronal transcripts in fmr1-/- zebrafish . For example , in accordance with the increase of RNA editing levels in glutamatergic and cholinergic genes , synaptic density was increased in the glutamatergic Hcrt neurons and the cholinergic spinal motor neurons in fmr1-/- zebrafish . Considering the role of these neurons in locomotor-activity regulation , the hyperlocomotor activity of fmr1-/- larvae may be linked to increased synaptic density in these neurons . Altogether , these results suggest an intricate interaction between Fmrp , Adars , and RNA editing in target genes that may affect the neurological symptoms of FXS patients . However , further research is needed in order to causally link RNA editing in specific targets with neuronal circuit-specific deficiencies in an animal model for FXS . In order to understand the mechanism of FXS , we used fmr1-/- zebrafish . In humans [28] , loss of Fmrp increased the expression levels of the inactive mTor protein . In mice , the levels of expression of the active form phospho-mTor increased , while the expression of the inactive form did not change [59] . As in the case of humans , in zebrafish , the loss of FMRP results in the increased expression of the mTor protein . The effect on phospho-mTor requires additional investigation . Taking into account the role of mTor in regulating structural , synaptic plasticity [30] , it may play a role in the mechanism that regulates the assembly of neuronal circuits that regulate behavior in zebrafish and FXS . The behavior of fmr1-/- zebrafish was previously studied only in adult zebrafish [17 , 18] . However , at larval stages , the zebrafish model provides unique high-throughput and transparency advantages . Thus , we studied the role of fmr1 in regulating neural circuit formation and behavior . High-throughput , video-tracking behavior systems were used to monitor the rhythmic locomotor activity during both day and night . As in the case of mammals and adult zebrafish [17 , 18] , the larvae were hyperactive . Furthermore , under a 30-min alternating light and dark cycle , the larvae were hyperactive and their response to dark-to-light transition was altered . This hyperlocomotor activity is distinctive , since loss of gene function and neuron alterations typically result in reduced locomotor activity [60] . An intriguing explanation for the hyperactivity of fmr1-/- larvae could be that Fmrp mediates structural synaptic plasticity that affects behavior . A key role of Fmrp is inhibition of synaptic protein translation [61] . Thus , the loss of Fmrp may result in hyperactivity due to the augmented translation of synaptic proteins that interferes with synaptic degeneration and axon pruning processes . Supporting this hypothesis , the imaging of cellular and presynaptic fluorescent markers in live zebrafish showed increased axonal branching in both spinal motor and RB sensory neurons , and increased synaptic density in the axons of the Hcrt and motor neurons in fmr1-/- larvae . Since the spinal motor neurons regulate locomotor activity and the Hcrt neurons regulate arousal [40 , 62] , the structural axonal and synaptic abnormalities found may induce the increased locomotor activity in fmr1-/- larvae . Furthermore , the abundance of immature dendritic spines is one of the neuroanatomical hallmarks of FXS in both humans and Fmr1-KO mice [12 , 63] . However , the role of Fmrp in regulating the development of axonal and presynaptic structures has yet to be fully characterized . In the cortex of FMR1-KO mice , the axonal arbors are diffused and demonstrate reduced connection probability and strength [64] . In Drosophila , Fmrp limits axon growth and facilitates activity-dependent pruning of axonal branches [65 , 66] . Thus , our findings in zebrafish support the findings in Drosophila and mice , and show that a lack of Fmrp results in excessive axonal processing and synaptic structures in neuronal circuits that induce locomotor activity . The ability of a neuron to modulate synaptic protein composition and function relies on the fine-tuning of transcription and translation processes in both the nucleus and cytoplasm , as well as in cellular transport along the neurites . The RNA-binding protein Fmrp regulates translation and mRNA transport of synaptic genes . Another RNA-binding protein , the Adar enzyme , acts on an array of RNA molecules and expands protein diversity beyond that encoded by the genome . It also interferes with gene expression and coordinates miRNA biosynthesis to fine-tune neuronal plasticity and brain functions [19] . In Drosophila , Fmrp and Adar interact in the nucleus to regulate RNA editing . Furthermore , Adars act downstream to Fmrp to regulate synaptic morphology of the neuromuscular junction [13] . Similarly , we found that zebrafish Fmrp and Adar2a biochemically interact . Moreover , zebrafish Fmrp binds adar1 mRNA . Notably , while in Drosophila Fmrp KO the levels of the single Adar protein [43] do not change [13] , in fmr1-/- zebrafish , adar mRNAs and Adar2 protein expression levels are increased . These results suggest that Fmrp inhibits either Adar mRNA , protein or both , and further research is needed in order to elucidate the specific pathways . In either of the mechanisms , Fmrp likely affects Adar activity , which results in altered RNA editing levels in Adar-target genes . Furthermore , since a growing body of evidence links Adar activity to a broad array of the cell’s regulatory mechanisms , including the enhancement of A-to-I editing in the flanking intronic sequences of circRNAs [22] , the potential increase of Adar activity in fmr1-/- larvae suggests a broad molecular modification of the transcriptome . In order to quantify RNA editing in zebrafish , we first characterized a genome-wide profile of clustered RNA editing sites . The focus on clustered RNA editing sites allows an accurate evaluation of the prevalence of RNA editing even in the absence of genomic DNA data , and can be used as an indicator of overall Adar activity . Analysis of the zebrafish transcriptome showed that 0 . 2% of the total aligned reads were RNA editing sites ( S1 Table ) , while the same analysis performed on RNA-seq data from the prefrontal cortex of healthy humans showed that 0 . 19% of the total reads were RNA editing sites [53] . Thus , as in the case of humans , these results demonstrate an extensive RNA editing process in zebrafish . Whether the loss of Fmrp affects RNA editing in vertebrates was unknown . The Fmrp-Adar zebrafish protein interaction and the detection of hundreds of RNA editing sites within zebrafish genes provided the groundwork needed for RNA editing quantification in dozens of potential zebrafish target genes . We selected evolutionarily conserved Adar-targets in the CDS of annotated genes and used the mmPCR system to quantify RNA editing in those genes . The advantage of this system is its ability to simultaneously detect and accurately quantify A-to-G RNA editing events across multiple samples in a single experiment , independent of gene expression levels . We found Fmrp-dependent changes in RNA editing in several neuronal genes , particularly in glutamate receptors . Notably , although the RNA editing levels increased in most sites , the levels decreased in one specific site ( cog3 chr9:55875506 ) . This is also the case in the mutant fmr1 fly , where the RNA editing levels increase in some genes and decrease in others [13] . The analysis of clustered RNA editing sites in gria2a showed that the overall prevalence of the mRNA containing the R/G recoding is higher in fmr1-/- larvae . Interestingly , this R/G editing site is located 2 nucleotides upstream to a splice site , which contributes to the generation of the Flip/Flop isoforms that modulate the kinetic properties of AMPA receptor channels , thus determining the time course of desensitization and re-sensitization [67] . Since the G forms of AMPA receptors have a faster recovery rate from desensitization compared to R forms [68] , the increase in RNA editing at the R/G site may induce the synaptic response to glutamate . Thus , the changes in RNA editing in glutamate receptors may affect synaptic strength and morphology as well as cause hyperlocomotor activity in fmr1-/- larvae . Although the changes in editing observed in whole fmr1-/- larvae were mild , they were significant in several cases and might have been larger had the analyses been performed on a specific tissue or cell population . Indeed , quantification of RNA editing , specifically in the brain , showed greater changes in RNA editing levels . In addition , the relatively small changes in RNA editing were found in evolutionarily conserved targets that are carefully regulated . Furthermore , subtle editing changes are also typical in other model organisms for brain disorders . RNA-editing studies of various human neurological diseases , such as ALS , epilepsy , schizophrenia , and bipolar disorder , have all evidence for mild alterations in RNA editing [69–71] . Finally , since we found thousands of new RNA editing sites throughout the zebrafish genome , other untested target sites may exhibit more robust , Fmrp-dependent changes in RNA editing levels . In summary , this study proposes a link between neurological deficiencies and RNA editing in the common mental disorder FXS . In order to elucidate the functional role of Fmrp in mediating Adar activity and RNA editing , further molecular and live-imaging studies should be performed . For example , establishing transgenic fish that overexpress edited and non-edited Fmrp-Adar target genes , combined with live imaging of the activity and structure of neurons and synapses throughout the brain , would help determine the pathogenesis of FXS symptoms . Taking into account that the zebrafish has become an attractive model for large-scale genetic and small-molecule screens , fmr1-/- larvae can provide the platform to elucidate the molecular mechanism and find therapeutic treatments for FXS .
Adult zebrafish were reared and maintained in fully automated zebrafish housing systems ( Aquazone , Israel; temperature 28±0 . 5°C , pH 7 . 0 , conductivity 300 μS ) under a 14-hour light/10-hour dark cycle , and fed twice a day . Embryos were generated by natural spawning and reared in water containing methylene blue ( 0 . 15% ) in a 28±0 . 5°C , light-controlled incubator . All animal protocols were reviewed and approved by the Bar-Ilan University Bioethics Committee . To prepare probes for whole-mount in situ hybridization ( ISH ) experiments , the full coding sequences of the following genes were amplified: mechanistic target of rapamycin ( mtor , NM_001077211 . 2 ) , SAM and SH3 domain containing 1a ( sash1a , NM_001044819 . 1 ) , talin 1 ( tln1 , NM_001009560 . 1 ) , adenosine deaminase acting on RNA 1 ( adar1 , NM_131596 . 1 ) , adenosine deaminase acting on RNA 2a ( adar2a , NM_131610 . 3 ) , adenosine deaminase acting on RNA 2b ( adar2b , XM_682018 . 7 ) , and adenosine deaminase acting on RNA 3 ( adar3 , XM_681334 . 5 ) . All polymerase chain reaction ( PCR ) products were cloned into a pCRII-TOPO vector ( Invitrogen , Carlsbad , CA ) , and served as a template to transcribe digoxigenin-labeled antisense mRNA probes . In order to generate the pcmv:EGFP-Adar2a and pcmv:MYC-Fmrp constructs , the CDS of adar2a and fmrp flanked with SalI/KpnI and EcoRI/BglII restriction sites , respectively , were amplified and double-digested with the appropriate enzymes . The pcmv:EGFP and pcmv:MYC vectors ( kindly provided by Prof . Uri Nir , BIU , Israel ) were double-digested with SalI/KpnI and EcoRI/BglII , respectively . The CDS of adar2a and fmrp were ligated into the digested pcmv:EGFP and pcmv:MYC vectors . The pT2-uas:SYP-EGFP construct was generated and used as described [60] . In order to generate the mnx1X3:GAL4 construct , the mnx1X3 promotor located within the p5E-mnx1 ( 3X ) ( kindly provided by Dr . Claire Wyart , ICM , France ) was double-digested with BamHI and HindIII , and ligated into a BamHI/HindIII-digested hcrt:GAL4 vector , replacing the hcrt promoter . To transiently express the following DNA constructs: pT2-huc:Gal4-VP16 , pT2-uas:tRFP , uas:memYFP , pT2-uas:SYP-EGFP [60] , and mnx1X3:GAL4 , the constructs were diluted to a concentration of 40 ng/μl and microinjected , using a micromanipulator and a PV830 Pneumatic PicoPump ( World Precision Instruments , Sarasota , FL ) , into one-cell-stage eggs . The embryos were kept in Petri dishes , and the pattern of EGFP expression was monitored throughout their development . The fmr1-/- line was kindly provided by Dr . Gordon X . Wang and Prof . Philippe Mourrian ( Stanford University , CA ) . To minimize genetic variations , heterozygous ( fmr1+/- ) zebrafish were crossed and their progeny genotyped . Either fmr1-/- and its sibling WT adults or their progeny were used in each experiment . DNA was extracted from larvae using the genomic DNA extraction kit ( Invitrogen , Carlsbad , CA , USA ) according to the protocol provided by the manufacturer . Total RNAs were extracted from the tissue with the Direct-zol RNA MiniPrep Kit ( Zymo Research Corporation , Irvine , CA , USA ) according to the procedure provided by the manufacturer . After DNase I treatment , 2–7 . 5 μg of total RNA was used to synthesize the first strand of cDNA with the iScript Advanced cDNA Synthesis Kit ( Bio-Rad Laboratories Ltd . , Berkeley , California , USA ) . cDNA was purified with the MinElute PCR Purification Kit ( QIAGEN Sciences , Germantown , Maryland , USA ) and concentrated using SpeedVac , if needed . In all ISH experiments , embryos and larvae were fixed in 4% paraformaldehyde overnight at 4°C , washed in phosphate-buffered saline ( PBS ) , and stored in 100% methanol . The areas of mRNA expression were detected by ISH , as previously described [62] . Digoxigenin antisense riboprobes for mtor , sash1 , talin1 , adar1 , adar2a , adar2b , and adar3 were transcribed in vitro using the vector templates described above , and standard reagents followed the manufacturer’s instructions ( Roche Applied Science , Nutley , NJ ) . ISHs were revealed using BM purple . HEK293T cell lines were grown in DMEM containing 10% heat-inactivated fetal bovine serum and 1% nonessential amino acids ( Biological Industries , Beit Haemek , Israel ) , and incubated at 37°C under 5% CO2 . HEK293T cells were transfected with 4 mg of pcmv:EGFP-Adar2a and pcmv:MYC-Fmrp vectors using the calcium phosphate method . The culture medium was changed 6 h after transfection , and cells were harvested 48 h later . Whole-cell proteins were extracted from mature fish brains or transfected HEK293T cell lines in lysis buffer containing 20 mM Tris , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , 0 . 5% sodium deoxycholate , 2 mM Na3VO4 , 1 mM NaF , and 10 mM b-glycerophosphate complement with protease inhibitors ( cOmplete Protease Inhibitor Cocktail Tablets , Roche ) . Lysates were incubated for 30 min on ice , and the supernatant was collected after a 20-min spin at 14 , 000 rpm at 4°C . Protein concentration was determined by Bradford analysis ( Bio-Rad Protein Assay Dye Reagent Concentrate , Bio-Rad , Hercules , CA , USA ) . A total of 30 mg protein extract was loaded per lane on 7 . 5% SDS polyacrylamide gel . After electrophoresis , proteins were transferred to a nitrocellulose membrane ( BIO-RAD , Hercules , CA , USA ) , and the membrane was blocked for 1 h in phosphate-buffered solution [0 . 1% Tween ( PBT ) with 5% skim milk] . Next , the membrane was incubated in PBT with 5% skim milk containing the appropriate primary antibody: enhanced green fluorescent protein ( EGFP ) diluted 1:1000 sc-9996 ( Santa Cruz Biotechnology , Dallas , TX , USA ) , anti-MYC diluted 1:1000 9E10 ( Developmental Studies Hybridoma Bank , The University of Iowa ) , anti-mTOR dilution 1:750 GTX124771 ( GeneTex , Inc . , Hsinchu City , Taiwan ) , anti-Adar2 dilution 1:500 sc-393272 ( Santa Cruz Biotechnology , Dallas , TX , USA , the antibody is expected to recognize zebrafish Adar2a and Adar2b alike] , anti-actin dilution 1:500 sc-1616R ( Santa Cruz Biotechnology , Dallas , TX , USA ) . After washing 3 × 5 min with PBT , the secondary antibody diluted 1:4000 [goat anti-mouse IgG-horseradish peroxidase: sc-2005 , or goat anti-rabbit IgG-horseradish peroxidase: sc-2004 ( Santa Cruz Biotechnology ) ] was incubated for 1 h in PBT with 5% skim milk . Membrane development was performed following 3 × 5 min washing with PBT using SuperSignal West Pico Chemiluminescent Substrate according to the manufacturer’s instructions ( Thermo Fisher Scientific , Waltham , MA , USA ) . These Western blot experiments were performed twice on independent biological samples . Whole-cell proteins were extracted from transfected HEK293T cell lines as previously described ( Western blotting ) . A total of 3000 μg protein extract was incubated overnight at 4°C with either EGFP diluted 1:100 sc-9996 ( Santa Cruz Biotechnology , Dallas , TX , USA ) , anti-c-Myc Tag ( 9E10 ) Affinity Gel ( BioLegend , San Diego , CA , USA ) , or b-actin 1:100 sc-1616R ( Santa Cruz Biotechnology , Dallas , TX , USA ) . Antigen-antibody complexes were precipitated with protein A/G-Sepharose sc-2003 ( Santa Cruz Biotechnology , Dallas , TX , USA ) for 1 h at 4°C and washed 3 times with cold PBSX1 . Precipitated proteins were then resolved by SDS-PAGE , blotted onto nitrocellulose membranes , and reacted with the appropriate antibodies: enhanced green fluorescent protein ( EGFP ) diluted 1:1000 sc-9996 ( Santa Cruz Biotechnology , Dallas , TX , USA ) , anti-MYC diluted 1:1000 9E10 ( Developmental Studies Hybridoma Bank , The University of Iowa ) , or anti-actin dilution 1:500 sc-1616R ( Santa Cruz Biotechnology , Dallas , TX , USA ) . Whole-cell proteins and RNAs were extracted from Fmrp-MYC transfected HEK293T cell lines as described above . A total of 3000 μg protein-RNA extract was incubated overnight at 4°C with either anti-c-Myc Tag ( 9E10 ) Affinity Gel ( BioLegend , San Diego , CA , USA ) , or b-actin 1:100 sc-1616R ( Santa Cruz Biotechnology , Dallas , TX , USA ) . Antigen-antibody complexes of b-actin were precipitated with protein A/G-Sepharose sc-2003 ( Santa Cruz Biotechnology , Dallas , TX , USA ) for 1 h at 4°C . Antibody-protein-RNA complexes of both samples were washed 3 times with cold PBSX1 . Coprecipitated RNAs were isolated by resuspending beads in 1 ml TRIzol reagent ( Zymo Research , Irvine , CA , USA ) followed by ethanol precipitation . cDNA was prepared as described above . PCR amplifications were performed using the following specific primers: adar1: 5'- CGGGCAATGCCTCGC -3' and 5'- AATGGATGGGTGTAGTATCCGC -3' . DNA sequencing and confirmation of targeted re-sequencing data obtained via the mmPCR was performed in Hy-Labs ( Rehovot , Israel ) using standard sequencing methods . RNA editing sites were examined using Sequencher 4 . 10 . 1 Demo version and BioEdit version 5 . 0 . 6 ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html; File > Batch Export of Raw Sequence Trace Data ) . Editing sites were quantified by finding the maximal amplitude height of the A peaks ( unedited ) and the G peaks ( edited ) , and also by calculating percentages of the population edited at each site [100% X ( number of C nucleotides/total number of nucleotides ) ] . The University of California Santa Cruz ( UCSC ) genome browser was used to locate editing sites , mismatches between RNA and ESTs , as well as to establish conservation and homology between different species via alignments . The dataset of HE sites was created by analyzing deposited RNA-seq data [52] ( SRA accession numbers SRR1028002 , SRR1028003 , and SRR1028004 ) . Fastq files were aligned to the zebrafish genome ( Zv9/DanRer7 ) using tophat , command: tophat -r 530 index fastq1 , fastq1_replication fastq2 , fastq2_replication fastq3 , fastq3_replication . We then realigned the fastq files to the zebrafish reference genome , and added the splice junction file , achieved from the first run , as input . Command: tophat -r 530 -j splice_junctions_file; indexfastq1 , fastq1_replication fastq2 , fastq2_replication fastq3 , fastq3_replication . Mpileup was then used to find differences between RNA sequences and the referenced-genome . Thresholds were set to collect sites that had more than five edited reads and that also had editing levels higher than 0 . 01 ( as described , [53] ) . Several genes within our target set harbor a number of editing sites located in close proximity in the same amplified target region . In these cases , the calculated percentage of the A/G ratio did not reveal the actual impact of editing on the sequence composition of the mRNA transcripts leading off to the resultant protein . Since PCR amplification of target regions that contain more than one targeted editing site on the same PCR product ( amplicon ) was used , it enables identifying the presence of RNA editing sites and the correlations between neighboring editing sites . The Fluidigm access array ( Fl-AA ) system enables the simultaneous amplification of at least 48 different target regions across a 48-cDNA-sample panel on a single microfluidic device . Thus , 2 , 304 separate PCR reactions are performed simultaneously , followed by in-parallel next-generation sequencing , allowing the precise quantification of A/G ratios . The basic modus operandi of the ( Fl-AA ) system is in a singleplex PCR mode that limits the number of primer pairs used for the amplification of target regions to 48 per run . We employed a selection process to determine the 48 target regions . We designed the primer set to amplify evolutionarily conserved [72] editing targets that reside within the coding region of genes , preferably on editing sites that substitute for amino acids . The first step of the selection process was to screen the RADAR database of A-to-I RNA editing sites in humans and to select editing sites that are located within genes but that also reside outside of Alu repetitive regions [56 , 73] . Next , the UCSC and the lift-over tool were used to convert the list of all editing sites from its human genomic locations to zebrafish genomic coordinates . We compiled a list of 48 target regions containing a total of 70 novel zebrafish putative RNA editing sites located in 33 genes , of which 12 are directly linked with neuronal function . Of the total 70 target sites; 41 are located within the coding sequence of genes that mostly encode neuronal transcripts ( 59% ) , of which 27 target sites exert a non-synonymous effect ( 39% ) . Another 28 sites ( 40% ) are located in the 3’-UTR of various genes , and one site was found to be located in an intron of the adar2 gene [20] . Interestingly , this site is auto-edited by Adar2 and has been linked with the fine tuning of the mRNA re-coding process and affects behavior [43] ( S3 Table ) . Finally , we compared the selected target-site list with the list of HE sites found in genes for shared entries , due to the fact that HE clusters occur with high probability in regions that generate dsRNA structures supportive of RNA editing . Indeed , we found that 7 of the 12 Refseq genes included in the final list of target regions were also found to contain HE clusters . This represents a 20-fold enrichment compared to the fraction of genes containing HE clusters in the entire zebrafish genome ( 2 . 9% ) . To distinguish between the fmr1-/- and WT samples , amplicons designed to contain the editing target sites were amplified across the sample panel containing both genotypes , using tagged fusion primers in a two-step consecutive PCR strategy . These primers were designed using Primer3 . 0 [http://frodo . wi . mit . edu/] and the 454 fusion primer design tool [http://eu . idtdna . com/scitools/applications/fusionprimers/default . aspx ( IDT , Coralville , IA ) ] . Using the mmPCR method , we automatically assembled 2 , 304 unique PCR reactions , each reaction including a portion from each of the 48 samples screened with each one of the 48 primer pairs . The mmPCR amplification and tagging strategy is based on two consecutive PCR reactions , each performed with specific fusion PCR primers . The first PCR is performed "on chip" and generates amplicons of interest containing the target sites , which are flanked by designed common sequences [CS1 ( fused to the forward primer ) /CS2 ( fused to the reverse primer ) ] . The second "off chip” PCR is performed on a thermal cycler and uses the first "on chip" PCR products as templates . The amplicons , now containing the CS regions conjoined ( by the previous PCR ) , enable forming the attachment with sample-specific barcodes and Ion-Torrent PGM adaptors , thus making all 48 mini-libraries compatible for in-parallel NGS ( Fig 6A ) . Four μl of singleplex primer ( 4 μM per primer in 1X AA-loading buffer ) was loaded into the primer inlets of the 48 . 48 Access Array IFC ( Fluidigm , San Francisco , CA , USA ) . To prepare the cDNA templates , 2 . 25 μl of each cDNA sample was added to 2 . 75 μl of the presample mix containing the following enzyme and reagents from the Roche FastStart High Fidelity PCR System: 0 . 5 μl of 10X FastStart High Fidelity Reaction Buffer wo/Mg , 0 . 5 μl DMSO ( 5% ) , 0 . 1 μl 10 mM PCR Grade Nucleotide Mix ( 200 μM ) , 0 . 9 μl 25 mM MgCl2 ( 4 . 5 mM ) , 0 . 25 μl 20X Access Array Loading Reagent ( Fluidigm , San Francisco , CA , USA ) , 0 . 05 μl of FastStart High Fidelity Enzyme Blend , and 0 . 7 μl of PCR grade water into the sample inlets of the 48 . 48 Access Array IFC ( Fluidigm , San Francisco , CA , USA ) . After loading both samples and primers via IFC Controller AX ( Fluidigm ) script , the IFC was subjected to thermal cycling using FC1 Cycler ( Fluidigm ) with the following program for 40 cycles: 50°C for 2:00 min; 70°C for 20:00 min; and 95°C 10 min . For 10 cycles: 95°C for 15 sec; 59 . 5°C for 30 sec; and 72°C for 1 min . For 4 cycles: 95°C for 15 sec; 80°C for 30 sec; 59 . 5°C for 30 sec; and 72°C for 1 min . For 10 cycles: 95°C for 15 sec; 59 . 5°C for 30 sec; and 72°C for 1 min . For 4 cycles: 95°C for 15 sec; 80°C for 30 sec; 60°C for 30 sec; and 72°C for 1 min . For 8 cycles: 95°C for 15 sec; 59 . 5°C for 30 sec; and 72°C for 1 min . For 4 cycles: 95°C for 15 sec; 80°C for 30 sec; 60°C for 30 sec; and 72°C for 1 min; Finalizing with 72°C for 3 min . Sample preparation included 1 . 0 μl of the 1:110-fold diluted PCR products as well as 15 μl of the presample mix containing the following enzyme and reagents from the Roche FastStart High Fidelity PCR System: 2 μl of 10X FastStart High Fidelity Reaction Buffer wo/Mg , 1 μl DMSO ( 5% ) , 0 . 4 μl 10 mM PCR Grade Nucleotide Mix ( 200 μM ) , 3 . 6 μl 25 mM MgCl2 ( 4 . 5 mM ) , 0 . 2 μl of FastStart High Fidelity Enzyme Blend , and 7 . 8 μl of PCR grade water . Four μl of primer mix from the 2 μM Access Array Barcode Library for Ion Torrent PGM Sequencer– 96 ( P/N100-4911 ) , utilizing the B-set; A–BC–CS2 , and P1–CS1 barcode primer combination , was added to the sample mix . We used the following PCR program: 95°C for 10 min , 11 cycles of 95°C for 30 s , 60°C for 30 s , 72°C for 1 min , and 72°C for 5 min . All 48-tagged mini-libraries were pooled into a single unified library and purified using the QIAquick PCR purification kit ( QIAGEN Sciences , Maryland , USA ) . The output library was analyzed and quantified in the 2100 Agilent BioAnalyzer system using the HS DNA kit ( Agilent Technologies , Santa Clara , CA , USA ) . After establishing the library dilution factor , the library underwent sequencing preparation using the Ion PGM Template OT2 200 kit , followed by the Ion PGM Sequencing 200-v2 kit , both according to the manufacturers' protocols . The fully processed library was loaded on the Ion 318 chip and sequenced using the Ion-Torrent PGM instructions ( Life Technologies , Grand Island , NY 14072 , USA ) . We used FASTX Toolkit to demultiplex the raw reads . We used BWA26 to align the reads to a combination of the reference genome and exonic sequences surrounding known splicing junctions from gene models annotated in RefSeq and Gencode V12 . We chose the length of the splicing junction regions to be slightly shorter than the reads in order to prevent redundant hits . For allelic-ratio count , we used the bases with a minimum quality score of 20 . For read-depth count , we used the coverage of the representative sites in each amplicon . To obtain novel RNA-editing sites , we required variants to be supported by at least 10 mismatch reads with a base quality score and a mapping quality score ≥20 . We also removed all known SNPs present in dbSNP ( except SNPs of molecular type “cDNA”; database version 135; http://www . ncbi . nlm . nih . gov/SNP/ ) , the 1000 Genomes Project or the University of Washington Exome Sequencing Project ( http://evs . gs . washington . edu/EVS/ ) . We used the Ion-Torrent PGM sequence output to detect and locate any A-to-G mismatches between the genomic DNA and the RNA sequences . Such mismatches were summed up and scored for their signal strength according to the fraction of ‘G’ reads of all ‘A+G’ reads [A/ ( A+G ) *100] . The relative mRNA quantification of mtor , sash1 , tln1 , adar1 , adar2a , adar2b , and adar3 was determined using qRT-PCR . Total RNA was extracted from 6 dpf embryos using the Direct-zol RNA MiniPrep kit ( Zymo Research Corporation , Irvine , CA ) , according to the manufacturer’s instructions . For each tested gene , a total of five biological samples were used . Each biological sample contained a pool of 10 embryos . mRNA ( 1 μg ) was reverse-transcribed using qScript cDNA SuperMix ( Quanta BioSciences , Gaithersburg , MD ) , according to the manufacturer’s instructions . Relative transcript levels were determined by the 7900HT Fast Real-Time PCR System ( Applied Biosystems , Foster City , CA ) . Triplicates of each cDNA sample were PCR-amplified using the PerfeCTa SYBR Green FastMix ( Quanta BioSciences , Gaithersburg , MD ) and the following specific primers: adar1: 5'- ACCGCTGTGTTAAAGGAGAG -3' and 5'-AAAATAGTCTCATCGCCAGGG -3'; adar2a: 5'- CGGCAAGTACAAATCCAGGT -3' and 5'- CAGGTTGCGGTTTTCCTTTA -3'; adar2b: 5'- CTGGGAAGTCTGTATCATGCTG -3' and 5'- GTTGCCTTGCTTCTGTGTTAC -3'; adar3: 5'—GCCAGCTCGCTGTACTTCTC -3' and 5'- CAGGCACTCTTCAACTTCAGG -3'; mtor: 5'- CCCAGACTTATTCGCCCATAC -3' and 5'- CCATTTCCTCATCTCCAGTCC -3'; sash1: 5'- CATCTTCGGACAGTTTCTCCC -3' and 5'- GTACTCTTGTGCCAGGTCATC -3'; tln1: 5'- GTCAACACCATCACCAAACTG -3' and 5'- TTTAGCCACGTCCTTCACAG -3' . The relative quantification of gene expression levels was normalized against β-actin: 5'- TGAATCCCAAAGCCAACAGAG -3' and 5'- CCAGAGTCCATCACAATACCAG -3' gene , and subjected to the ΔΔCT method [74] . Gene levels were normalized by dividing the absolute levels of each sample with the average of all WT samples . Two-way t-test , assuming unequal variances , was used to compare the expression level of both genotypes . In all experiments , data were presented as means ± standard error of the mean ( SEM ) . At 6 dpf , fmr1-/- larvae and WT were individually placed in 48-well plates under 14 h light/10 h dark cycles . Larva-containing plates were placed in the Noldus DanioVision tracking system ( Noldus Information Technology , Wageningen , Netherlands ) and acclimated for one hour prior to recording . Light intensity in the tracking system was 70LUX ( 25% in the operating software ) for all experiments . To monitor rhythmic activity during a daily cycle , larvae were maintained under the same light-dark regime prior to the experiment . To monitor responses to light/dark transitions , larvae were subjected to 3 intervals of 30 min light/30 min darkness . Live video-tracking and analysis were conducted using the EthoVision XT 9 software ( Noldus Information Technology , Wageningen , Netherlands ) [62] . Four independent assays were performed , with a total of 177 and 179 larvae for each genotype in the light/dark-transition experiments , respectively , and a total of 30 and 34 larvae for each genotype in the daily-cycle experiment , respectively . Phylogenetic analysis was performed with PhyML 3 . 0 aLRT ( http://www . phylogeny . fr/version2_cgi/one_task . cgi ? task_type=phyml ) using the "one click" program . An epifluorescence stereomicroscope ( Leica M165FC ) was used to image fixed larvae . Pictures were taken using Leica Application Suite imaging software v . 3 . 7 . For confocal imaging , embryos and larvae were placed in low-melting-point agarose ( 0 . 5–1 . 0% ) on a specially designed dish filled with embryo water . A similar mounting protocol was used to image fixed embryos subjected to whole-mount ISH . Confocal imaging was performed using a Zeiss LSM710 upright confocal microscope ( Zeiss , Oberkochen , Germany ) . To visualize single cells , optic sections of 0 . 5–1 micron were acquired . Positive cells were manually quantified using z-stack images of serial tissue slices . Images were processed using ImageJ ( National Institutes of Health , Bethesda , MD ) and Adobe Photoshop ( San Jose , CA ) software . Calculation of total arbor length and axonal branching in single motor neurons , RB sensory neurons , and Hcrt neurons was performed using NeuronJ plugin in ImageJ software ( National Institutes of Health , Bethesda , MD ) . Synaptic density was calculated by quantifying the number of synapses per 10 μm in the axonal arbor of single motor neurons and Hcrt neurons , as well as 30 μm in RB neurons , using ImageJ software ( National Institutes of Health , Bethesda , MD ) . | The most frequent inherited mental retardation disorder is fragile X syndrome , which is characterized by learning disabilities , cognitive impairment , anxiety , and hyperactive behavior . The genetic cause of this disorder is the silencing of the fmr1 gene , which encodes the RNA-binding protein Fmrp . This protein inhibits the production of various proteins in the brain and interacts with the Adar enzyme , which converts the nucleotide A into I in RNAs . However , it is unclear by which mechanism the loss of Fmrp affects the sequence of neuronal genes and , ultimately , brain function . Here , we used the fmr1 mutant zebrafish ( fmr1-/- ) , which enables high-throughput genetics and live imaging experiments in a transparent and evolutionarily conserved brain . We found that loss of Fmrp altered neuronal circuit formation . Furthermore , similar to human patients , the fmr1-/- larvae were hyperactive . Biochemical assays showed that Fmrp interacts with the Adar2a protein , which is increased in fmr1-/- larvae . Thus , we characterized global RNA editing in the zebrafish transcriptome and used a microfluidic-based high-throughput technique to accurately quantify RNA editing levels . Loss of Fmrp resulted in a mild increase in RNA editing in the coding sequences of conserved synaptic genes . These findings propose that altered RNA editing levels may affect neuronal and behavioral deficiencies in FXS . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Fmrp Interacts with Adar and Regulates RNA Editing, Synaptic Density and Locomotor Activity in Zebrafish |
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